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Schlattmann P, Wieske V, Bressem KK, Götz T, Schuetz GM, Andreini D, Pontone G, Alkadhi H, Hausleiter J, Zimmermann E, Gerber B, Shabestari AA, Meijs MFL, Sato A, Øvrehus KA, Jenkins SMM, Knuuti J, Hamdan A, Halvorsen BA, Mendoza-Rodriguez V, Rixe J, Wan YL, Langer C, Leschka S, Martuscelli E, Ghostine S, Tardif JC, Sánchez AR, Haase R, Dewey M. The effectiveness of coronary computed tomography angiography and functional testing for the diagnosis of obstructive coronary artery disease: results from the individual patient data Collaborative Meta-Analysis of Cardiac CT (COME-CCT). Insights Imaging 2024; 15:208. [PMID: 39143443 PMCID: PMC11324632 DOI: 10.1186/s13244-024-01702-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 11/01/2023] [Indexed: 08/16/2024] Open
Abstract
AIM To determine the effectiveness of functional stress testing and computed tomography angiography (CTA) for diagnosis of obstructive coronary artery disease (CAD). METHODS AND RESULTS Two-thousand nine-hundred twenty symptomatic stable chest pain patients were included in the international Collaborative Meta-Analysis of Cardiac CT consortium to compare CTA with exercise electrocardiography (exercise-ECG) and single-photon emission computed tomography (SPECT) for diagnosis of CAD defined as ≥ 50% diameter stenosis by invasive coronary angiography (ICA) as reference standard. Generalised linear mixed models were used for calculating the diagnostic accuracy of each diagnostic test including non-diagnostic results as dependent variables in a logistic regression model with random intercepts and slopes. Covariates were the reference standard ICA, the type of diagnostic method, and their interactions. CTA showed significantly better diagnostic performance (p < 0.0001) with a sensitivity of 94.6% (95% CI 92.7-96) and a specificity of 76.3% (72.2-80) compared to exercise-ECG with 54.9% (47.9-61.7) and 60.9% (53.4-66.3), SPECT with 72.9% (65-79.6) and 44.9% (36.8-53.4), respectively. The positive predictive value of CTA was ≥ 50% in patients with a clinical pretest probability of 10% or more while this was the case for ECG and SPECT at pretest probabilities of ≥ 40 and 28%. CTA reliably excluded obstructive CAD with a post-test probability of below 15% in patients with a pretest probability of up to 74%. CONCLUSION In patients with stable chest pain, CTA is more effective than functional testing for the diagnosis as well as for reliable exclusion of obstructive CAD. CTA should become widely adopted in patients with intermediate pretest probability. SYSTEMATIC REVIEW REGISTRATION PROSPERO Database for Systematic Reviews-CRD42012002780. CRITICAL RELEVANCE STATEMENT In symptomatic stable chest pain patients, coronary CTA is more effective than functional testing for diagnosis and reliable exclusion of obstructive CAD in intermediate pretest probability of CAD. KEY POINTS Coronary computed tomography angiography showed significantly better diagnostic performance (p < 0.0001) for diagnosis of coronary artery disease compared to exercise-ECG and SPECT. The positive predictive value of coronary computed tomography angiography was ≥ 50% in patients with a clinical pretest probability of at least 10%, for ECG ≥ 40%, and for SPECT 28%. Coronary computed tomography angiography reliably excluded obstructive coronary artery disease with a post-test probability of below 15% in patients with a pretest probability of up to 74%.
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Affiliation(s)
- Peter Schlattmann
- Institute of Medical Statistics, Computer Sciences, and Data Science, University Hospital of Friedrich Schiller University Jena, Jena, Germany
| | - Viktoria Wieske
- Department of Radiology, Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany
| | - Keno K Bressem
- Department of Radiology, Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany
| | - Theresa Götz
- Institute of Medical Statistics, Computer Sciences, and Data Science, University Hospital of Friedrich Schiller University Jena, Jena, Germany
| | - Georg M Schuetz
- Department of Radiology, Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany
| | | | | | - Hatem Alkadhi
- Institute of Diagnostic and Interventional Radiology University Hospital Zurich, Zurich, Switzerland
| | | | - Elke Zimmermann
- Department of Radiology, Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany
| | - Bernhard Gerber
- Department of Cardiology, Clinique Universitaire St Luc, Institut de Recherche Clinique et Expérimentale, Brussels, Belgium
| | - Abbas A Shabestari
- Modarres Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Matthijs F L Meijs
- Department of Cardiology, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Akira Sato
- Cardiovascular Division, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan
| | | | | | - Juhani Knuuti
- Turku University Hospital and University of Turku, Turku, Finland
| | - Ashraf Hamdan
- Department of Cardiovascular Imaging, Department of Cardiology, Rabin Medical Center, Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | | | | | - Johannes Rixe
- Department of Cardiology and Electrophysiology, Jung Stilling Hospital Siegen, Siegen, Germany
| | - Yung-Liang Wan
- Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Chang Gung Memorial Hospital at Linkou, Taoyaun City, Taiwan
| | - Christoph Langer
- Kardiologisch-Angiologische Praxis, Herzzentrum Bremen, Bremen, Germany
| | - Sebastian Leschka
- Department of Radiology, Kantonsspital St Gallen, St Gallen, Switzerland
| | - Eugenio Martuscelli
- Department of Internal Medicine, University of Rome Tor Vergata, Rome, Italy
| | - Said Ghostine
- Department of Cardiology, Centre Chirurgical Marie Lannelongue, Le Plessis Robinson, France
| | | | | | - Robert Haase
- Department of Radiology, Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany
| | - Marc Dewey
- Department of Radiology, Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany.
- Berlin Institute of Health, Berlin, Germany.
- DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, Berlin, Germany.
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Allotey J, Archer L, Coomar D, Snell KI, Smuk M, Oakey L, Haqnawaz S, Betrán AP, Chappell LC, Ganzevoort W, Gordijn S, Khalil A, Mol BW, Morris RK, Myers J, Papageorghiou AT, Thilaganathan B, Da Silva Costa F, Facchinetti F, Coomarasamy A, Ohkuchi A, Eskild A, Arenas Ramírez J, Galindo A, Herraiz I, Prefumo F, Saito S, Sletner L, Cecatti JG, Gabbay-Benziv R, Goffinet F, Baschat AA, Souza RT, Mone F, Farrar D, Heinonen S, Salvesen KÅ, Smits LJ, Bhattacharya S, Nagata C, Takeda S, van Gelder MM, Anggraini D, Yeo S, West J, Zamora J, Mistry H, Riley RD, Thangaratinam S. Development and validation of prediction models for fetal growth restriction and birthweight: an individual participant data meta-analysis. Health Technol Assess 2024; 28:1-119. [PMID: 39252507 PMCID: PMC11404361 DOI: 10.3310/dabw4814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/11/2024] Open
Abstract
Background Fetal growth restriction is associated with perinatal morbidity and mortality. Early identification of women having at-risk fetuses can reduce perinatal adverse outcomes. Objectives To assess the predictive performance of existing models predicting fetal growth restriction and birthweight, and if needed, to develop and validate new multivariable models using individual participant data. Design Individual participant data meta-analyses of cohorts in International Prediction of Pregnancy Complications network, decision curve analysis and health economics analysis. Participants Pregnant women at booking. External validation of existing models (9 cohorts, 441,415 pregnancies); International Prediction of Pregnancy Complications model development and validation (4 cohorts, 237,228 pregnancies). Predictors Maternal clinical characteristics, biochemical and ultrasound markers. Primary outcomes fetal growth restriction defined as birthweight <10th centile adjusted for gestational age and with stillbirth, neonatal death or delivery before 32 weeks' gestation birthweight. Analysis First, we externally validated existing models using individual participant data meta-analysis. If needed, we developed and validated new International Prediction of Pregnancy Complications models using random-intercept regression models with backward elimination for variable selection and undertook internal-external cross-validation. We estimated the study-specific performance (c-statistic, calibration slope, calibration-in-the-large) for each model and pooled using random-effects meta-analysis. Heterogeneity was quantified using τ2 and 95% prediction intervals. We assessed the clinical utility of the fetal growth restriction model using decision curve analysis, and health economics analysis based on National Institute for Health and Care Excellence 2008 model. Results Of the 119 published models, one birthweight model (Poon) could be validated. None reported fetal growth restriction using our definition. Across all cohorts, the Poon model had good summary calibration slope of 0.93 (95% confidence interval 0.90 to 0.96) with slight overfitting, and underpredicted birthweight by 90.4 g on average (95% confidence interval 37.9 g to 142.9 g). The newly developed International Prediction of Pregnancy Complications-fetal growth restriction model included maternal age, height, parity, smoking status, ethnicity, and any history of hypertension, pre-eclampsia, previous stillbirth or small for gestational age baby and gestational age at delivery. This allowed predictions conditional on a range of assumed gestational ages at delivery. The pooled apparent c-statistic and calibration were 0.96 (95% confidence interval 0.51 to 1.0), and 0.95 (95% confidence interval 0.67 to 1.23), respectively. The model showed positive net benefit for predicted probability thresholds between 1% and 90%. In addition to the predictors in the International Prediction of Pregnancy Complications-fetal growth restriction model, the International Prediction of Pregnancy Complications-birthweight model included maternal weight, history of diabetes and mode of conception. Average calibration slope across cohorts in the internal-external cross-validation was 1.00 (95% confidence interval 0.78 to 1.23) with no evidence of overfitting. Birthweight was underestimated by 9.7 g on average (95% confidence interval -154.3 g to 173.8 g). Limitations We could not externally validate most of the published models due to variations in the definitions of outcomes. Internal-external cross-validation of our International Prediction of Pregnancy Complications-fetal growth restriction model was limited by the paucity of events in the included cohorts. The economic evaluation using the published National Institute for Health and Care Excellence 2008 model may not reflect current practice, and full economic evaluation was not possible due to paucity of data. Future work International Prediction of Pregnancy Complications models' performance needs to be assessed in routine practice, and their impact on decision-making and clinical outcomes needs evaluation. Conclusion The International Prediction of Pregnancy Complications-fetal growth restriction and International Prediction of Pregnancy Complications-birthweight models accurately predict fetal growth restriction and birthweight for various assumed gestational ages at delivery. These can be used to stratify the risk status at booking, plan monitoring and management. Study registration This study is registered as PROSPERO CRD42019135045. Funding This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: 17/148/07) and is published in full in Health Technology Assessment; Vol. 28, No. 14. See the NIHR Funding and Awards website for further award information.
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Affiliation(s)
- John Allotey
- WHO Collaborating Centre for Global Women's Health, Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
| | - Lucinda Archer
- Centre for Prognosis Research, School of Medicine, Keele University, Keele, UK
| | - Dyuti Coomar
- WHO Collaborating Centre for Global Women's Health, Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
| | - Kym Ie Snell
- Centre for Prognosis Research, School of Medicine, Keele University, Keele, UK
| | - Melanie Smuk
- Blizard Institute, Centre for Genomics and Child Health, Queen Mary University of London, London, UK
| | - Lucy Oakey
- WHO Collaborating Centre for Global Women's Health, Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
| | - Sadia Haqnawaz
- The Hildas, Dame Hilda Lloyd Network, WHO Collaborating Centre for Global Women's Health, University of Birmingham, Birmingham, UK
| | - Ana Pilar Betrán
- Department of Reproductive and Health Research, World Health Organization, Geneva, Switzerland
| | - Lucy C Chappell
- Department of Women and Children's Health, School of Life Course Sciences, King's College London, London, UK
| | - Wessel Ganzevoort
- Department of Obstetrics, Amsterdam UMC University of Amsterdam, Amsterdam, the Netherlands
| | - Sanne Gordijn
- Faculty of Medical Sciences, University Medical Center Groningen, Groningen, the Netherlands
| | - Asma Khalil
- Fetal Medicine Unit, St George's University Hospitals NHS Foundation Trust and Molecular and Clinical Sciences Research Institute, St George's University of London, London, UK
| | - Ben W Mol
- Department of Obstetrics and Gynaecology, Monash University, Monash Medical Centre, Clayton, Victoria, Australia
- Aberdeen Centre for Women's Health Research, Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, UK
| | - Rachel K Morris
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - Jenny Myers
- Maternal and Fetal Health Research Centre, Manchester Academic Health Science Centre, University of Manchester, Central Manchester NHS Trust, Manchester, UK
| | - Aris T Papageorghiou
- Fetal Medicine Unit, St George's University Hospitals NHS Foundation Trust and Molecular and Clinical Sciences Research Institute, St George's University of London, London, UK
| | - Basky Thilaganathan
- Fetal Medicine Unit, St George's University Hospitals NHS Foundation Trust and Molecular and Clinical Sciences Research Institute, St George's University of London, London, UK
- Tommy's National Centre for Maternity Improvement, Royal College of Obstetrics and Gynaecology, London, UK
| | - Fabricio Da Silva Costa
- Maternal Fetal Medicine Unit, Gold Coast University Hospital and School of Medicine, Griffith University, Gold Coast, Queensland, Australia
| | - Fabio Facchinetti
- Mother-Infant Department, University of Modena and Reggio Emilia, Emilia-Romagna, Italy
| | - Arri Coomarasamy
- WHO Collaborating Centre for Global Women's Health, Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
| | - Akihide Ohkuchi
- Department of Obstetrics and Gynecology, Jichi Medical University School of Medicine, Shimotsuke-shi, Tochigi, Japan
| | - Anne Eskild
- Akershus University Hospital, University of Oslo, Oslo, Norway
| | | | - Alberto Galindo
- Fetal Medicine Unit, Maternal and Child Health and Development Network (SAMID), Department of Obstetrics and Gynaecology, Hospital Universitario, Instituto de Investigación Hospital, Universidad Complutense de Madrid, Madrid, Spain
| | - Ignacio Herraiz
- Department of Obstetrics and Gynaecology, Hospital Universitario, Madrid, Spain
| | - Federico Prefumo
- Department of Clinical and Experimental Sciences, University of Brescia, Italy
| | - Shigeru Saito
- Department Obstetrics and Gynecology, University of Toyama, Toyama, Japan
| | - Line Sletner
- Deptartment of Pediatric and Adolescents Medicine, Akershus University Hospital, Sykehusveien, Norway
| | - Jose Guilherme Cecatti
- Obstetric Unit, Department of Obstetrics and Gynecology, University of Campinas, Campinas, Sao Paulo, Brazil
| | - Rinat Gabbay-Benziv
- Maternal Fetal Medicine Unit, Department of Obstetrics and Gynecology, Hillel Yaffe Medical Center Hadera, Affiliated to the Ruth and Bruce Rappaport School of Medicine, Technion, Haifa, Israel
| | - Francois Goffinet
- Maternité Port-Royal, AP-HP, APHP, Centre-Université de Paris, FHU PREMA, Paris, France
- Université de Paris, INSERM U1153, Equipe de recherche en Epidémiologie Obstétricale, Périnatale et Pédiatrique (EPOPé), Centre de Recherche Epidémiologie et Biostatistique Sorbonne Paris Cité (CRESS), Paris, France
| | - Ahmet A Baschat
- Department of Gynecology and Obstetrics, Johns Hopkins University School of Medicine, MD, USA
| | - Renato T Souza
- Obstetric Unit, Department of Obstetrics and Gynecology, University of Campinas, Campinas, Sao Paulo, Brazil
| | - Fionnuala Mone
- Centre for Public Health, Queen's University, Belfast, UK
| | - Diane Farrar
- Bradford Institute for Health Research, Bradford, UK
| | - Seppo Heinonen
- Department of Obstetrics and Gynecology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Kjell Å Salvesen
- Department of Laboratory Medicine, Children's and Women's Health, Norwegian University of Science and Technology, Trondheim, Norway
| | - Luc Jm Smits
- Care and Public Health Research Institute, Maastricht University Medical Centre, Maastricht, the Netherlands
| | - Sohinee Bhattacharya
- Aberdeen Centre for Women's Health Research, Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, UK
| | - Chie Nagata
- Center for Postgraduate Education and Training, National Center for Child Health and Development, Tokyo, Japan
| | - Satoru Takeda
- Department of Obstetrics and Gynecology, Juntendo University, Tokyo, Japan
| | - Marleen Mhj van Gelder
- Department for Health Evidence, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Dewi Anggraini
- Faculty of Mathematics and Natural Sciences, Lambung Mangkurat University, South Kalimantan, Indonesia
| | - SeonAe Yeo
- University of North Carolina at Chapel Hill, School of Nursing, NC, USA
| | - Jane West
- Bradford Institute for Health Research, Bradford, UK
| | - Javier Zamora
- WHO Collaborating Centre for Global Women's Health, Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
- Clinical Biostatistics Unit, Hospital Universitario Ramón y Cajal (IRYCIS), Madrid, Spain
| | - Hema Mistry
- Warwick Medical School, University of Warwick, Warwick, UK
| | - Richard D Riley
- Centre for Prognosis Research, School of Medicine, Keele University, Keele, UK
| | - Shakila Thangaratinam
- WHO Collaborating Centre for Global Women's Health, Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
- Birmingham Women's and Children's NHS Foundation Trust, Birmingham, UK
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Lee CC, Chen CW, Yen HK, Lin YP, Lai CY, Wang JL, Groot OQ, Janssen SJ, Schwab JH, Hsu FM, Lin WH. Comparison of Two Modern Survival Prediction Tools, SORG-MLA and METSSS, in Patients With Symptomatic Long-bone Metastases Who Underwent Local Treatment With Surgery Followed by Radiotherapy and With Radiotherapy Alone. Clin Orthop Relat Res 2024:00003086-990000000-01687. [PMID: 39051924 DOI: 10.1097/corr.0000000000003185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Accepted: 06/20/2024] [Indexed: 07/27/2024]
Abstract
BACKGROUND Survival estimation for patients with symptomatic skeletal metastases ideally should be made before a type of local treatment has already been determined. Currently available survival prediction tools, however, were generated using data from patients treated either operatively or with local radiation alone, raising concerns about whether they would generalize well to all patients presenting for assessment. The Skeletal Oncology Research Group machine-learning algorithm (SORG-MLA), trained with institution-based data of surgically treated patients, and the Metastases location, Elderly, Tumor primary, Sex, Sickness/comorbidity, and Site of radiotherapy model (METSSS), trained with registry-based data of patients treated with radiotherapy alone, are two of the most recently developed survival prediction models, but they have not been tested on patients whose local treatment strategy is not yet decided. QUESTIONS/PURPOSES (1) Which of these two survival prediction models performed better in a mixed cohort made up both of patients who received local treatment with surgery followed by radiotherapy and who had radiation alone for symptomatic bone metastases? (2) Which model performed better among patients whose local treatment consisted of only palliative radiotherapy? (3) Are laboratory values used by SORG-MLA, which are not included in METSSS, independently associated with survival after controlling for predictions made by METSSS? METHODS Between 2010 and 2018, we provided local treatment for 2113 adult patients with skeletal metastases in the extremities at an urban tertiary referral academic medical center using one of two strategies: (1) surgery followed by postoperative radiotherapy or (2) palliative radiotherapy alone. Every patient's survivorship status was ascertained either by their medical records or the national death registry from the Taiwanese National Health Insurance Administration. After applying a priori designated exclusion criteria, 91% (1920) were analyzed here. Among them, 48% (920) of the patients were female, and the median (IQR) age was 62 years (53 to 70 years). Lung was the most common primary tumor site (41% [782]), and 59% (1128) of patients had other skeletal metastases in addition to the treated lesion(s). In general, the indications for surgery were the presence of a complete pathologic fracture or an impending pathologic fracture, defined as having a Mirels score of ≥ 9, in patients with an American Society of Anesthesiologists (ASA) classification of less than or equal to IV and who were considered fit for surgery. The indications for radiotherapy were relief of pain, local tumor control, prevention of skeletal-related events, and any combination of the above. In all, 84% (1610) of the patients received palliative radiotherapy alone as local treatment for the target lesion(s), and 16% (310) underwent surgery followed by postoperative radiotherapy. Neither METSSS nor SORG-MLA was used at the point of care to aid clinical decision-making during the treatment period. Survival was retrospectively estimated by these two models to test their potential for providing survival probabilities. We first compared SORG to METSSS in the entire population. Then, we repeated the comparison in patients who received local treatment with palliative radiation alone. We assessed model performance by area under the receiver operating characteristic curve (AUROC), calibration analysis, Brier score, and decision curve analysis (DCA). The AUROC measures discrimination, which is the ability to distinguish patients with the event of interest (such as death at a particular time point) from those without. AUROC typically ranges from 0.5 to 1.0, with 0.5 indicating random guessing and 1.0 a perfect prediction, and in general, an AUROC of ≥ 0.7 indicates adequate discrimination for clinical use. Calibration refers to the agreement between the predicted outcomes (in this case, survival probabilities) and the actual outcomes, with a perfect calibration curve having an intercept of 0 and a slope of 1. A positive intercept indicates that the actual survival is generally underestimated by the prediction model, and a negative intercept suggests the opposite (overestimation). When comparing models, an intercept closer to 0 typically indicates better calibration. Calibration can also be summarized as log(O:E), the logarithm scale of the ratio of observed (O) to expected (E) survivors. A log(O:E) > 0 signals an underestimation (the observed survival is greater than the predicted survival); and a log(O:E) < 0 indicates the opposite (the observed survival is lower than the predicted survival). A model with a log(O:E) closer to 0 is generally considered better calibrated. The Brier score is the mean squared difference between the model predictions and the observed outcomes, and it ranges from 0 (best prediction) to 1 (worst prediction). The Brier score captures both discrimination and calibration, and it is considered a measure of overall model performance. In Brier score analysis, the "null model" assigns a predicted probability equal to the prevalence of the outcome and represents a model that adds no new information. A prediction model should achieve a Brier score at least lower than the null-model Brier score to be considered as useful. The DCA was developed as a method to determine whether using a model to inform treatment decisions would do more good than harm. It plots the net benefit of making decisions based on the model's predictions across all possible risk thresholds (or cost-to-benefit ratios) in relation to the two default strategies of treating all or no patients. The care provider can decide on an acceptable risk threshold for the proposed treatment in an individual and assess the corresponding net benefit to determine whether consulting with the model is superior to adopting the default strategies. Finally, we examined whether laboratory data, which were not included in the METSSS model, would have been independently associated with survival after controlling for the METSSS model's predictions by using the multivariable logistic and Cox proportional hazards regression analyses. RESULTS Between the two models, only SORG-MLA achieved adequate discrimination (an AUROC of > 0.7) in the entire cohort (of patients treated operatively or with radiation alone) and in the subgroup of patients treated with palliative radiotherapy alone. SORG-MLA outperformed METSSS by a wide margin on discrimination, calibration, and Brier score analyses in not only the entire cohort but also the subgroup of patients whose local treatment consisted of radiotherapy alone. In both the entire cohort and the subgroup, DCA demonstrated that SORG-MLA provided more net benefit compared with the two default strategies (of treating all or no patients) and compared with METSSS when risk thresholds ranged from 0.2 to 0.9 at both 90 days and 1 year, indicating that using SORG-MLA as a decision-making aid was beneficial when a patient's individualized risk threshold for opting for treatment was 0.2 to 0.9. Higher albumin, lower alkaline phosphatase, lower calcium, higher hemoglobin, lower international normalized ratio, higher lymphocytes, lower neutrophils, lower neutrophil-to-lymphocyte ratio, lower platelet-to-lymphocyte ratio, higher sodium, and lower white blood cells were independently associated with better 1-year and overall survival after adjusting for the predictions made by METSSS. CONCLUSION Based on these discoveries, clinicians might choose to consult SORG-MLA instead of METSSS for survival estimation in patients with long-bone metastases presenting for evaluation of local treatment. Basing a treatment decision on the predictions of SORG-MLA could be beneficial when a patient's individualized risk threshold for opting to undergo a particular treatment strategy ranged from 0.2 to 0.9. Future studies might investigate relevant laboratory items when constructing or refining a survival estimation model because these data demonstrated prognostic value independent of the predictions of the METSSS model, and future studies might also seek to keep these models up to date using data from diverse, contemporary patients undergoing both modern operative and nonoperative treatments. LEVEL OF EVIDENCE Level III, diagnostic study.
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Affiliation(s)
- Chia-Che Lee
- Department of Orthopaedic Surgery, National Taiwan University Hospital, Taipei, Taiwan
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
| | - Chih-Wei Chen
- Department of Biomedical Engineering, National Taiwan University, Taipei, Taiwan
| | - Hung-Kuan Yen
- Department of Orthopaedic Surgery, National Taiwan University Hospital, Taipei, Taiwan
- Department of Orthopaedic Surgery, National Taiwan University Hospital, Hsin-Chu branch, Hsinchu, Taiwan
| | - Yen-Po Lin
- Department of Orthopaedic Surgery, National Taiwan University Hospital, Hsin-Chu branch, Hsinchu, Taiwan
| | - Cheng-Yo Lai
- Department of Orthopaedic Surgery, National Taiwan University Hospital, Hsin-Chu branch, Hsinchu, Taiwan
| | - Jaw-Lin Wang
- Department of Biomedical Engineering, National Taiwan University, Taipei, Taiwan
| | - Olivier Q Groot
- Department of Orthopedic Surgery and Sports Medicine, Amsterdam University Medical Centers, Amsterdam, the Netherlands
- Department of Orthopaedics, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Stein J Janssen
- Department of Orthopedic Surgery and Sports Medicine, Amsterdam University Medical Centers, Amsterdam, the Netherlands
| | - Joseph H Schwab
- Department of Orthopedics and Neurosurgery, Cedars Sinai Hospital, Los Angeles, CA, USA
| | - Feng-Ming Hsu
- Division of Radiation Oncology, Department of Oncology, National Taiwan University Hospital, Taipei, Taiwan
- Graduate Institute of Oncology, National Taiwan University College of Medicine, Taipei, Taiwan
- Department of Radiation Oncology, National Taiwan University Cancer Center, Taipei, Taiwan
| | - Wei-Hsin Lin
- Department of Orthopaedic Surgery, National Taiwan University Hospital, Taipei, Taiwan
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Cai Y, Cai YQ, Tang LY, Wang YH, Gong M, Jing TC, Li HJ, Li-Ling J, Hu W, Yin Z, Gong DX, Zhang GW. Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review. BMC Med 2024; 22:56. [PMID: 38317226 PMCID: PMC10845808 DOI: 10.1186/s12916-024-03273-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Accepted: 01/23/2024] [Indexed: 02/07/2024] Open
Abstract
BACKGROUND A comprehensive overview of artificial intelligence (AI) for cardiovascular disease (CVD) prediction and a screening tool of AI models (AI-Ms) for independent external validation are lacking. This systematic review aims to identify, describe, and appraise AI-Ms of CVD prediction in the general and special populations and develop a new independent validation score (IVS) for AI-Ms replicability evaluation. METHODS PubMed, Web of Science, Embase, and IEEE library were searched up to July 2021. Data extraction and analysis were performed for the populations, distribution, predictors, algorithms, etc. The risk of bias was evaluated with the prediction risk of bias assessment tool (PROBAST). Subsequently, we designed IVS for model replicability evaluation with five steps in five items, including transparency of algorithms, performance of models, feasibility of reproduction, risk of reproduction, and clinical implication, respectively. The review is registered in PROSPERO (No. CRD42021271789). RESULTS In 20,887 screened references, 79 articles (82.5% in 2017-2021) were included, which contained 114 datasets (67 in Europe and North America, but 0 in Africa). We identified 486 AI-Ms, of which the majority were in development (n = 380), but none of them had undergone independent external validation. A total of 66 idiographic algorithms were found; however, 36.4% were used only once and only 39.4% over three times. A large number of different predictors (range 5-52,000, median 21) and large-span sample size (range 80-3,660,000, median 4466) were observed. All models were at high risk of bias according to PROBAST, primarily due to the incorrect use of statistical methods. IVS analysis confirmed only 10 models as "recommended"; however, 281 and 187 were "not recommended" and "warning," respectively. CONCLUSION AI has led the digital revolution in the field of CVD prediction, but is still in the early stage of development as the defects of research design, report, and evaluation systems. The IVS we developed may contribute to independent external validation and the development of this field.
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Affiliation(s)
- Yue Cai
- China Medical University, Shenyang, 110122, China
| | - Yu-Qing Cai
- China Medical University, Shenyang, 110122, China
| | - Li-Ying Tang
- China Medical University, Shenyang, 110122, China
| | - Yi-Han Wang
- China Medical University, Shenyang, 110122, China
| | - Mengchun Gong
- Digital Health China Co. Ltd, Beijing, 100089, China
| | - Tian-Ci Jing
- Smart Hospital Management Department, the First Hospital of China Medical University, Shenyang, 110001, China
| | - Hui-Jun Li
- Shenyang Medical & Film Science and Technology Co. Ltd., Shenyang, 110001, China
- Enduring Medicine Smart Innovation Research Institute, Shenyang, 110001, China
| | - Jesse Li-Ling
- Institute of Genetic Medicine, School of Life Science, State Key Laboratory of Biotherapy, Sichuan University, Chengdu, 610065, China
| | - Wei Hu
- Bayi Orthopedic Hospital, Chengdu, 610017, China
| | - Zhihua Yin
- Department of Epidemiology, School of Public Health, China Medical University, Shenyang, 110122, China.
| | - Da-Xin Gong
- Smart Hospital Management Department, the First Hospital of China Medical University, Shenyang, 110001, China.
- The Internet Hospital Branch of the Chinese Research Hospital Association, Beijing, 100006, China.
| | - Guang-Wei Zhang
- Smart Hospital Management Department, the First Hospital of China Medical University, Shenyang, 110001, China.
- The Internet Hospital Branch of the Chinese Research Hospital Association, Beijing, 100006, China.
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Wolde HF, Clements ACA, Alene KA. Development and validation of a risk prediction model for pulmonary tuberculosis among presumptive tuberculosis cases in Ethiopia. BMJ Open 2023; 13:e076587. [PMID: 38101842 PMCID: PMC10729072 DOI: 10.1136/bmjopen-2023-076587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 09/22/2023] [Indexed: 12/17/2023] Open
Abstract
BACKGROUND Early diagnosis and treatment of tuberculosis (TB) is one of the key strategies to achieve the WHO End TB targets. This study aimed to develop and validate a simple, convenient risk score to diagnose pulmonary TB among presumptive TB cases. METHODS This prediction model used Ethiopian national TB prevalence survey data and included 5459 presumptive TB cases from all regions of Ethiopia. Logistic regression was used to determine which variables are predictive of pulmonary TB. A risk prediction model was developed, incorporating significant variables (p<0.05). The Youden Index method was used to choose the optimal cut-off point to separate the risk score of the patients as high and low. Model performance was assessed using discrimination power and calibration. Internal validation of the model was assessed using Efron's enhanced bootstrap method, and the clinical utility of the risk score was assessed using decision curve analysis. RESULTS Of total participants, 94 (1.7%) were confirmed to have TB. The final prediction model included three factors with different scores: (1) TB contact history, (2) chest X-ray (CXR) abnormality and (3) two or more symptoms of TB. The optimal cut-off point for the risk score was 6 and was found to have a good discrimination accuracy (c-statistic=0.70, 95% CI: 0.65 to 0.75). The risk score has sensitivity of 51.1%, specificity of 79.9%, positive predictive value of 4.3% and negative predictive value of 98.9%. After internal validation, the optimism coefficient was 0.003, which indicates the model is internally valid. CONCLUSION We developed a risk score that combines TB contact, number of TB symptoms and CXR abnormality to estimate individual risk of pulmonary TB among presumptive TB cases. Though the score is easy to calculate and internally validated, it needs external validation before widespread implementation in a new setting.
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Affiliation(s)
- Haileab Fekadu Wolde
- School of Population Health, Faculty of Health Sciences, Curtin University, Perth, Western Australia, Australia
- Geospatial and Tuberculosis Research Team, Telethon Kids Institute, Nedlands, Western Australia, Australia
- Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
| | | | - Kefyalew Addis Alene
- School of Population Health, Faculty of Health Sciences, Curtin University, Perth, Western Australia, Australia
- Geospatial and Tuberculosis Research Team, Telethon Kids Institute, Nedlands, Western Australia, Australia
- Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
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Merola GP, Boy OB, Fascina I, Pecoraro V, Falone A, Patti A, Santarelli G, Cicero DC, Ballerini A, Ricca V. Aberrant Salience Inventory: A meta-analysis to investigate its psychometric properties and identify screening cutoff scores. Scand J Psychol 2023; 64:734-745. [PMID: 37243361 DOI: 10.1111/sjop.12931] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Revised: 05/04/2023] [Accepted: 05/05/2023] [Indexed: 05/28/2023]
Abstract
INTRODUCTION The Aberrant Salience Inventory (ASI) is a useful tool to measure salience abnormalities among the general population. There is strong clinical and scientific evidence that salience alteration is linked to psychosis. To the present day, no meta-analysis evaluating ASI's psychometric properties and screening potential has been published. MATERIALS AND METHODS PubMed, Google Scholar, Scopus, and Embase were searched using terms including "psychosis," "schizophrenia," and "Aberrant Salience Inventory." Observational and experimental studies employing ASI on populations of non-psychotic controls and patients with psychosis were included. ASI scores and other demographic measures (age, gender, ethnicity) were extracted as outcomes. Individual patients' data (IPD) were collected. Exploratory factor analysis (EFA) was performed on the IPD. RESULTS Eight articles were finally included in the meta-analysis. ASI scores differ significantly between psychotic and non-psychotic populations; a novel three-factor model is proposed regarding subscales structure. Theoretical positive predictive values (PPVs) and negative predictive values (NPVs) were calculated and presented together with different cutoff points depending on preselected specific populations of interest. DISCUSSION PPV and NPV values reached levels adequate for ASI to be considered a viable screening tool for psychosis. The factor analysis highlights the presence of a novel subscale that was named "Unveiling experiences." Implications regarding the meaning of the new factor structure are discussed, as well as ASI's potential as a screening tool.
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Affiliation(s)
| | - Ottone Baccaredda Boy
- Psychiatry Unit, Department of Health Sciences, University of Florence, Florence, Italy
| | - Isotta Fascina
- Psychiatry Unit, Department of Health Sciences, University of Florence, Florence, Italy
| | - Vincenzo Pecoraro
- Psychiatry Unit, Department of Health Sciences, University of Florence, Florence, Italy
| | - Andrea Falone
- Psychiatry Unit, Department of Health Sciences, University of Florence, Florence, Italy
| | - Andrea Patti
- Psychiatry Unit, Department of Health Sciences, University of Florence, Florence, Italy
| | - Gabriele Santarelli
- Psychiatry Unit, Department of Health Sciences, University of Florence, Florence, Italy
| | | | - Andrea Ballerini
- Psychiatry Unit, Department of Health Sciences, University of Florence, Florence, Italy
| | - Valdo Ricca
- Psychiatry Unit, Department of Health Sciences, University of Florence, Florence, Italy
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Wilson J, Chowdhury F, Hassan S, Harriss EK, Alves F, Dahal P, Stepniewska K, Guérin PJ. Prognostic prediction models for clinical outcomes in patients diagnosed with visceral leishmaniasis: protocol for a systematic review. BMJ Open 2023; 13:e075597. [PMID: 37879686 PMCID: PMC10603465 DOI: 10.1136/bmjopen-2023-075597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 10/05/2023] [Indexed: 10/27/2023] Open
Abstract
INTRODUCTION Visceral leishmaniasis (VL) is a neglected tropical disease responsible for many thousands of preventable deaths each year. Symptomatic patients often struggle to access effective treatment, without which death is the norm. Risk prediction tools support clinical teams and policymakers in identifying high-risk patients who could benefit from more intensive management pathways. Investigators interested in using their clinical data for prognostic research should first identify currently available models that are candidates for validation and possible updating. Addressing these needs, we aim to identify, summarise and appraise the available models predicting clinical outcomes in VL patients. METHODS AND ANALYSIS We will include studies that have developed, validated or updated prognostic models predicting future clinical outcomes in patients diagnosed with VL. Systematic reviews and meta-analyses that include eligible studies are also considered for review. Conference abstracts and educational theses are excluded. Data extraction, appraisal and reporting will follow current methodological guidelines. Ovid Embase; Ovid MEDLINE; the Web of Science Core Collection, SciELO and LILACS are searched from database inception to 1 March 2023 using terms developed for the identification of prediction models, and with no language restriction. Screening, data extraction and risk of bias assessment will be performed in duplicate with discordance resolved by a third independent reviewer. Risk of bias will be assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Tables and figures will compare and contrast key model information, including source data, participants, model development and performance measures, and risk of bias. We will consider the strengths, limitations and clinical applicability of the identified models. ETHICS AND DISSEMINATION Ethics approval is not required for this review. The systematic review and all accompanying data will be submitted to an open-access journal. Findings will also be disseminated through the research group's website (www.iddo.org/research-themes/visceral-leishmaniasis) and social media channels. PROSPERO REGISTRATION NUMBER CRD42023417226.
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Affiliation(s)
- James Wilson
- Infectious Diseases Data Observatory (IDDO), University of Oxford, Oxford, UK
- Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
| | - Forhad Chowdhury
- Infectious Diseases Data Observatory (IDDO), University of Oxford, Oxford, UK
- Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
| | - Shermarke Hassan
- Infectious Diseases Data Observatory (IDDO), University of Oxford, Oxford, UK
- Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
| | - Elinor K Harriss
- Bodleian Health Care Libraries, University of Oxford, Oxford, UK
| | - Fabiana Alves
- Drugs for Neglected Disease Initiative, Geneva, Switzerland
| | - Prabin Dahal
- Infectious Diseases Data Observatory (IDDO), University of Oxford, Oxford, UK
- Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
| | - Kasia Stepniewska
- Infectious Diseases Data Observatory (IDDO), University of Oxford, Oxford, UK
- Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
| | - Philippe J Guérin
- Infectious Diseases Data Observatory (IDDO), University of Oxford, Oxford, UK
- Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
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8
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Hoogland J, Takada T, van Smeden M, Rovers MM, de Sutter AI, Merenstein D, Kaiser L, Liira H, Little P, Bucher HC, Moons KGM, Reitsma JB, Venekamp RP. Prognosis and prediction of antibiotic benefit in adults with clinically diagnosed acute rhinosinusitis: an individual participant data meta-analysis. Diagn Progn Res 2023; 7:16. [PMID: 37667327 PMCID: PMC10478354 DOI: 10.1186/s41512-023-00154-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 07/20/2023] [Indexed: 09/06/2023] Open
Abstract
BACKGROUND A previous individual participant data meta-analysis (IPD-MA) of antibiotics for adults with clinically diagnosed acute rhinosinusitis (ARS) showed a marginal overall effect of antibiotics, but was unable to identify patients that are most likely to benefit from antibiotics when applying conventional (i.e. univariable or one-variable-at-a-time) subgroup analysis. We updated the systematic review and investigated whether multivariable prediction of patient-level prognosis and antibiotic treatment effect may lead to more tailored treatment assignment in adults presenting to primary care with ARS. METHODS An IPD-MA of nine double-blind placebo-controlled trials of antibiotic treatment (n=2539) was conducted, with the probability of being cured at 8-15 days as the primary outcome. A logistic mixed effects model was developed to predict the probability of being cured based on demographic characteristics, signs and symptoms, and antibiotic treatment assignment. Predictive performance was quantified based on internal-external cross-validation in terms of calibration and discrimination performance, overall model fit, and the accuracy of individual predictions. RESULTS Results indicate that the prognosis with respect to risk of cure could not be reliably predicted (c-statistic 0.58 and Brier score 0.24). Similarly, patient-level treatment effect predictions did not reliably distinguish between those that did and did not benefit from antibiotics (c-for-benefit 0.50). CONCLUSIONS In conclusion, multivariable prediction based on patient demographics and common signs and symptoms did not reliably predict the patient-level probability of cure and antibiotic effect in this IPD-MA. Therefore, these characteristics cannot be expected to reliably distinguish those that do and do not benefit from antibiotics in adults presenting to primary care with ARS.
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Affiliation(s)
- Jeroen Hoogland
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
- Department of Epidemiology and Data Science, Amsterdam University Medical Centres, Amsterdam University, Amsterdam, The Netherlands.
| | - Toshihiko Takada
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Department of General Medicine, Shirakawa Satellite for Teaching And Research (STAR), Fukushima Medical University, Fukushima, Japan
| | - Maarten van Smeden
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Maroeska M Rovers
- Radboud Institute for Health Sciences (RIHS), Radboud University Medical Center, Nijmegen, The Netherlands
| | - An I de Sutter
- Department of Public Health and Primary Care, Ghent University, Ghent, Belgium
| | - Daniel Merenstein
- Department of Family Medicine, Georgetown University Medical Center, Washington, DC, USA
| | - Laurent Kaiser
- Department of Medicine, Division of Infectious Diseases, University Hospital Geneva, Geneva, Switzerland
| | - Helena Liira
- Department of General Practice, School of Primary, Aboriginal and Rural Health Care, University of Western Australia, Perth, Australia
- Department of General Practice and Primary Care, University of Helsinki, Helsinki, Finland
| | - Paul Little
- Primary Care & Population Sciences Unit, Aldermoor Health Centre, University of Southampton, Southampton, UK
| | - Heiner C Bucher
- Division of Clinical Epidemiology, Department of Clinical Research, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Karel G M Moons
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Johannes B Reitsma
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Roderick P Venekamp
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
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Ajuwon BI, Richardson A, Roper K, Lidbury BA. Clinical Validity of a Machine Learning Decision Support System for Early Detection of Hepatitis B Virus: A Binational External Validation Study. Viruses 2023; 15:1735. [PMID: 37632077 PMCID: PMC10458613 DOI: 10.3390/v15081735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 08/04/2023] [Accepted: 08/10/2023] [Indexed: 08/27/2023] Open
Abstract
HepB LiveTest is a machine learning decision support system developed for the early detection of hepatitis B virus (HBV). However, there is a lack of evidence on its generalisability. In this study, we aimed to externally assess the clinical validity and portability of HepB LiveTest in predicting HBV infection among independent patient cohorts from Nigeria and Australia. The performance of HepB LiveTest was evaluated by constructing receiver operating characteristic curves and estimating the area under the curve. Delong's method was used to estimate the 95% confidence interval (CI) of the area under the receiver-operating characteristic curve (AUROC). Compared to the Australian cohort, patients in the derivation cohort of HepB LiveTest and the hospital-based Nigerian cohort were younger (mean age, 45.5 years vs. 38.8 years vs. 40.8 years, respectively; p < 0.001) and had a higher incidence of HBV infection (1.9% vs. 69.4% vs. 57.3%). In the hospital-based Nigerian cohort, HepB LiveTest performed optimally with an AUROC of 0.94 (95% CI, 0.91-0.97). The model provided tailored predictions that ensured most cases of HBV infection did not go undetected. However, its discriminatory measure dropped to 0.60 (95% CI, 0.56-0.64) in the Australian cohort. These findings indicate that HepB LiveTest exhibits adequate cross-site transportability and clinical validity in the hospital-based Nigerian patient cohort but shows limited performance in the Australian cohort. Whilst HepB LiveTest holds promise for reducing HBV prevalence in underserved populations, caution is warranted when implementing the model in older populations, particularly in regions with low incidence of HBV infection.
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Affiliation(s)
- Busayo I. Ajuwon
- National Centre for Epidemiology and Population Health, ANU College of Health and Medicine, The Australian National University, Acton, Canberra, ACT 2601, Australia; (K.R.); (B.A.L.)
- Department of Biosciences and Biotechnology, Faculty of Pure and Applied Sciences, Kwara State University, Malete 241103, Nigeria
| | - Alice Richardson
- Statistical Support Network, The Australian National University, Acton, Canberra, ACT 2601, Australia;
| | - Katrina Roper
- National Centre for Epidemiology and Population Health, ANU College of Health and Medicine, The Australian National University, Acton, Canberra, ACT 2601, Australia; (K.R.); (B.A.L.)
| | - Brett A. Lidbury
- National Centre for Epidemiology and Population Health, ANU College of Health and Medicine, The Australian National University, Acton, Canberra, ACT 2601, Australia; (K.R.); (B.A.L.)
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Dhana A, Gupta RK, Hamada Y, Kengne AP, Kerkhoff AD, Yoon C, Cattamanchi A, Reeve BWP, Theron G, Ndlangalavu G, Wood R, Drain PK, Calderwood CJ, Noursadeghi M, Boyles T, Meintjes G, Maartens G, Barr DA. Clinical utility of WHO-recommended screening tools and development and validation of novel clinical prediction models for pulmonary tuberculosis screening among outpatients living with HIV: an individual participant data meta-analysis. Eur Respir Rev 2023; 32:230021. [PMID: 37286216 PMCID: PMC10245131 DOI: 10.1183/16000617.0021-2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 03/15/2023] [Indexed: 06/09/2023] Open
Abstract
BACKGROUND The World Health Organization (WHO) recommends that outpatient people living with HIV (PLHIV) undergo tuberculosis screening with the WHO four-symptom screen (W4SS) or C-reactive protein (CRP) (5 mg·L-1 cut-off) followed by confirmatory testing if screen positive. We conducted an individual participant data meta-analysis to determine the performance of WHO-recommended screening tools and two newly developed clinical prediction models (CPMs). METHODS Following a systematic review, we identified studies that recruited adult outpatient PLHIV irrespective of tuberculosis signs and symptoms or with a positive W4SS, evaluated CRP and collected sputum for culture. We used logistic regression to develop an extended CPM (which included CRP and other predictors) and a CRP-only CPM. We used internal-external cross-validation to evaluate performance. RESULTS We pooled data from eight cohorts (n=4315 participants). The extended CPM had excellent discrimination (C-statistic 0.81); the CRP-only CPM had similar discrimination. The C-statistics for WHO-recommended tools were lower. Both CPMs had equivalent or higher net benefit compared with the WHO-recommended tools. Compared with both CPMs, CRP (5 mg·L-1 cut-off) had equivalent net benefit across a clinically useful range of threshold probabilities, while the W4SS had a lower net benefit. The W4SS would capture 91% of tuberculosis cases and require confirmatory testing for 78% of participants. CRP (5 mg·L-1 cut-off), the extended CPM (4.2% threshold) and the CRP-only CPM (3.6% threshold) would capture similar percentages of cases but reduce confirmatory tests required by 24, 27 and 36%, respectively. CONCLUSIONS CRP sets the standard for tuberculosis screening among outpatient PLHIV. The choice between using CRP at 5 mg·L-1 cut-off or in a CPM depends on available resources.
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Affiliation(s)
- Ashar Dhana
- Department of Medicine, University of Cape Town, Cape Town, South Africa
| | - Rishi K Gupta
- Institute for Global Health, University College London, London, UK
| | - Yohhei Hamada
- Institute for Global Health, University College London, London, UK
- Centre for International Cooperation and Global TB Information, The Research Institute of Tuberculosis, Japan Anti-Tuberculosis Association, Tokyo, Japan
| | - Andre P Kengne
- Non-communicable Diseases Research Unit, South African Medical Research Council, Cape Town, South Africa
| | - Andrew D Kerkhoff
- Division of HIV, Infectious Diseases and Global Medicine, Zuckerberg San Francisco General Hospital and Trauma Center, University of California San Francisco, San Francisco, CA, USA
| | - Christina Yoon
- Department of Medicine, Division of Pulmonary and Critical Care Medicine and Center for Tuberculosis, University of California San Francisco, San Francisco, CA, USA
| | - Adithya Cattamanchi
- Department of Medicine, Division of Pulmonary and Critical Care Medicine and Center for Tuberculosis, University of California San Francisco, San Francisco, CA, USA
| | - Byron W P Reeve
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Grant Theron
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Gcobisa Ndlangalavu
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Robin Wood
- Institute of Infectious Disease and Molecular Medicine (IDM), Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Paul K Drain
- Departments of Global Health, Medicine, and Epidemiology, University of Washington, Seattle, WA, USA
| | - Claire J Calderwood
- Institute for Global Health, University College London, London, UK
- Clinical Research Department, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK
- The Research Unit Zimbabwe, Biomedical Research and Training Institute, Harare, Zimbabwe
| | - Mahdad Noursadeghi
- Division of Infection and Immunity, University College London, London, UK
| | - Tom Boyles
- Helen Joseph Hospital, Johannesburg, South Africa
| | - Graeme Meintjes
- Department of Medicine, University of Cape Town, Cape Town, South Africa
- Wellcome Centre for Infectious Diseases Research in Africa (CIDRI-Africa), Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa
| | - Gary Maartens
- Department of Medicine, University of Cape Town, Cape Town, South Africa
- Wellcome Centre for Infectious Diseases Research in Africa (CIDRI-Africa), Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa
| | - David A Barr
- Institute of Infection and Global Health, University of Liverpool, Liverpool, UK
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Luo Y, Chalkou K, Funada S, Salanti G, Furukawa TA. Estimating Patient-Specific Relative Benefit of Adding Biologics to Conventional Rheumatoid Arthritis Treatment: An Individual Participant Data Meta-Analysis. JAMA Netw Open 2023; 6:e2321398. [PMID: 37389866 PMCID: PMC10314313 DOI: 10.1001/jamanetworkopen.2023.21398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 05/16/2023] [Indexed: 07/01/2023] Open
Abstract
Importance Current evidence remains ambiguous regarding whether biologics should be added to conventional treatment of rheumatoid arthritis for specific patients, which may cause potential overuse or treatment delay. Objectives To estimate the benefit of adding biologics to conventional antirheumatic drugs for the treatment of rheumatoid arthritis given baseline characteristics. Data Sources Cochrane CENTRAL, Scopus, MEDLINE, and the World Health Organization International Clinical Trials Registry Platform were searched for articles published from database inception to March 2, 2022. Study Selection Randomized clinical trials comparing certolizumab plus conventional antirheumatic drugs with placebo plus conventional drugs were selected. Data Extraction and Synthesis Individual participant data of the prespecified outcomes and covariates were acquired from the Vivli database. A 2-stage model was fitted to estimate patient-specific relative outcomes of adding certolizumab vs conventional drugs only. Stage 1 was a penalized logistic regression model to estimate the baseline expected probability of the outcome regardless of treatment using baseline characteristics. Stage 2 was a bayesian individual participant data meta-regression model to estimate the relative outcomes for a particular baseline expected probability. Patient-specific results were displayed interactively on an application based on a 2-stage model. Main Outcomes and Measures The primary outcome was low disease activity or remission at 3 months, defined by 3 disease activity indexes (ie, Disease Activity Score based on the evaluation of 28 joints, Clinical Disease Activity Index, or Simplified Disease Activity Index). Results Individual participant data were obtained from 3790 patients (2996 female [79.1%] and 794 male [20.9%]; mean [SD] age, 52.7 [12.3] years) from 5 large randomized clinical trials for moderate to high activity rheumatoid arthritis with usable data for 22 prespecified baseline covariates. Overall, adding certolizumab was associated with a higher probability of reaching low disease activity. The odds ratio for patients with an average baseline expected probability of the outcome was 6.31 (95% credible interval, 2.22-15.25). However, the benefits differed in patients with different baseline characteristics. For example, the estimated risk difference was smaller than 10% for patients with either low or high baseline expected probability. Conclusions and Relevance In this individual participant data meta-analysis, adding certolizumab was associated with more effectiveness for rheumatoid arthritis in general. However, the benefit was uncertain for patients with low or high baseline expected probability, for whom other evaluations were necessary. The interactive application displaying individual estimates may help with treatment selection.
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Affiliation(s)
- Yan Luo
- Department of Health Promotion and Human Behavior, School of Public Health, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Population Health and Policy Research Unit, Medical Education Center, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Konstantina Chalkou
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Graduate School for Health Sciences, University of Bern, Bern, Switzerland
| | - Satoshi Funada
- Department of Health Promotion and Human Behavior, School of Public Health, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Department of Preventive Medicine and Public Health, School of Medicine, Keio University, Tokyo, Japan
| | - Georgia Salanti
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
| | - Toshi A. Furukawa
- Department of Health Promotion and Human Behavior, School of Public Health, Graduate School of Medicine, Kyoto University, Kyoto, Japan
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Karamouza E, Glasspool RM, Kelly C, Lewsley LA, Carty K, Kristensen GB, Ethier JL, Kagimura T, Yanaihara N, Cecere SC, You B, Boere IA, Pujade-Lauraine E, Ray-Coquard I, Proust-Lima C, Paoletti X. CA-125 Early Dynamics to Predict Overall Survival in Women with Newly Diagnosed Advanced Ovarian Cancer Based on Meta-Analysis Data. Cancers (Basel) 2023; 15:1823. [PMID: 36980708 PMCID: PMC10047009 DOI: 10.3390/cancers15061823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Revised: 03/10/2023] [Accepted: 03/13/2023] [Indexed: 03/19/2023] Open
Abstract
(1) Background: Cancer antigen 125 (CA-125) is a protein produced by ovarian cancer cells that is used for patients' monitoring. However, the best ways to analyze its decline and prognostic role are poorly quantified. (2) Methods: We leveraged individual patient data from the Gynecologic Cancer Intergroup (GCIG) meta-analysis (N = 5573) to compare different approaches summarizing the early trajectory of CA-125 before the prediction time (called the landmark time) at 3 or 6 months after treatment initiation in order to predict overall survival. These summaries included observed and estimated measures obtained by a linear mixed model (LMM). Their performances were evaluated by 10-fold cross-validation with the Brier score and the area under the ROC (AUC). (3) Results: The estimated value and the last observed value at 3 months were the best measures used to predict overall survival, with an AUC of 0.75 CI 95% [0.70; 0.80] at 24 and 36 months and 0.74 [0.69; 0.80] and 0.75 [0.69; 0.80] at 48 months, respectively, considering that CA-125 over 6 months did not improve the AUC, with 0.74 [0.68; 0.78] at 24 months and 0.71 [0.65; 0.76] at 36 and 48 months. (4) Conclusions: A 3-month surveillance provided reliable individual information on overall survival until 48 months for patients receiving first-line chemotherapy.
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Affiliation(s)
- Eleni Karamouza
- Gustave Roussy, Office of Biostatistics and Epidemiology, Université Paris-Saclay, 94805 Villejuif, France
- Oncostat, Labeled Ligue Contre le Cancer, CESP U1018, Inserm, Université Paris-Saclay, 94805 Villejuif, France
| | - Rosalind M. Glasspool
- Beatson West of Scotland Cancer Centre, NHS Greater Glasgow and Clyde, Glasgow G12 0XH, UK
| | - Caroline Kelly
- Cancer Research UK Clinical Trials Unit, Institute of Cancer Sciences, University of Glasgow, Glasgow G12 0YN, UK
| | - Liz-Anne Lewsley
- Cancer Research UK Clinical Trials Unit, Institute of Cancer Sciences, University of Glasgow, Glasgow G12 0YN, UK
| | - Karen Carty
- Cancer Research UK Clinical Trials Unit, Institute of Cancer Sciences, University of Glasgow, Glasgow G12 0YN, UK
| | - Gunnar B. Kristensen
- Department of Gynecologic Oncology, Institute for Cancer Genetics and Informatics, Oslo University Hospital, 0424 Oslo, Norway
| | - Josee-Lyne Ethier
- Department of Medical Oncology, Cancer Centre of Southeastern Ontario, Queen’s University, Kingston, ON K7L 3N6, Canada
| | - Tatsuo Kagimura
- Foundation for Biomedical Research and Innocation, Translational Research Center for Medical Innovation, Kobe 650-0047, Japan
| | | | - Sabrina Chiara Cecere
- Department of Urology and Gynecology, Istituto Nazionale Tumori IRCCS Fondazione G. Pascale, 80131 Napoli, Italy
| | - Benoit You
- EMR UCBL/HCL 3738, Faculté de Médecine Lyon-Sud, Université Lyon, Université Claude Bernard Lyon 1, 69100 Lyon, France
- Medical Oncology, Institut de Cancérologie des Hospices Civils de Lyon (IC-HCL), CITOHL, Centre Hospitalier Lyon-Sud, GINECO, GINEGEPS, 69495 Lyon, France
| | - Ingrid A. Boere
- Department of Medical Oncology, Erasmus MC Cancer Institute, 3015 GD Rotterdam, The Netherlands
| | | | | | - Cécile Proust-Lima
- UMR1219, Bordeaux Population Health Research Center, Inserm, University of Bordeaux, 33000 Bordeaux, France
| | - Xavier Paoletti
- Faculty of Medicine, University of Versailles Saint-Quentin, Université Paris Saclay, 78000 Versailles, France
- INSERM U900, Statistics for Personalized Medicine, Institut Curie, 92210 Saint-Cloud, France
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13
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Ribero VA, Alwan H, Efthimiou O, Abolhassani N, Bauer DC, Henrard S, Christiaens A, Waeber G, Rodondi N, Gencer B, Del Giovane C. Cardiovascular disease and type 2 diabetes in older adults: a combined protocol for an individual participant data analysis for risk prediction and a network meta-analysis of novel anti-diabetic drugs. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.13.23287105. [PMID: 36993427 PMCID: PMC10055459 DOI: 10.1101/2023.03.13.23287105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/16/2023]
Abstract
Introduction Older and multimorbid adults with type 2 diabetes (T2D) are at high risk of cardiovascular disease (CVD) and chronic kidney disease (CKD). Estimating risk and preventing CVD is a challenge in this population notably because it is underrepresented in clinical trials. Our study aims to (1) assess if T2D and haemoglobin A1c (HbA1c) are associated with the risk of CVD events and mortality in older adults, (2) develop a risk score for CVD events and mortality for older adults with T2D, (3) evaluate the comparative efficacy and safety of novel antidiabetics. Methods and analysis For Aim 1, we will analyse individual participant data on individuals aged ≥65 years from five cohort studies: the Optimising Therapy to Prevent Avoidable Hospital Admissions in Multimorbid Older People study; the Cohorte Lausannoise study; the Health, Aging and Body Composition study; the Health and Retirement Study; and the Survey of Health, Ageing and Retirement in Europe. We will fit flexible parametric survival models (FPSM) to assess the association of T2D and HbA1c with CVD events and mortality. For Aim 2, we will use data on individuals aged ≥65 years with T2D from the same cohorts to develop risk prediction models for CVD events and mortality using FPSM. We will assess model performance, perform internal-external cross validation, and derive a point-based risk score. For Aim 3, we will systematically search randomized controlled trials of novel antidiabetics. Network meta-analysis will be used to determine comparative efficacy in terms of CVD, CKD, and retinopathy outcomes, and safety of these drugs. Confidence in results will be judged using the CINeMA tool. Ethics and dissemination Aims 1 and 2 were approved by the local ethics committee (Kantonale Ethikkommission Bern); no approval is required for Aim 3. Results will be published in peer-reviewed journals and presented in scientific conferences.
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Affiliation(s)
- Valerie Aponte Ribero
- Institute of Primary Health Care (BIHAM), University of Bern, 3012, Bern, Switzerland
- Graduate School for Health Sciences, University of Bern, Mittelstrasse 43, 3012, Bern, Switzerland
| | - Heba Alwan
- Institute of Primary Health Care (BIHAM), University of Bern, 3012, Bern, Switzerland
- Graduate School for Health Sciences, University of Bern, Mittelstrasse 43, 3012, Bern, Switzerland
| | - Orestis Efthimiou
- Institute of Primary Health Care (BIHAM), University of Bern, 3012, Bern, Switzerland
- Institute of Social and Preventive Medicine, University of Bern, 3012, Bern, Switzerland
| | - Nazanin Abolhassani
- Institute of Primary Health Care (BIHAM), University of Bern, 3012, Bern, Switzerland
- Department of Epidemiology and Health Systems, Center for Primary Care and Public Health (Unisante), University of Lausanne, Switzerland
| | - Douglas C Bauer
- Departments of Medicine and Epidemiology & Biostatistics, University of California San Francisco, San Francisco, California, USA
| | - Séverine Henrard
- Clinical Pharmacy research group, Louvain Drug Research Institute (LDRI), Université catholique de Louvain, 1200, Brussels, Belgium
- Institute of Health and Society (IRSS), Université catholique de Louvain, 1200 Brussels, Belgium
| | - Antoine Christiaens
- Clinical Pharmacy research group, Louvain Drug Research Institute (LDRI), Université catholique de Louvain, 1200, Brussels, Belgium
- Fonds de la Recherche Scientifique – FNRS, 1000 Brussels, Belgium
| | - Gérard Waeber
- Department of Medicine, Lausanne University Hospital (CHUV), University of Lausanne, 1011, Lausanne, Switzerland
| | - Nicolas Rodondi
- Institute of Primary Health Care (BIHAM), University of Bern, 3012, Bern, Switzerland
- Department of General Internal Medicine, Inselspital, Bern University Hospital, University of Bern, 3010, Bern, Switzerland
| | - Baris Gencer
- Institute of Primary Health Care (BIHAM), University of Bern, 3012, Bern, Switzerland
- Cardiology Division, Geneva University Hospitals, 1205, Geneva, Switzerland
| | - Cinzia Del Giovane
- Institute of Primary Health Care (BIHAM), University of Bern, 3012, Bern, Switzerland
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14
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Feleke SF, Dessie AM, Tenaw D, Yimer A, Geremew H, Mulatie R, Kebede A. Systematic review and meta-analysis protocol for development and validation of a prediction model for gestational hypertension in Africa. SAGE Open Med 2023; 11:20503121231153508. [PMID: 36778201 PMCID: PMC9912540 DOI: 10.1177/20503121231153508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 01/10/2023] [Indexed: 02/11/2023] Open
Abstract
Objective Examining the development and validation of predictive models for gestational hypertension, evaluating the validity of the methodology, and investigating predictors typically employed in such models. Design Systematic review and meta-analysis protocol. Methods The Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols (PRISMA-P) guideline will be used to carry out the study procedure. Using the key phrases "Gestational hypertension," "prediction, risk prediction," and "validation," a full systematic search will be conducted in PubMed/MEDLINE, Hinari, Cochrane Library, and Google Scholar. The methodological quality of the included studies will be evaluated using the prediction model risk of bias assessment tool. The CHARMS (checklist for critical evaluation and data extraction for systematic reviews of prediction modeling research) will be used to extract the data, and STATA 16 will be used to analyze it. The degree of study heterogeneity will be assessed using Cochrane I2 statistics. Discussion A subgroup analysis will be performed to reduce the variance between primary studies. To examine the impact of individual studies on the pooled estimates, a sensitivity analysis will be performed. The funnel plot test and Egger's statistical test will be used to assess the small study effect. The presence of a modest study effect is shown by Egger's test (p-value 0.05), which will be handled by nonparametric trim and fill analysis using the random-effects model. The protocol has been registered in the PROSPERO-International Prospective Register of systematic reviews, with the registration number CRD42022314601.
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Affiliation(s)
- Sefineh Fenta Feleke
- Department of Public Health, College of
Health Sciences, Woldia University, Woldia, Ethiopia
| | - Anteneh Mengist Dessie
- Department of Public Health, College of
Health Sciences, Debre Tabor University, Debre Tabor, Ethiopia
| | - Denekew Tenaw
- Department of Public Health, College of
Health Sciences, Debre Tabor University, Debre Tabor, Ethiopia
| | - Ali Yimer
- Department of Public Health, College of
Health Sciences, Woldia University, Woldia, Ethiopia
| | - Habtamu Geremew
- Department of Nursing, College of
Health Sciences, Oda Bultum University, Chiro, Ethiopia
| | - Rahel Mulatie
- Department of Public Health, College of
Health Sciences, Debre Tabor University, Debre Tabor, Ethiopia
| | - Abayneh Kebede
- Department of Mathematics, College of
Natural and Computational Sciences, Debre Tabor University, Debre Tabor,
Ethiopia
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15
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Debray TPA, Collins GS, Riley RD, Snell KIE, Van Calster B, Reitsma JB, Moons KGM. Transparent reporting of multivariable prediction models developed or validated using clustered data: TRIPOD-Cluster checklist. BMJ 2023; 380:e071018. [PMID: 36750242 PMCID: PMC9903175 DOI: 10.1136/bmj-2022-071018] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/09/2022] [Indexed: 02/09/2023]
Affiliation(s)
- Thomas P A Debray
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Gary S Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Oxford, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - Richard D Riley
- Centre for Prognosis Research, School of Primary, Community and Social Care, Keele University, Keele, UK
| | - Kym I E Snell
- Centre for Prognosis Research, School of Primary, Community and Social Care, Keele University, Keele, UK
| | - Ben Van Calster
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
| | - Johannes B Reitsma
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Karel G M Moons
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
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16
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Debray TPA, Collins GS, Riley RD, Snell KIE, Van Calster B, Reitsma JB, Moons KGM. Transparent reporting of multivariable prediction models developed or validated using clustered data (TRIPOD-Cluster): explanation and elaboration. BMJ 2023; 380:e071058. [PMID: 36750236 PMCID: PMC9903176 DOI: 10.1136/bmj-2022-071058] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/07/2022] [Indexed: 02/09/2023]
Affiliation(s)
- Thomas P A Debray
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Gary S Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Oxford, UK
- National Institute for Health and Care Research Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - Richard D Riley
- Centre for Prognosis Research, School of Medicine, Keele University, Keele, UK
| | - Kym I E Snell
- Centre for Prognosis Research, School of Medicine, Keele University, Keele, UK
| | - Ben Van Calster
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- EPI-centre, KU Leuven, Leuven, Belgium
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, Netherlands
| | - Johannes B Reitsma
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Karel G M Moons
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
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17
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Implementing clinical trial data sharing requires training a new generation of biomedical researchers. Nat Med 2023; 29:298-301. [PMID: 36732626 DOI: 10.1038/s41591-022-02080-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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18
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Binuya MAE, Engelhardt EG, Schats W, Schmidt MK, Steyerberg EW. Methodological guidance for the evaluation and updating of clinical prediction models: a systematic review. BMC Med Res Methodol 2022; 22:316. [PMID: 36510134 PMCID: PMC9742671 DOI: 10.1186/s12874-022-01801-8] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 11/22/2022] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Clinical prediction models are often not evaluated properly in specific settings or updated, for instance, with information from new markers. These key steps are needed such that models are fit for purpose and remain relevant in the long-term. We aimed to present an overview of methodological guidance for the evaluation (i.e., validation and impact assessment) and updating of clinical prediction models. METHODS We systematically searched nine databases from January 2000 to January 2022 for articles in English with methodological recommendations for the post-derivation stages of interest. Qualitative analysis was used to summarize the 70 selected guidance papers. RESULTS Key aspects for validation are the assessment of statistical performance using measures for discrimination (e.g., C-statistic) and calibration (e.g., calibration-in-the-large and calibration slope). For assessing impact or usefulness in clinical decision-making, recent papers advise using decision-analytic measures (e.g., the Net Benefit) over simplistic classification measures that ignore clinical consequences (e.g., accuracy, overall Net Reclassification Index). Commonly recommended methods for model updating are recalibration (i.e., adjustment of intercept or baseline hazard and/or slope), revision (i.e., re-estimation of individual predictor effects), and extension (i.e., addition of new markers). Additional methodological guidance is needed for newer types of updating (e.g., meta-model and dynamic updating) and machine learning-based models. CONCLUSION Substantial guidance was found for model evaluation and more conventional updating of regression-based models. An important development in model evaluation is the introduction of a decision-analytic framework for assessing clinical usefulness. Consensus is emerging on methods for model updating.
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Affiliation(s)
- M. A. E. Binuya
- grid.430814.a0000 0001 0674 1393Division of Molecular Pathology, the Netherlands Cancer Institute – Antoni van Leeuwenhoek Hospital, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands ,grid.10419.3d0000000089452978Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands ,grid.10419.3d0000000089452978Department of Clinical Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - E. G. Engelhardt
- grid.430814.a0000 0001 0674 1393Division of Molecular Pathology, the Netherlands Cancer Institute – Antoni van Leeuwenhoek Hospital, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands ,grid.430814.a0000 0001 0674 1393Division of Psychosocial Research and Epidemiology, the Netherlands Cancer Institute – Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - W. Schats
- grid.430814.a0000 0001 0674 1393Scientific Information Service, The Netherlands Cancer Institute – Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - M. K. Schmidt
- grid.430814.a0000 0001 0674 1393Division of Molecular Pathology, the Netherlands Cancer Institute – Antoni van Leeuwenhoek Hospital, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands ,grid.10419.3d0000000089452978Department of Clinical Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - E. W. Steyerberg
- grid.10419.3d0000000089452978Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
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19
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Meijs DA, van Kuijk SM, Wynants L, Stessel B, Mehagnoul-Schipper J, Hana A, Scheeren CI, Bergmans DC, Bickenbach J, Vander Laenen M, Smits LJ, van der Horst IC, Marx G, Mesotten D, van Bussel BC. Predicting COVID-19 prognosis in the ICU remained challenging: external validation in a multinational regional cohort. J Clin Epidemiol 2022; 152:257-268. [PMID: 36309146 PMCID: PMC9605784 DOI: 10.1016/j.jclinepi.2022.10.015] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 10/04/2022] [Accepted: 10/19/2022] [Indexed: 01/25/2023]
Abstract
OBJECTIVES Many prediction models for coronavirus disease 2019 (COVID-19) have been developed. External validation is mandatory before implementation in the intensive care unit (ICU). We selected and validated prognostic models in the Euregio Intensive Care COVID (EICC) cohort. STUDY DESIGN AND SETTING In this multinational cohort study, routine data from COVID-19 patients admitted to ICUs within the Euregio Meuse-Rhine were collected from March to August 2020. COVID-19 models were selected based on model type, predictors, outcomes, and reporting. Furthermore, general ICU scores were assessed. Discrimination was assessed by area under the receiver operating characteristic curves (AUCs) and calibration by calibration-in-the-large and calibration plots. A random-effects meta-analysis was used to pool results. RESULTS 551 patients were admitted. Mean age was 65.4 ± 11.2 years, 29% were female, and ICU mortality was 36%. Nine out of 238 published models were externally validated. Pooled AUCs were between 0.53 and 0.70 and calibration-in-the-large between -9% and 6%. Calibration plots showed generally poor but, for the 4C Mortality score and Spanish Society of Infectious Diseases and Clinical Microbiology (SEIMC) score, moderate calibration. CONCLUSION Of the nine prognostic models that were externally validated in the EICC cohort, only two showed reasonable discrimination and moderate calibration. For future pandemics, better models based on routine data are needed to support admission decision-making.
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Affiliation(s)
- Daniek A.M. Meijs
- Department of Intensive Care Medicine, Maastricht University Medical Centre (Maastricht UMC+), Maastricht, The Netherlands,Department of Intensive Care Medicine, Laurentius Ziekenhuis, Roermond, The Netherlands,Cardiovascular Research Institute Maastricht (CARIM), Maastricht, The Netherlands,Corresponding author: Maastricht UMC+, Department of Intensive Care Medicine, P. Debyelaan 25, 6229 HX Maastricht, The Netherlands. Tel.: +31620126764; fax: +31433874330
| | - Sander M.J. van Kuijk
- Department of Epidemiology, Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, The Netherlands
| | - Laure Wynants
- Department of Epidemiology, Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, The Netherlands,Department of Development and Regeneration, KULeuven, Leuven, Belgium,Epi-centre, KULeuven, Leuven, Belgium
| | - Björn Stessel
- Department of Intensive Care Medicine, Jessa Hospital, Hasselt, Belgium,Faculty of Medicine and Life Sciences, UHasselt, Diepenbeek, Belgium
| | | | - Anisa Hana
- Department of Intensive Care Medicine, Laurentius Ziekenhuis, Roermond, The Netherlands,Department of Intensive Care Medicine, University Hospital of Zurich, Zurich, Switzerland
| | - Clarissa I.E. Scheeren
- Department of Intensive Care Medicine, Zuyderland Medisch Centrum, Heerlen/Sittard, The Netherlands
| | - Dennis C.J.J. Bergmans
- Department of Intensive Care Medicine, Maastricht University Medical Centre (Maastricht UMC+), Maastricht, The Netherlands,School of Nutrition and Translational Research in Metabolism (NUTRIM), Maastricht University, Maastricht, The Netherlands
| | - Johannes Bickenbach
- Department of Intensive Care Medicine, University Hospital Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen, Aachen, Germany
| | | | - Luc J.M. Smits
- Department of Epidemiology, Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, The Netherlands
| | - Iwan C.C. van der Horst
- Department of Intensive Care Medicine, Maastricht University Medical Centre (Maastricht UMC+), Maastricht, The Netherlands,Cardiovascular Research Institute Maastricht (CARIM), Maastricht, The Netherlands
| | - Gernot Marx
- Department of Intensive Care Medicine, University Hospital Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen, Aachen, Germany
| | - Dieter Mesotten
- Faculty of Medicine and Life Sciences, UHasselt, Diepenbeek, Belgium,Department of Intensive Care Medicine, Ziekenhuis Oost-Limburg, Genk, Belgium
| | - Bas C.T. van Bussel
- Department of Intensive Care Medicine, Maastricht University Medical Centre (Maastricht UMC+), Maastricht, The Netherlands,Cardiovascular Research Institute Maastricht (CARIM), Maastricht, The Netherlands,Department of Epidemiology, Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, The Netherlands
| | - CoDaP InvestigatorsHeijnenNanon F.L.oMulderMark M.G.oKoelmannMarceloBelsJulia L.M.oWilmesNickoHendriksCharlotte W.E.oJanssenEmma B.N.J.oFlorackMicheline C.D.M.oyGhossein-DohaChahindaoqvan der WoudeMeta C.E.yBormans-RussellLaurayPierletNoëllaabGoethuysBenabBruggenJonasabVermeirenGillesabVervloessemHendrikabBoerWillemabDepartment of Intensive Care Medicine, Maastricht University Medical Centre + (Maastricht UMC+), Maastricht, The NetherlandsCardiovascular Research Institute Maastricht (CARIM), Maastricht, The NetherlandsDepartment of Intensive Care Medicine, Zuyderland Medisch Centrum, Heerlen/Sittard, The NetherlandsDepartment of Intensive Care Medicine, Ziekenhuis Oost-Limburg, Genk, Belgium
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20
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Yen HK, Chiang H. Letter to the Editor: CORR Synthesis: When Should We Be Skeptical of Clinical Prediction Models? Clin Orthop Relat Res 2022; 480:2271-2273. [PMID: 36083689 PMCID: PMC9556068 DOI: 10.1097/corr.0000000000002395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 08/16/2022] [Indexed: 01/31/2023]
Affiliation(s)
- Hung-Kuan Yen
- Department of Orthopaedic Surgery, National Taiwan University Hospital, Hsin-Chu Branch, Hsin-Chu City, Taiwan
- Department of Medical Education, National Taiwan University Hospital, Hsin-Chu Branch, Hsin-Chu City, Taiwan
- Department of Orthopaedic Surgery, National Taiwan University Hospital, Taipei City, Taiwan
| | - Hongsen Chiang
- Department of Orthopaedic Surgery, National Taiwan University Hospital, Taipei City, Taiwan
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21
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Madhvani K, Garcia SF, Fernandez-Felix BM, Zamora J, Carpenter T, Khan KS. Predicting major complications in patients undergoing laparoscopic and open hysterectomy for benign indications. CMAJ 2022; 194:E1306-E1317. [PMID: 36191941 PMCID: PMC9529570 DOI: 10.1503/cmaj.220914] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/04/2022] [Indexed: 11/05/2022] Open
Abstract
BACKGROUND Hysterectomy, the most common gynecological operation, requires surgeons to counsel women about their operative risks. We aimed to develop and validate multivariable logistic regression models to predict major complications of laparoscopic or abdominal hysterectomy for benign conditions. METHODS We obtained routinely collected health administrative data from the English National Health Service (NHS) from 2011 to 2018. We defined major complications based on core outcomes for postoperative complications including ureteric, gastrointestinal and vascular injury, and wound complications. We specified 11 predictors a priori. We used internal-external cross-validation to evaluate discrimination and calibration across 7 NHS regions in the development cohort. We validated the final models using data from an additional NHS region. RESULTS We found that major complications occurred in 4.4% (3037/68 599) of laparoscopic and 4.9% (6201/125 971) of abdominal hysterectomies. Our models showed consistent discrimination in the development cohort (laparoscopic, C-statistic 0.61, 95% confidence interval [CI] 0.60 to 0.62; abdominal, C-statistic 0.67, 95% CI 0.64 to 0.70) and similar or better discrimination in the validation cohort (laparoscopic, C-statistic 0.67, 95% CI 0.65 to 0.69; abdominal, C-statistic 0.67, 95% CI 0.65 to 0.69). Adhesions were most predictive of complications in both models (laparoscopic, odds ratio [OR] 1.92, 95% CI 1.73 to 2.13; abdominal, OR 2.46, 95% CI 2.27 to 2.66). Other factors predictive of complications included adenomyosis in the laparoscopic model, and Asian ethnicity and diabetes in the abdominal model. Protective factors included age and diagnoses of menstrual disorders or benign adnexal mass in both models and diagnosis of fibroids in the abdominal model. INTERPRETATION Personalized risk estimates from these models, which showed moderate discrimination, can inform clinical decision-making for people with benign conditions who may require hysterectomy.
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Affiliation(s)
- Krupa Madhvani
- Barts and the London School of Medicine and Dentistry (Madhvani), Queen Mary University of London, London, UK; University Hospitals Dorset (Carpenter), NHS Foundation Trust, UK; Clinical Biostatistics Unit, Hospital Ramón y Cajal (IRYCIS) (Fernandez Garcia, Fernandez-Felix, Zamora); CIBER Epidemiology and Public Health (Fernandez-Felix, Zamora, Khan), Madrid, Spain; WHO Collaborating Centre for Global Women's Health (Zamora), Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK; Department of Preventative Medicine and Public Health (Khan), Faculty of Medicine, University of Granada, Spain
| | - Silvia Fernandez Garcia
- Barts and the London School of Medicine and Dentistry (Madhvani), Queen Mary University of London, London, UK; University Hospitals Dorset (Carpenter), NHS Foundation Trust, UK; Clinical Biostatistics Unit, Hospital Ramón y Cajal (IRYCIS) (Fernandez Garcia, Fernandez-Felix, Zamora); CIBER Epidemiology and Public Health (Fernandez-Felix, Zamora, Khan), Madrid, Spain; WHO Collaborating Centre for Global Women's Health (Zamora), Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK; Department of Preventative Medicine and Public Health (Khan), Faculty of Medicine, University of Granada, Spain
| | - Borja M Fernandez-Felix
- Barts and the London School of Medicine and Dentistry (Madhvani), Queen Mary University of London, London, UK; University Hospitals Dorset (Carpenter), NHS Foundation Trust, UK; Clinical Biostatistics Unit, Hospital Ramón y Cajal (IRYCIS) (Fernandez Garcia, Fernandez-Felix, Zamora); CIBER Epidemiology and Public Health (Fernandez-Felix, Zamora, Khan), Madrid, Spain; WHO Collaborating Centre for Global Women's Health (Zamora), Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK; Department of Preventative Medicine and Public Health (Khan), Faculty of Medicine, University of Granada, Spain
| | - Javier Zamora
- Barts and the London School of Medicine and Dentistry (Madhvani), Queen Mary University of London, London, UK; University Hospitals Dorset (Carpenter), NHS Foundation Trust, UK; Clinical Biostatistics Unit, Hospital Ramón y Cajal (IRYCIS) (Fernandez Garcia, Fernandez-Felix, Zamora); CIBER Epidemiology and Public Health (Fernandez-Felix, Zamora, Khan), Madrid, Spain; WHO Collaborating Centre for Global Women's Health (Zamora), Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK; Department of Preventative Medicine and Public Health (Khan), Faculty of Medicine, University of Granada, Spain
| | - Tyrone Carpenter
- Barts and the London School of Medicine and Dentistry (Madhvani), Queen Mary University of London, London, UK; University Hospitals Dorset (Carpenter), NHS Foundation Trust, UK; Clinical Biostatistics Unit, Hospital Ramón y Cajal (IRYCIS) (Fernandez Garcia, Fernandez-Felix, Zamora); CIBER Epidemiology and Public Health (Fernandez-Felix, Zamora, Khan), Madrid, Spain; WHO Collaborating Centre for Global Women's Health (Zamora), Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK; Department of Preventative Medicine and Public Health (Khan), Faculty of Medicine, University of Granada, Spain
| | - Khalid S Khan
- Barts and the London School of Medicine and Dentistry (Madhvani), Queen Mary University of London, London, UK; University Hospitals Dorset (Carpenter), NHS Foundation Trust, UK; Clinical Biostatistics Unit, Hospital Ramón y Cajal (IRYCIS) (Fernandez Garcia, Fernandez-Felix, Zamora); CIBER Epidemiology and Public Health (Fernandez-Felix, Zamora, Khan), Madrid, Spain; WHO Collaborating Centre for Global Women's Health (Zamora), Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK; Department of Preventative Medicine and Public Health (Khan), Faculty of Medicine, University of Granada, Spain
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22
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Sheyn D, Gregory WT, Osazuwa-Peters O, Jelovsek JE. Development and Validation of a Model for Predicting Surgical Site Infection After Pelvic Organ Prolapse Surgery. Female Pelvic Med Reconstr Surg 2022; 28:658-666. [PMID: 35830590 PMCID: PMC9590370 DOI: 10.1097/spv.0000000000001222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
IMPORTANCE Surgical site infection (SSI) is a common and costly complication. Targeted interventions in high-risk patients may lead to a reduction in SSI; at present, there is no method to consistently identify patients at increased risk of SSI. OBJECTIVE The aim of this study was to develop and validate a model for predicting risk of SSI after pelvic organ prolapse surgery. STUDY DESIGN Women undergoing surgery between 2011 and 2017 were identified using Current Procedural Terminology codes from the Centers for Medicare and Medicaid Services 5% Limited Data Set. Surgical site infection ≤90 days of surgery was the primary outcome, with 41 candidate predictors identified, including demographics, comorbidities, and perioperative variables. Generalized linear regression was used to fit a full specified model, including all predictors and a reduced penalized model approximating the full model. Model performance was measured using the c-statistic, Brier score, and calibration curves. Accuracy measures were internally validated using bootstrapping to correct for bias and overfitting. Decision curves were used to determine the net benefit of using the model. RESULTS Of 12,334 women, 4.7% experienced SSI. The approximated model included 10 predictors. Model accuracy was acceptable (bias-corrected c-statistic [95% confidence interval], 0.603 [0.578-0.624]; Brier score, 0.045). The model was moderately calibrated when predicting up to 5-6 times the average risk of SSI between 0 and 25-30%. There was a net benefit for clinical use when risk thresholds for intervention were between 3% and 12%. CONCLUSIONS This model provides estimates of probability of SSI within 90 days after pelvic organ prolapse surgery and demonstrates net benefit when considering prevention strategies to reduce SSI.
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Affiliation(s)
- David Sheyn
- Urology Institute, Division of Female Pelvic Medicine and Reconstructive Surgery, University Hospitals Cleveland, Cleveland OH
| | - W. Thomas Gregory
- Department of Obstetrics and Gynecology, Division of Female Pelvic Medicine and Reconstructive Surgery, Oregon Health & Science University, Portland, OR
| | | | - J. Eric Jelovsek
- Department of Obstetrics and Gynecology, Division of Urogynecology, Duke University School of Medicine, Durham, NC
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23
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van Bentem K, van der Hoorn ML, van Lith J, le Cessie S, Lashley E. Development of hypertensive complications in oocyte donation pregnancy: protocol for a systematic review and individual participant data meta-analysis (DONOR IPD). BMJ Open 2022; 12:e059594. [PMID: 35851011 PMCID: PMC9297201 DOI: 10.1136/bmjopen-2021-059594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
INTRODUCTION The assisted reproductive technique of oocyte donation (OD) is comparable to in vitro fertilisation (IVF), with the distinction of using a donated oocyte and thus involving two women. Compared with IVF and naturally conceived (NC) pregnancies, OD pregnancies have a higher risk for pregnancy complications as pregnancy-induced hypertension (PIH) and pre-eclampsia (PE). Various covariates among women pregnant by OD, however, also contribute to an increased risk for developing hypertensive complications. Therefore, we will conduct the DONation of Oocytes in Reproduction individual participant data (DONOR IPD) meta-analysis to determine the risk for the development of hypertensive complications in OD pregnancy, in comparison to autologous oocyte pregnancy (non-donor IVF/intracytoplasmic sperm injection (ICSI) and NC pregnancy). The DONOR IPD meta-analysis will provide an opportunity to adjust for confounders and perform subgroup analyses. Furthermore, IPD will be used to externally validate a prediction model for the development of PE in OD pregnancy. METHODS AND ANALYSIS A systematic literature search will be performed to search for studies that included women pregnant by OD, and documented on hypertensive complications in OD pregnancy. The authors from each study will be asked to collaborate and share IPD. Using the pseudoanonymised combined IPD, we will perform statistical analyses with one-stage and two-stage approaches, subgroup analyses and possibly time-to-event analyses to investigate the risk of developing hypertensive complications in OD pregnancy. Furthermore, we will formally assess a prediction model on its performance in an external validation with the use of IPD. ETHICS AND DISSEMINATION Ethical approval and individual patient consent will not be required in most cases since this IPD meta-analysis will use existing pseudoanonymised data from cohort studies. Results will be disseminated through peer-reviewed journals and international conferences. PROSPERO REGISTRATION NUMBER CRD42021267908.
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Affiliation(s)
- Kim van Bentem
- Gynecology and Obstetrics, Leiden University Medical Center, Leiden, The Netherlands
| | | | - Jan van Lith
- Gynecology and Obstetrics, Leiden University Medical Center, Leiden, The Netherlands
| | - Saskia le Cessie
- Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Eileen Lashley
- Gynecology and Obstetrics, Leiden University Medical Center, Leiden, The Netherlands
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24
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Lee KK, Doudesis D, Anwar M, Astengo F, Chenevier-Gobeaux C, Claessens YE, Wussler D, Kozhuharov N, Strebel I, Sabti Z, deFilippi C, Seliger S, Moe G, Fernando C, Bayes-Genis A, van Kimmenade RRJ, Pinto Y, Gaggin HK, Wiemer JC, Möckel M, Rutten JHW, van den Meiracker AH, Gargani L, Pugliese NR, Pemberton C, Ibrahim I, Gegenhuber A, Mueller T, Neumaier M, Behnes M, Akin I, Bombelli M, Grassi G, Nazerian P, Albano G, Bahrmann P, Newby DE, Japp AG, Tsanas A, Shah ASV, Richards AM, McMurray JJV, Mueller C, Januzzi JL, Mills NL. Development and validation of a decision support tool for the diagnosis of acute heart failure: systematic review, meta-analysis, and modelling study. BMJ 2022; 377:e068424. [PMID: 35697365 PMCID: PMC9189738 DOI: 10.1136/bmj-2021-068424] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/25/2012] [Indexed: 11/03/2022]
Abstract
OBJECTIVES To evaluate the diagnostic performance of N-terminal pro-B-type natriuretic peptide (NT-proBNP) thresholds for acute heart failure and to develop and validate a decision support tool that combines NT-proBNP concentrations with clinical characteristics. DESIGN Individual patient level data meta-analysis and modelling study. SETTING Fourteen studies from 13 countries, including randomised controlled trials and prospective observational studies. PARTICIPANTS Individual patient level data for 10 369 patients with suspected acute heart failure were pooled for the meta-analysis to evaluate NT-proBNP thresholds. A decision support tool (Collaboration for the Diagnosis and Evaluation of Heart Failure (CoDE-HF)) that combines NT-proBNP with clinical variables to report the probability of acute heart failure for an individual patient was developed and validated. MAIN OUTCOME MEASURE Adjudicated diagnosis of acute heart failure. RESULTS Overall, 43.9% (4549/10 369) of patients had an adjudicated diagnosis of acute heart failure (73.3% (2286/3119) and 29.0% (1802/6208) in those with and without previous heart failure, respectively). The negative predictive value of the guideline recommended rule-out threshold of 300 pg/mL was 94.6% (95% confidence interval 91.9% to 96.4%); despite use of age specific rule-in thresholds, the positive predictive value varied at 61.0% (55.3% to 66.4%), 73.5% (62.3% to 82.3%), and 80.2% (70.9% to 87.1%), in patients aged <50 years, 50-75 years, and >75 years, respectively. Performance varied in most subgroups, particularly patients with obesity, renal impairment, or previous heart failure. CoDE-HF was well calibrated, with excellent discrimination in patients with and without previous heart failure (area under the receiver operator curve 0.846 (0.830 to 0.862) and 0.925 (0.919 to 0.932) and Brier scores of 0.130 and 0.099, respectively). In patients without previous heart failure, the diagnostic performance was consistent across all subgroups, with 40.3% (2502/6208) identified at low probability (negative predictive value of 98.6%, 97.8% to 99.1%) and 28.0% (1737/6208) at high probability (positive predictive value of 75.0%, 65.7% to 82.5%) of having acute heart failure. CONCLUSIONS In an international, collaborative evaluation of the diagnostic performance of NT-proBNP, guideline recommended thresholds to diagnose acute heart failure varied substantially in important patient subgroups. The CoDE-HF decision support tool incorporating NT-proBNP as a continuous measure and other clinical variables provides a more consistent, accurate, and individualised approach. STUDY REGISTRATION PROSPERO CRD42019159407.
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Affiliation(s)
- Kuan Ken Lee
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
- Contributed equally
| | - Dimitrios Doudesis
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
- Usher Institute, University of Edinburgh, Edinburgh, UK
- Contributed equally
| | - Mohamed Anwar
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
- Contributed equally
| | - Federica Astengo
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | | | - Yann-Erick Claessens
- Department of Emergency Medicine, Princess Grace Hospital Center, Monaco, Principality of Monaco
| | - Desiree Wussler
- Cardiovascular Research Institute of Basel, Department of Cardiology, University Hospital Basel, Basel, Switzerland
- Department of Internal Medicine, University Hospital Basel, University of Basel, Switzerland
| | - Nikola Kozhuharov
- Cardiovascular Research Institute of Basel, Department of Cardiology, University Hospital Basel, Basel, Switzerland
- Liverpool Heart and Chest Hospital, Liverpool, UK
| | - Ivo Strebel
- Cardiovascular Research Institute of Basel, Department of Cardiology, University Hospital Basel, Basel, Switzerland
| | - Zaid Sabti
- Cardiovascular Research Institute of Basel, Department of Cardiology, University Hospital Basel, Basel, Switzerland
| | | | - Stephen Seliger
- Division of Nephrology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Gordon Moe
- University of Toronto, St Michael's Hospital, Toronto, ON, Canada
| | - Carlos Fernando
- University of Toronto, St Michael's Hospital, Toronto, ON, Canada
| | - Antoni Bayes-Genis
- Heart Institute, Hospital Universitari Germans Trias i Pujol, Badalona, CIBERCV, Spain
| | | | - Yigal Pinto
- University of Amsterdam, Amsterdam, Netherlands
| | - Hanna K Gaggin
- Harvard Medical School, Boston, MA, USA
- Division of Cardiology, Massachusetts General Hospital, Boston, MA, USA
| | - Jan C Wiemer
- BRAHMS, Thermo Fisher Scientific, Hennigsdorf, Germany
| | - Martin Möckel
- Department of Emergency and Acute Medicine with Chest Pain Units, Charité - Universitätsmedizin Berlin, Campus Mitte and Virchow, Berlin, Germany
| | - Joost H W Rutten
- Department of Internal Medicine, Radboud University Medical Center, Nijmegen, Netherlands
| | - Anton H van den Meiracker
- Department of Internal Medicine, Division of Pharmacology and Vascular Medicine, Erasmus Medical Center, Rotterdam, Netherlands
| | - Luna Gargani
- Department of Surgical, Medical and Molecular Pathology and Critical Care Medicine, University of Pisa, Pisa, Italy
| | - Nicola R Pugliese
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | | | - Irwani Ibrahim
- Emergency Medicine Department, National University Hospital, Singapore
| | - Alfons Gegenhuber
- Department of Internal Medicine, Krankenhaus Bad Ischl, Bad Ischl, Austria
| | - Thomas Mueller
- Department of Laboratory Medicine, Hospital Voecklabruck, Voecklabruck, Austria
| | - Michael Neumaier
- Institute for Clinical Chemistry, University Medical Centre Mannheim, Faculty of Medicine Mannheim, University of Heidelberg, Mannheim, Germany
| | - Michael Behnes
- First Department of Medicine, University Medical Centre Mannheim, Faculty of Medicine Mannheim, University of Heidelberg, Mannheim, Germany
| | - Ibrahim Akin
- First Department of Medicine, University Medical Centre Mannheim, Faculty of Medicine Mannheim, University of Heidelberg, Mannheim, Germany
| | - Michele Bombelli
- University of Milan Bicocca, ASST-Brianza, Pio XI Hospital of Desio, Internal Medicine, Desio, Italy
| | - Guido Grassi
- Clinica Medica, University Milan Bicocca, Milan, Italy
| | - Peiman Nazerian
- Department of Emergency Medicine, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | - Giovanni Albano
- Department of Emergency Medicine, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | - Philipp Bahrmann
- Department of Internal Medicine III, Division of Cardiology, University Hospital of Heidelberg, Ruprecht-Karls University Heidelberg, Heidelberg, Germany
| | - David E Newby
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - Alan G Japp
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | | | - Anoop S V Shah
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
- London School of Hygiene and Tropical Medicine, London, UK
| | - A Mark Richards
- Christchurch Heart Institute, University of Otago, Christchurch, New Zealand
- Cardiovascular Research Institute, National University Heart Centre Singapore, Singapore
| | - John J V McMurray
- British Heart Foundation Cardiovascular Research Centre, University of Glasgow, Glasgow, UK
| | - Christian Mueller
- Cardiovascular Research Institute of Basel, Department of Cardiology, University Hospital Basel, Basel, Switzerland
| | - James L Januzzi
- Harvard Medical School, Boston, MA, USA
- Division of Cardiology, Massachusetts General Hospital, Boston, MA, USA
| | - Nicholas L Mills
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
- Usher Institute, University of Edinburgh, Edinburgh, UK
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25
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Knight SR, Gupta RK, Ho A, Pius R, Buchan I, Carson G, Drake TM, Dunning J, Fairfield CJ, Gamble C, Green CA, Halpin S, Hardwick HE, Holden KA, Horby PW, Jackson C, Mclean KA, Merson L, Nguyen-Van-Tam JS, Norman L, Olliaro PL, Pritchard MG, Russell CD, Shaw CA, Sheikh A, Solomon T, Sudlow C, Swann OV, Turtle LCW, Openshaw PJM, Baillie JK, Docherty A, Semple MG, Noursadeghi M, Harrison EM. Prospective validation of the 4C prognostic models for adults hospitalised with COVID-19 using the ISARIC WHO Clinical Characterisation Protocol. Thorax 2022; 77:606-615. [PMID: 34810237 PMCID: PMC8610617 DOI: 10.1136/thoraxjnl-2021-217629] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 10/11/2021] [Indexed: 01/08/2023]
Abstract
PURPOSE To prospectively validate two risk scores to predict mortality (4C Mortality) and in-hospital deterioration (4C Deterioration) among adults hospitalised with COVID-19. METHODS Prospective observational cohort study of adults (age ≥18 years) with confirmed or highly suspected COVID-19 recruited into the International Severe Acute Respiratory and emerging Infections Consortium (ISARIC) WHO Clinical Characterisation Protocol UK (CCP-UK) study in 306 hospitals across England, Scotland and Wales. Patients were recruited between 27 August 2020 and 17 February 2021, with at least 4 weeks follow-up before final data extraction. The main outcome measures were discrimination and calibration of models for in-hospital deterioration (defined as any requirement of ventilatory support or critical care, or death) and mortality, incorporating predefined subgroups. RESULTS 76 588 participants were included, of whom 27 352 (37.4%) deteriorated and 12 581 (17.4%) died. Both the 4C Mortality (0.78 (0.77 to 0.78)) and 4C Deterioration scores (pooled C-statistic 0.76 (95% CI 0.75 to 0.77)) demonstrated consistent discrimination across all nine National Health Service regions, with similar performance metrics to the original validation cohorts. Calibration remained stable (4C Mortality: pooled slope 1.09, pooled calibration-in-the-large 0.12; 4C Deterioration: 1.00, -0.04), with no need for temporal recalibration during the second UK pandemic wave of hospital admissions. CONCLUSION Both 4C risk stratification models demonstrate consistent performance to predict clinical deterioration and mortality in a large prospective second wave validation cohort of UK patients. Despite recent advances in the treatment and management of adults hospitalised with COVID-19, both scores can continue to inform clinical decision making. TRIAL REGISTRATION NUMBER ISRCTN66726260.
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Affiliation(s)
- Stephen R Knight
- Centre for Medical Informatics, The University of Edinburgh, Usher Institute of Population Health Sciences and Informatics, Edinburgh, UK
| | - Rishi K Gupta
- University College London Institute for Global Health, London, UK
| | - Antonia Ho
- Medical Research Council University of Glasgow Centre for Virus Research, Glasgow, UK
| | - Riinu Pius
- Centre for Medical Informatics, The University of Edinburgh, Usher Institute of Population Health Sciences and Informatics, Edinburgh, UK
| | - Iain Buchan
- Manchester Academic Health Science Centre, Manchester, UK
- Department of Public Health and Policy, University of Liverpool, Liverpool, UK
| | - Gail Carson
- Nuffield Department of Clinical Medicine, ISARIC Global Support Centre, Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
| | - Thomas M Drake
- Centre for Medical Informatics, The University of Edinburgh, Usher Institute of Population Health Sciences and Informatics, Edinburgh, UK
| | - Jake Dunning
- Public Health England National Infection Service, Salisbury, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Cameron J Fairfield
- Centre for Medical Informatics, The University of Edinburgh, Usher Institute of Population Health Sciences and Informatics, Edinburgh, UK
| | - Carrol Gamble
- Liverpool Clinical Trials Centre, University of Liverpool, Liverpool, UK
| | - Christopher A Green
- Institute of Microbiology and Infection, University of Birmingham, Birmingham, UK
| | - Sophie Halpin
- Liverpool Clinical Trials Centre, University of Liverpool, Liverpool, UK
| | - Hayley E Hardwick
- NIHR Health Protection Research Unit, Institute of Infection, Veterinary and Ecological Sciences, Faculty of Health and Life Sciences, University of Liverpool, Liverpool, UK
| | - Karl A Holden
- NIHR Health Protection Research Unit, Institute of Infection, Veterinary and Ecological Sciences, Faculty of Health and Life Sciences, University of Liverpool, Liverpool, UK
| | - Peter W Horby
- Nuffield Department of Clinical Medicine, ISARIC Global Support Centre, Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
| | - Clare Jackson
- Liverpool Clinical Trials Centre, University of Liverpool, Liverpool, UK
| | - Kenneth A Mclean
- Centre for Medical Informatics, The University of Edinburgh, Usher Institute of Population Health Sciences and Informatics, Edinburgh, UK
| | - Laura Merson
- Nuffield Department of Clinical Medicine, ISARIC Global Support Centre, Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
| | | | - Lisa Norman
- Centre for Medical Informatics, The University of Edinburgh, Usher Institute of Population Health Sciences and Informatics, Edinburgh, UK
| | - Piero L Olliaro
- Nuffield Department of Medicine, Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
| | - Mark G Pritchard
- Nuffield Department of Medicine, Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
| | - Clark D Russell
- Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK
| | - Catherine A Shaw
- Centre for Medical Informatics, The University of Edinburgh, Usher Institute of Population Health Sciences and Informatics, Edinburgh, UK
| | - Aziz Sheikh
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Tom Solomon
- NIHR Health Protection Research Unit, Institute of Infection, Veterinary and Ecological Sciences, Faculty of Health and Life Sciences, University of Liverpool, Liverpool, UK
| | | | - Olivia V Swann
- Department of Child Life and Health, University of Edinburgh, Edinburgh, UK
| | - Lance C W Turtle
- Clinical Infection, Microbiology and Immunology, University of Liverpool Faculty of Health and Life Sciences, Liverpool, UK
- Liverpool University Hospitals Foundation Trust, Member of Liverpool Health Partners, Liverpool, UK
| | | | - J Kenneth Baillie
- Genetics and Genomics, Roslin Institute, University of Edinburgh, Edinburgh, UK
| | - Annemarie Docherty
- Centre for Medical Informatics, The University of Edinburgh, Usher Institute of Population Health Sciences and Informatics, Edinburgh, UK
| | - Malcolm G Semple
- NIHR Health Protection Research Unit, Institute of Infection, Veterinary and Ecological Sciences, Faculty of Health and Life Sciences, University of Liverpool, Liverpool, UK
- Respiratory Medicine, Alder Hey Children's Hospital, University of Liverpool, Liverpool, UK
| | - Mahdad Noursadeghi
- Division of Infection and Immunity, University College London, London, UK
| | - Ewen M Harrison
- Centre for Medical Informatics, The University of Edinburgh, Usher Institute of Population Health Sciences and Informatics, Edinburgh, UK
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26
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Housseine N, Rijken MJ, Weller K, Nassor NH, Gbenga K, Dodd C, Debray T, Meguid T, Franx A, Grobbee DE, Browne JL. Development of a clinical prediction model for perinatal deaths in low resource settings. EClinicalMedicine 2022; 44:101288. [PMID: 35252826 PMCID: PMC8888338 DOI: 10.1016/j.eclinm.2022.101288] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 12/19/2021] [Accepted: 01/17/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Most pregnancy-related deaths in low and middle income countries occur around the time of birth and are avoidable with timely care. This study aimed to develop a prognostic model to identify women at risk of intrapartum-related perinatal deaths in low-resourced settings, by (1) external validation of an existing prediction model, and subsequently (2) development of a novel model. METHODS A prospective cohort study was conducted among pregnant women who presented consecutively for delivery at the maternity unit of Zanzibar's tertiary hospital, Mnazi Mmoja Hospital, the Republic of Tanzania between October 2017 and May 2018. Candidate predictors of perinatal deaths included maternal and foetal characteristics obtained from routine history and physical examination at the time of admission to the labour ward. The outcomes were intrapartum stillbirths and neonatal death before hospital discharge. An existing stillbirth prediction model with six predictors from Nigeria was applied to the Zanzibar cohort to assess its discrimination and calibration performance. Subsequently, a new prediction model was developed using multivariable logistic regression. Model performance was evaluated through internal validation and corrected for overfitting using bootstrapping methods. FINDINGS 5747 mother-baby pairs were analysed. The existing model showed poor discrimination performance (c-statistic 0·57). The new model included 15 clinical predictors and showed promising discriminative and calibration performance after internal validation (optimism adjusted c-statistic of 0·78, optimism adjusted calibration slope =0·94). INTERPRETATION The new model consisted of predictors easily obtained through history-taking and physical examination at the time of admission to the labour ward. It had good performance in predicting risk of perinatal death in women admitted in labour wards. Therefore, it has the potential to assist skilled birth attendance to triage women for appropriate management during labour. Before routine implementation, external validation and usefulness should be determined in future studies. FUNDING The study received funding from Laerdal Foundation, Otto Kranendonk Fund and UMC Global Health Fellowship. TD acknowledges financial support from the Netherlands Organisation for Health Research and Development (grant 91617050).
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Affiliation(s)
- Natasha Housseine
- Julius Global Health, Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, The Netherlands
- Division of Woman and Baby, University Medical Centre Utrecht, The Netherlands
- Department of Obstetrics and Gynaecology, Mnazi Mmoja Hospital, Zanzibar, Tanzania
- Corresponding author: Natasha Housseine, Julius Center for Health Sciences and Primary Care, UMC Utrecht, Postal address: Huispost nr 1. STR 6.131, P.O. Box 85500, 3508 GA Utrecht, The Netherlands, Telephone number: +255 745 338950.
| | - Marcus J Rijken
- Julius Global Health, Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, The Netherlands
- Division of Woman and Baby, University Medical Centre Utrecht, The Netherlands
| | - Katinka Weller
- Department of Obstetrics and Gynaecology, Erasmus MC University Medical Centre Rotterdam, The Netherlands
| | | | - Kayode Gbenga
- Julius Global Health, Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, The Netherlands
| | - Caitlin Dodd
- Julius Global Health, Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, The Netherlands
| | - Thomas Debray
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, The Netherlands
| | - Tarek Meguid
- Department of Obstetrics and Gynaecology, Mnazi Mmoja Hospital, Zanzibar, Tanzania
- School of Health and Medical Sciences, State University of Zanzibar
- Village Health Works, Kigutu, Burundi
| | - Arie Franx
- Department of Obstetrics and Gynaecology, Erasmus MC University Medical Centre Rotterdam, The Netherlands
| | - Diederick E Grobbee
- Julius Global Health, Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, The Netherlands
| | - Joyce L Browne
- Julius Global Health, Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, The Netherlands
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27
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Allotey J, Whittle R, Snell KIE, Smuk M, Townsend R, von Dadelszen P, Heazell AEP, Magee L, Smith GCS, Sandall J, Thilaganathan B, Zamora J, Riley RD, Khalil A, Thangaratinam S. External validation of prognostic models to predict stillbirth using International Prediction of Pregnancy Complications (IPPIC) Network database: individual participant data meta-analysis. ULTRASOUND IN OBSTETRICS & GYNECOLOGY : THE OFFICIAL JOURNAL OF THE INTERNATIONAL SOCIETY OF ULTRASOUND IN OBSTETRICS AND GYNECOLOGY 2022; 59:209-219. [PMID: 34405928 DOI: 10.1002/uog.23757] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 06/30/2021] [Accepted: 08/02/2021] [Indexed: 06/13/2023]
Abstract
OBJECTIVE Stillbirth is a potentially preventable complication of pregnancy. Identifying women at high risk of stillbirth can guide decisions on the need for closer surveillance and timing of delivery in order to prevent fetal death. Prognostic models have been developed to predict the risk of stillbirth, but none has yet been validated externally. In this study, we externally validated published prediction models for stillbirth using individual participant data (IPD) meta-analysis to assess their predictive performance. METHODS MEDLINE, EMBASE, DH-DATA and AMED databases were searched from inception to December 2020 to identify studies reporting stillbirth prediction models. Studies that developed or updated prediction models for stillbirth for use at any time during pregnancy were included. IPD from cohorts within the International Prediction of Pregnancy Complications (IPPIC) Network were used to validate externally the identified prediction models whose individual variables were available in the IPD. The risk of bias of the models and cohorts was assessed using the Prediction study Risk Of Bias ASsessment Tool (PROBAST). The discriminative performance of the models was evaluated using the C-statistic, and calibration was assessed using calibration plots, calibration slope and calibration-in-the-large. Performance measures were estimated separately in each cohort, as well as summarized across cohorts using random-effects meta-analysis. Clinical utility was assessed using net benefit. RESULTS Seventeen studies reporting the development of 40 prognostic models for stillbirth were identified. None of the models had been previously validated externally, and the full model equation was reported for only one-fifth (20%, 8/40) of the models. External validation was possible for three of these models, using IPD from 19 cohorts (491 201 pregnant women) within the IPPIC Network database. Based on evaluation of the model development studies, all three models had an overall high risk of bias, according to PROBAST. In the IPD meta-analysis, the models had summary C-statistics ranging from 0.53 to 0.65 and summary calibration slopes ranging from 0.40 to 0.88, with risk predictions that were generally too extreme compared with the observed risks. The models had little to no clinical utility, as assessed by net benefit. However, there remained uncertainty in the performance of some models due to small available sample sizes. CONCLUSIONS The three validated stillbirth prediction models showed generally poor and uncertain predictive performance in new data, with limited evidence to support their clinical application. The findings suggest methodological shortcomings in their development, including overfitting. Further research is needed to further validate these and other models, identify stronger prognostic factors and develop more robust prediction models. © 2021 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.
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Affiliation(s)
- J Allotey
- WHO Collaborating Centre for Global Women's Health, Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - R Whittle
- Centre for Prognosis Research, School of Medicine, Keele University, Keele, UK
| | - K I E Snell
- Centre for Prognosis Research, School of Medicine, Keele University, Keele, UK
| | - M Smuk
- Medical Statistics Department, London School of Hygiene and Tropical Medicine, London, UK
| | - R Townsend
- Fetal Medicine Unit, St George's University Hospitals NHS Foundation Trust, University of London, London, UK
- Vascular Biology Research Centre, Molecular and Clinical Sciences Research Institute, St George's University of London, London, UK
| | - P von Dadelszen
- Department of Women and Children's Health, School of Life Course Sciences, King's College London, London, UK
| | - A E P Heazell
- Maternal and Fetal Health Research Centre, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - L Magee
- Department of Women and Children's Health, School of Life Course Sciences, King's College London, London, UK
| | - G C S Smith
- Department of Obstetrics and Gynaecology, NIHR Biomedical Research Centre, Cambridge University, Cambridge, UK
| | - J Sandall
- Department of Women and Children's Health, School of Life Course Sciences, King's College London, London, UK
- Health Service and Population Research Department, Centre for Implementation Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - B Thilaganathan
- Fetal Medicine Unit, St George's University Hospitals NHS Foundation Trust, University of London, London, UK
- Vascular Biology Research Centre, Molecular and Clinical Sciences Research Institute, St George's University of London, London, UK
| | - J Zamora
- WHO Collaborating Centre for Global Women's Health, Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
- Clinical Biostatistics Unit, Hospital Universitario Ramón y Cajal (IRYCIS), Madrid, Spain
- CIBER Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - R D Riley
- Centre for Prognosis Research, School of Medicine, Keele University, Keele, UK
| | - A Khalil
- Fetal Medicine Unit, St George's University Hospitals NHS Foundation Trust, University of London, London, UK
- Vascular Biology Research Centre, Molecular and Clinical Sciences Research Institute, St George's University of London, London, UK
| | - S Thangaratinam
- WHO Collaborating Centre for Global Women's Health, Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
- Birmingham Women's and Children's NHS Foundation Trust, Birmingham, UK
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Collin CB, Gebhardt T, Golebiewski M, Karaderi T, Hillemanns M, Khan FM, Salehzadeh-Yazdi A, Kirschner M, Krobitsch S, Kuepfer L. Computational Models for Clinical Applications in Personalized Medicine—Guidelines and Recommendations for Data Integration and Model Validation. J Pers Med 2022; 12:jpm12020166. [PMID: 35207655 PMCID: PMC8879572 DOI: 10.3390/jpm12020166] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 01/14/2022] [Accepted: 01/20/2022] [Indexed: 12/12/2022] Open
Abstract
The future development of personalized medicine depends on a vast exchange of data from different sources, as well as harmonized integrative analysis of large-scale clinical health and sample data. Computational-modelling approaches play a key role in the analysis of the underlying molecular processes and pathways that characterize human biology, but they also lead to a more profound understanding of the mechanisms and factors that drive diseases; hence, they allow personalized treatment strategies that are guided by central clinical questions. However, despite the growing popularity of computational-modelling approaches in different stakeholder communities, there are still many hurdles to overcome for their clinical routine implementation in the future. Especially the integration of heterogeneous data from multiple sources and types are challenging tasks that require clear guidelines that also have to comply with high ethical and legal standards. Here, we discuss the most relevant computational models for personalized medicine in detail that can be considered as best-practice guidelines for application in clinical care. We define specific challenges and provide applicable guidelines and recommendations for study design, data acquisition, and operation as well as for model validation and clinical translation and other research areas.
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Affiliation(s)
- Catherine Bjerre Collin
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 N Copenhagen, Denmark; (C.B.C.); (T.K.)
| | - Tom Gebhardt
- Department of Systems Biology and Bioinformatics, University of Rostock, 18057 Rostock, Germany; (T.G.); (M.H.); (F.M.K.)
| | - Martin Golebiewski
- Heidelberg Institute for Theoretical Studies gGmbH, 69118 Heidelberg, Germany;
| | - Tugce Karaderi
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 N Copenhagen, Denmark; (C.B.C.); (T.K.)
- Center for Health Data Science, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 N Copenhagen, Denmark
| | - Maximilian Hillemanns
- Department of Systems Biology and Bioinformatics, University of Rostock, 18057 Rostock, Germany; (T.G.); (M.H.); (F.M.K.)
| | - Faiz Muhammad Khan
- Department of Systems Biology and Bioinformatics, University of Rostock, 18057 Rostock, Germany; (T.G.); (M.H.); (F.M.K.)
| | | | - Marc Kirschner
- Forschungszentrum Jülich GmbH, Project Management Jülich, 52425 Jülich, Germany; (M.K.); (S.K.)
| | - Sylvia Krobitsch
- Forschungszentrum Jülich GmbH, Project Management Jülich, 52425 Jülich, Germany; (M.K.); (S.K.)
| | | | - Lars Kuepfer
- Institute for Systems Medicine with Focus on Organ Interaction, University Hospital RWTH Aachen, 52074 Aachen, Germany
- Correspondence: ; Tel.: +49-241-8085900
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Geersing GJ, Takada T, Klok FA, Büller HR, Courtney DM, Freund Y, Galipienzo J, Le Gal G, Ghanima W, Kline JA, Huisman MV, Moons KGM, Perrier A, Parpia S, Robert-Ebadi H, Righini M, Roy PM, van Smeden M, Stals MAM, Wells PS, de Wit K, Kraaijpoel N, van Es N. Ruling out pulmonary embolism across different healthcare settings: A systematic review and individual patient data meta-analysis. PLoS Med 2022; 19:e1003905. [PMID: 35077453 PMCID: PMC8824365 DOI: 10.1371/journal.pmed.1003905] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 02/08/2022] [Accepted: 01/06/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND The challenging clinical dilemma of detecting pulmonary embolism (PE) in suspected patients is encountered in a variety of healthcare settings. We hypothesized that the optimal diagnostic approach to detect these patients in terms of safety and efficiency depends on underlying PE prevalence, case mix, and physician experience, overall reflected by the type of setting where patients are initially assessed. The objective of this study was to assess the capability of ruling out PE by available diagnostic strategies across all possible settings. METHODS AND FINDINGS We performed a literature search (MEDLINE) followed by an individual patient data (IPD) meta-analysis (MA; 23 studies), including patients from self-referral emergency care (n = 12,612), primary healthcare clinics (n = 3,174), referred secondary care (n = 17,052), and hospitalized or nursing home patients (n = 2,410). Multilevel logistic regression was performed to evaluate diagnostic performance of the Wells and revised Geneva rules, both using fixed and adapted D-dimer thresholds to age or pretest probability (PTP), for the YEARS algorithm and for the Pulmonary Embolism Rule-out Criteria (PERC). All strategies were tested separately in each healthcare setting. Following studies done in this field, the primary diagnostic metrices estimated from the models were the "failure rate" of each strategy-i.e., the proportion of missed PE among patients categorized as "PE excluded" and "efficiency"-defined as the proportion of patients categorized as "PE excluded" among all patients. In self-referral emergency care, the PERC algorithm excludes PE in 21% of suspected patients at a failure rate of 1.12% (95% confidence interval [CI] 0.74 to 1.70), whereas this increases to 6.01% (4.09 to 8.75) in referred patients to secondary care at an efficiency of 10%. In patients from primary healthcare and those referred to secondary care, strategies adjusting D-dimer to PTP are the most efficient (range: 43% to 62%) at a failure rate ranging between 0.25% and 3.06%, with higher failure rates observed in patients referred to secondary care. For this latter setting, strategies adjusting D-dimer to age are associated with a lower failure rate ranging between 0.65% and 0.81%, yet are also less efficient (range: 33% and 35%). For all strategies, failure rates are highest in hospitalized or nursing home patients, ranging between 1.68% and 5.13%, at an efficiency ranging between 15% and 30%. The main limitation of the primary analyses was that the diagnostic performance of each strategy was compared in different sets of studies since the availability of items used in each diagnostic strategy differed across included studies; however, sensitivity analyses suggested that the findings were robust. CONCLUSIONS The capability of safely and efficiently ruling out PE of available diagnostic strategies differs for different healthcare settings. The findings of this IPD MA help in determining the optimum diagnostic strategies for ruling out PE per healthcare setting, balancing the trade-off between failure rate and efficiency of each strategy.
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Affiliation(s)
- Geert-Jan Geersing
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
- * E-mail:
| | - Toshihiko Takada
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
- Department of General Medicine, Shirakawa Satellite for Teaching And Research (STAR), Fukushima Medical University, Fukushima, Japan
| | - Frederikus A. Klok
- Department of Medicine, Thrombosis and Haemostasis, Dutch Thrombosis Network, Leiden University Medical Center, Leiden, the Netherlands
| | - Harry R. Büller
- Department of Medicine, Amsterdam University Medical Center, Amsterdam Cardiovascular Sciences, Amsterdam, the Netherlands
| | - D. Mark Courtney
- Department of Emergency Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
| | - Yonathan Freund
- Sorbonne University, Emergency Department, Hôpital Pitié-Salpêtrière, Assistance Publique—Hôpitaux de Paris, Paris, France
| | - Javier Galipienzo
- Service of Anesthesiology, MD Anderson Cancer Center Madrid, Madrid, Spain
| | - Gregoire Le Gal
- Department of Medicine, University of Ottawa, Ottawa Hospital Research Institute, Ottawa, Canada
| | - Waleed Ghanima
- Department of Medicine, Østfold Hospital Trust, Norway and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Jeffrey A. Kline
- Department of Emergency Medicine, Wayne State School of Medicine, Detroit, Michigan, United States of America
| | - Menno V. Huisman
- Department of Medicine, Thrombosis and Haemostasis, Dutch Thrombosis Network, Leiden University Medical Center, Leiden, the Netherlands
| | - Karel G. M. Moons
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
- Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Arnaud Perrier
- Division of Angiology and Hemostasis, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland
| | - Sameer Parpia
- Department of Oncology, McMaster University, Hamilton, Canada
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada
| | - Helia Robert-Ebadi
- Division of Angiology and Hemostasis, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland
| | - Marc Righini
- Division of Angiology and Hemostasis, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland
| | - Pierre-Marie Roy
- UNIV Angers, UMR (CNRS 6015—INSERM 1083) and CHU Angers, Department of Emergency Medicine, F-CRIN InnoVTE, Angers, France
| | - Maarten van Smeden
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Milou A. M. Stals
- Department of Medicine, Thrombosis and Haemostasis, Dutch Thrombosis Network, Leiden University Medical Center, Leiden, the Netherlands
| | - Philip S. Wells
- Department of Medicine, University of Ottawa, Ottawa Hospital Research Institute, Ottawa, Canada
| | - Kerstin de Wit
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada
- Department of Emergency Medicine, Queen’s University, Kingston, Canada
| | - Noémie Kraaijpoel
- Department of Medicine, Amsterdam University Medical Center, Amsterdam Cardiovascular Sciences, Amsterdam, the Netherlands
| | - Nick van Es
- Department of Medicine, Amsterdam University Medical Center, Amsterdam Cardiovascular Sciences, Amsterdam, the Netherlands
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Tamási B, Crowther M, Puhan MA, Steyerberg EW, Hothorn T. Individual participant data meta-analysis with mixed-effects transformation models. Biostatistics 2021; 23:1083-1098. [PMID: 34969073 PMCID: PMC9566326 DOI: 10.1093/biostatistics/kxab045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 11/03/2021] [Accepted: 11/22/2021] [Indexed: 11/23/2022] Open
Abstract
One-stage meta-analysis of individual participant data (IPD) poses several statistical and computational challenges. For time-to-event outcomes, the approach requires the estimation of complicated nonlinear mixed-effects models that are flexible enough to realistically capture the most important characteristics of the IPD. We present a model class that incorporates general normally distributed random effects into linear transformation models. We discuss extensions to model between-study heterogeneity in baseline risks and covariate effects and also relax the assumption of proportional hazards. Within the proposed framework, data with arbitrary random censoring patterns can be handled. The accompanying \documentclass[12pt]{minimal}
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}{}$\textsf{R}$\end{document} package tramME utilizes the Laplace approximation and automatic differentiation to perform efficient maximum likelihood estimation and inference in mixed-effects transformation models. We compare several variants of our model to predict the survival of patients with chronic obstructive pulmonary disease using a large data set of prognostic studies. Finally, a simulation study is presented that verifies the correctness of the implementation and highlights its efficiency compared to an alternative approach.
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Affiliation(s)
- Bálint Tamási
- Institut für Epidemiologie, Biostatistik und Prävention, Departement Biostatistik, Universität Zürich, Hirschengraben 84, CH-8001 Zürich, Switzerland
| | - Michael Crowther
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 171 77 Stockholm, Sweden
| | - Milo Alan Puhan
- Institut für Epidemiologie, Biostatistik und Prävention, Departement Epidemiologie, Universität Zürich, Hirschengraben 84, CH-8001 Zürich, Switzerland
| | - Ewout W Steyerberg
- Department of Biomedical Data Sciences, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, the Netherlands
| | - Torsten Hothorn
- Institut für Epidemiologie, Biostatistik und Prävention, Departement Biostatistik, Universität Zürich, Hirschengraben 84, CH-8001 Zürich, Switzerland
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Fernandez-Felix BM, Barca LV, Garcia-Esquinas E, Correa-Pérez A, Fernández-Hidalgo N, Muriel A, Lopez-Alcalde J, Álvarez-Diaz N, Pijoan JI, Ribera A, Elorza EN, Muñoz P, Fariñas MDC, Goenaga MÁ, Zamora J. Prognostic models for mortality after cardiac surgery in patients with infective endocarditis: a systematic review and aggregation of prediction models. Clin Microbiol Infect 2021; 27:1422-1430. [PMID: 34620380 DOI: 10.1016/j.cmi.2021.05.051] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2021] [Revised: 05/19/2021] [Accepted: 05/31/2021] [Indexed: 12/13/2022]
Abstract
BACKGROUND There are several prognostic models to estimate the risk of mortality after surgery for active infective endocarditis (IE). However, these models incorporate different predictors and their performance is uncertain. OBJECTIVE We systematically reviewed and critically appraised all available prediction models of postoperative mortality in patients undergoing surgery for IE, and aggregated them into a meta-model. DATA SOURCES We searched Medline and EMBASE databases from inception to June 2020. STUDY ELIGIBILITY CRITERIA We included studies that developed or updated a prognostic model of postoperative mortality in patient with IE. METHODS We assessed the risk of bias of the models using PROBAST (Prediction model Risk Of Bias ASsessment Tool) and we aggregated them into an aggregate meta-model based on stacked regressions and optimized it for a nationwide registry of IE patients. The meta-model performance was assessed using bootstrap validation methods and adjusted for optimism. RESULTS We identified 11 prognostic models for postoperative mortality. Eight models had a high risk of bias. The meta-model included weighted predictors from the remaining three models (EndoSCORE, specific ES-I and specific ES-II), which were not rated as high risk of bias and provided full model equations. Additionally, two variables (age and infectious agent) that had been modelled differently across studies, were estimated based on the nationwide registry. The performance of the meta-model was better than the original three models, with the corresponding performance measures: C-statistics 0.79 (95% CI 0.76-0.82), calibration slope 0.98 (95% CI 0.86-1.13) and calibration-in-the-large -0.05 (95% CI -0.20 to 0.11). CONCLUSIONS The meta-model outperformed published models and showed a robust predictive capacity for predicting the individualized risk of postoperative mortality in patients with IE. PROTOCOL REGISTRATION PROSPERO (registration number CRD42020192602).
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Affiliation(s)
- Borja M Fernandez-Felix
- Clinical Biostatistics Unit, Hospital Universitario Ramon y Cajal (IRYCIS), Madrid, Spain; CIBER Epidemiology and Public Health (CIBERESP), Madrid, Spain.
| | - Laura Varela Barca
- Department of Cardiovascular Surgery, Fundacion Jimenez Diaz University Hospital, Madrid, Spain
| | - Esther Garcia-Esquinas
- CIBER Epidemiology and Public Health (CIBERESP), Madrid, Spain; Department of Preventive Medicine and Public Health, School of Medicine, Universidad Autónoma de Madrid, Madrid, Spain; IdiPaz (Hospital Universitario La Paz-Universidad Autónoma de Madrid), Madrid, Spain
| | - Andrea Correa-Pérez
- Clinical Biostatistics Unit, Hospital Universitario Ramon y Cajal (IRYCIS), Madrid, Spain; Faculty of Medicine, Universidad Francisco de Vitoria, Madrid, Spain
| | - Nuria Fernández-Hidalgo
- Servei de Malalties Infeccioses, Hospital Universitari Vall d'Hebron, Barcelona, Spain; Red Española de Investigación en Patología Infecciosa (REIPI), Instituto de Salud Carlos III, Madrid, Spain
| | - Alfonso Muriel
- Clinical Biostatistics Unit, Hospital Universitario Ramon y Cajal (IRYCIS), Madrid, Spain; CIBER Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Jesus Lopez-Alcalde
- Clinical Biostatistics Unit, Hospital Universitario Ramon y Cajal (IRYCIS), Madrid, Spain; CIBER Epidemiology and Public Health (CIBERESP), Madrid, Spain; Faculty of Medicine, Universidad Francisco de Vitoria, Madrid, Spain; Institute for Complementary and Integrative Medicine, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Noelia Álvarez-Diaz
- Medical Library, Hospital Universitario Ramon y Cajal (IRYCIS), Madrid, Madrid, Spain
| | - Jose I Pijoan
- CIBER Epidemiology and Public Health (CIBERESP), Madrid, Spain; Hospital Universitario Cruces/OSI EEC, Barakaldo, Spain; Biocruces-Bizkaia Health Research Institute, Barakaldo, Spain
| | - Aida Ribera
- CIBER Epidemiology and Public Health (CIBERESP), Madrid, Spain; Cardiovascular Epidemiology and Research Unit, Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | - Enrique Navas Elorza
- Department of Infectology, Hospital Universitario Ramon y Cajal (IRYCIS), Madrid, Spain
| | - Patricia Muñoz
- Clinical Microbiology and Infectious Diseases Service, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón, CIBER Enfermedades Respiratorias-CIBERES, Facultad de Medicina, Universidad Complutense de Madrid, Madrid, Spain
| | - María Del Carmen Fariñas
- Infectious Diseases Service, Hospital Universitario Marqués de Valdecilla-IDIVAL, Universidad de Cantabria, Santander, Spain
| | - Miguel Ángel Goenaga
- Infectious Diseases Service, Hospital Universitario Donostia, IIS Biodonostia, OSI Donostialdea, San Sebastián, Spain
| | - Javier Zamora
- Clinical Biostatistics Unit, Hospital Universitario Ramon y Cajal (IRYCIS), Madrid, Spain; CIBER Epidemiology and Public Health (CIBERESP), Madrid, Spain; WHO Collaborating Centre for Global Women's Health, Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
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Allotey J, Snell KI, Smuk M, Hooper R, Chan CL, Ahmed A, Chappell LC, von Dadelszen P, Dodds J, Green M, Kenny L, Khalil A, Khan KS, Mol BW, Myers J, Poston L, Thilaganathan B, Staff AC, Smith GC, Ganzevoort W, Laivuori H, Odibo AO, Ramírez JA, Kingdom J, Daskalakis G, Farrar D, Baschat AA, Seed PT, Prefumo F, da Silva Costa F, Groen H, Audibert F, Masse J, Skråstad RB, Salvesen KÅ, Haavaldsen C, Nagata C, Rumbold AR, Heinonen S, Askie LM, Smits LJ, Vinter CA, Magnus PM, Eero K, Villa PM, Jenum AK, Andersen LB, Norman JE, Ohkuchi A, Eskild A, Bhattacharya S, McAuliffe FM, Galindo A, Herraiz I, Carbillon L, Klipstein-Grobusch K, Yeo S, Teede HJ, Browne JL, Moons KG, Riley RD, Thangaratinam S. Validation and development of models using clinical, biochemical and ultrasound markers for predicting pre-eclampsia: an individual participant data meta-analysis. Health Technol Assess 2021; 24:1-252. [PMID: 33336645 DOI: 10.3310/hta24720] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Pre-eclampsia is a leading cause of maternal and perinatal mortality and morbidity. Early identification of women at risk is needed to plan management. OBJECTIVES To assess the performance of existing pre-eclampsia prediction models and to develop and validate models for pre-eclampsia using individual participant data meta-analysis. We also estimated the prognostic value of individual markers. DESIGN This was an individual participant data meta-analysis of cohort studies. SETTING Source data from secondary and tertiary care. PREDICTORS We identified predictors from systematic reviews, and prioritised for importance in an international survey. PRIMARY OUTCOMES Early-onset (delivery at < 34 weeks' gestation), late-onset (delivery at ≥ 34 weeks' gestation) and any-onset pre-eclampsia. ANALYSIS We externally validated existing prediction models in UK cohorts and reported their performance in terms of discrimination and calibration. We developed and validated 12 new models based on clinical characteristics, clinical characteristics and biochemical markers, and clinical characteristics and ultrasound markers in the first and second trimesters. We summarised the data set-specific performance of each model using a random-effects meta-analysis. Discrimination was considered promising for C-statistics of ≥ 0.7, and calibration was considered good if the slope was near 1 and calibration-in-the-large was near 0. Heterogeneity was quantified using I 2 and τ2. A decision curve analysis was undertaken to determine the clinical utility (net benefit) of the models. We reported the unadjusted prognostic value of individual predictors for pre-eclampsia as odds ratios with 95% confidence and prediction intervals. RESULTS The International Prediction of Pregnancy Complications network comprised 78 studies (3,570,993 singleton pregnancies) identified from systematic reviews of tests to predict pre-eclampsia. Twenty-four of the 131 published prediction models could be validated in 11 UK cohorts. Summary C-statistics were between 0.6 and 0.7 for most models, and calibration was generally poor owing to large between-study heterogeneity, suggesting model overfitting. The clinical utility of the models varied between showing net harm to showing minimal or no net benefit. The average discrimination for IPPIC models ranged between 0.68 and 0.83. This was highest for the second-trimester clinical characteristics and biochemical markers model to predict early-onset pre-eclampsia, and lowest for the first-trimester clinical characteristics models to predict any pre-eclampsia. Calibration performance was heterogeneous across studies. Net benefit was observed for International Prediction of Pregnancy Complications first and second-trimester clinical characteristics and clinical characteristics and biochemical markers models predicting any pre-eclampsia, when validated in singleton nulliparous women managed in the UK NHS. History of hypertension, parity, smoking, mode of conception, placental growth factor and uterine artery pulsatility index had the strongest unadjusted associations with pre-eclampsia. LIMITATIONS Variations in study population characteristics, type of predictors reported, too few events in some validation cohorts and the type of measurements contributed to heterogeneity in performance of the International Prediction of Pregnancy Complications models. Some published models were not validated because model predictors were unavailable in the individual participant data. CONCLUSION For models that could be validated, predictive performance was generally poor across data sets. Although the International Prediction of Pregnancy Complications models show good predictive performance on average, and in the singleton nulliparous population, heterogeneity in calibration performance is likely across settings. FUTURE WORK Recalibration of model parameters within populations may improve calibration performance. Additional strong predictors need to be identified to improve model performance and consistency. Validation, including examination of calibration heterogeneity, is required for the models we could not validate. STUDY REGISTRATION This study is registered as PROSPERO CRD42015029349. FUNDING This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 24, No. 72. See the NIHR Journals Library website for further project information.
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Practical Considerations and Challenges When Conducting an Individual Participant Data (IPD) Meta-Analysis. Methods Mol Biol 2021. [PMID: 34550596 DOI: 10.1007/978-1-0716-1566-9_16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
Abstract
This chapter provides a broad overview of the use of individual participant (sometimes referred to as patient) data (IPD ) within meta-analyses, the associated advantages of using IPD in meta-analysis compared to aggregate data, and when IPD should be used in meta-analysis.This chapter also outlines the steps of conducting an IPD meta-analysis, with practical guidance relating to requesting and obtaining IPD for meta-analysis. Challenges that can be associated with conducting an IPD meta-analysis are also discussed, including consideration of availability bias, when a subset of the relevant IPD is not available for meta-analysis.
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Tian J, Gao Y, Zhang J, Yang Z, Dong S, Zhang T, Sun F, Wu S, Wu J, Wang J, Yao L, Ge L, Li L, Shi C, Wang Q, Li J, Zhao Y, Xiao Y, Yang F, Fan J, Bao S, Song F. Progress and challenges of network meta-analysis. J Evid Based Med 2021; 14:218-231. [PMID: 34463038 DOI: 10.1111/jebm.12443] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 08/03/2021] [Accepted: 08/03/2021] [Indexed: 11/28/2022]
Abstract
In the past years, network meta-analysis (NMA) has been widely used among clinicians, guideline makers, and health technology assessment agencies and has played an important role in clinical decision-making and guideline development. To inform further development of NMAs, we conducted a bibliometric analysis to assess the current status of published NMA methodological studies, summarized the methodological progress of seven types of NMAs, and discussed the current challenges of NMAs.
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Affiliation(s)
- Jinhui Tian
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
- Key Laboratory of Evidence-Based Medicine and Knowledge Translation of Gansu Province, Lanzhou, China
| | - Ya Gao
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
- Key Laboratory of Evidence-Based Medicine and Knowledge Translation of Gansu Province, Lanzhou, China
| | - Junhua Zhang
- Evidence-Based Medicine Center, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Zhirong Yang
- Primary Care Unit, Department of Public Health and Primary Care, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Shengjie Dong
- Orthopedic Department, Yantaishan Hospital, Yantai, Shandong, China
| | - Tiansong Zhang
- Department of Traditional Chinese Medicine, Jing'an District Central Hospital, Shanghai, China
| | - Feng Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Shanshan Wu
- National Clinical Research Center of Digestive Diseases, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Jiarui Wu
- Department of Clinical Chinese Pharmacy, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China
| | - Junfeng Wang
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands
| | - Liang Yao
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada
| | - Long Ge
- Key Laboratory of Evidence-Based Medicine and Knowledge Translation of Gansu Province, Lanzhou, China
- Evidence-Based Social Science Research Center, School of Public Health, Lanzhou University, Lanzhou, China
| | - Lun Li
- Department of Breast Cancer, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Chunhu Shi
- Division of Nursing, Midwifery and Social Work, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Quan Wang
- Department of Gastrointestinal Surgery, Peking University People's Hospital, Beijing, China
| | - Jiang Li
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ye Zhao
- First Clinical Medical College, Lanzhou University, Lanzhou, China
- Departments of Biochemistry and Molecular Biology, Melvin and Bren Simon Comprehensive Cancer Center, Indiana University School of Medicine, Indianapolis, Indiana
| | - Yue Xiao
- China National Health Development Research Center, Beijing, China
| | - Fengwen Yang
- Evidence-Based Medicine Center, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Jinchun Fan
- Epidemiology and Evidence Based-Medicine, School of Public Health, Gansu University of Chinese Medicine, Lanzhou, China
| | - Shisan Bao
- Epidemiology and Evidence Based-Medicine, School of Public Health, Gansu University of Chinese Medicine, Lanzhou, China
- Sydney, NSW, Australia
| | - Fujian Song
- Public Health and Health Services Research, Norwich Medical School, University of East Anglia, Norwich, UK
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Internal-external cross-validation helped to evaluate the generalizability of prediction models in large clustered datasets. J Clin Epidemiol 2021; 137:83-91. [PMID: 33836256 DOI: 10.1016/j.jclinepi.2021.03.025] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Revised: 03/05/2021] [Accepted: 03/29/2021] [Indexed: 12/23/2022]
Abstract
OBJECTIVE To illustrate how to evaluate the need of complex strategies for developing generalizable prediction models in large clustered datasets. STUDY DESIGN AND SETTING We developed eight Cox regression models to estimate the risk of heart failure using a large population-level dataset. These models differed in the number of predictors, the functional form of the predictor effects (non-linear effects and interaction) and the estimation method (maximum likelihood and penalization). Internal-external cross-validation was used to evaluate the models' generalizability across the included general practices. RESULTS Among 871,687 individuals from 225 general practices, 43,987 (5.5%) developed heart failure during a median follow-up time of 5.8 years. For discrimination, the simplest prediction model yielded a good concordance statistic, which was not much improved by adopting complex strategies. Between-practice heterogeneity in discrimination was similar in all models. For calibration, the simplest model performed satisfactorily. Although accounting for non-linear effects and interaction slightly improved the calibration slope, it also led to more heterogeneity in the observed/expected ratio. Similar results were found in a second case study involving patients with stroke. CONCLUSION In large clustered datasets, prediction model studies may adopt internal-external cross-validation to evaluate the generalizability of competing models, and to identify promising modelling strategies.
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Venekamp RP, Hoogland J, van Smeden M, Rovers MM, De Sutter AI, Merenstein D, van Essen GA, Kaiser L, Liira H, Little P, Bucher HC, Reitsma JB. Identifying adults with acute rhinosinusitis in primary care that benefit most from antibiotics: protocol of an individual patient data meta-analysis using multivariable risk prediction modelling. BMJ Open 2021; 11:e047186. [PMID: 34210729 PMCID: PMC8252877 DOI: 10.1136/bmjopen-2020-047186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Abstract
INTRODUCTION Acute rhinosinusitis (ARS) is a prime reason for doctor visits and among the conditions with highest antibiotic overprescribing rates in adults. To reduce inappropriate prescribing, we aim to predict the absolute benefit of antibiotic treatment for individual adult patients with ARS by applying multivariable risk prediction methods to individual patient data (IPD) of multiple randomised placebo-controlled trials. METHODS AND ANALYSIS This is an update and re-analysis of a 2008 IPD meta-analysis on antibiotics for adults with clinically diagnosed ARS. First, the reference list of the 2018 Cochrane review on antibiotics for ARS will be reviewed for relevant studies published since 2008. Next, the systematic searches of CENTRAL, MEDLINE and Embase of the Cochrane review will be updated to 1 September 2020. Methodological quality of eligible studies will be assessed using the Cochrane Risk of Bias 2 tool. The primary outcome is cure at 8-15 days. Regression-based methods will be used to model the risk of being cured based on relevant predictors and treatment, while accounting for clustering. Such model allows for risk predictions as a function of treatment and individual patient characteristics and hence gives insight into individualised absolute benefit. Candidate predictors will be based on literature, clinical reasoning and availability. Calibration and discrimination will be evaluated to assess model performance. Resampling techniques will be used to assess internal validation. In addition, internal-external cross-validation procedures will be used to inform on between-study differences and estimate out-of-sample model performance. Secondarily, we will study possible heterogeneity of treatment effect as a function of outcome risk. ETHICS AND DISSEMINATION In this study, no identifiable patient data will be used. As such, the Medical Research Involving Humans Subject Act (WMO) does not apply and official ethical approval is not required. Results will be submitted for publication in international peer-reviewed journals. PROSPERO REGISTRATION NUMBER CRD42020220108.
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Affiliation(s)
- Roderick P Venekamp
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Jeroen Hoogland
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Maarten van Smeden
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Maroeska M Rovers
- Radboud Institute for Health Sciences (RIHS), Radboud University Medical Center, Nijmegen, The Netherlands
| | - An I De Sutter
- Department of Public Health and Primary Care, Ghent University, Ghent, Belgium
| | - Daniel Merenstein
- Department of Family Medicine, Georgetown University Medical Center, Washington, DC, USA
| | | | - Laurent Kaiser
- Department of Medicine, Division of Infectious Diseases, University Hospital Geneva, Geneva, Switzerland
| | - Helena Liira
- Department of General Practice, School of Primary, Aboriginal and Rural Health Care, University of Western Autralia, Perth, Western Australia, Australia
- Department of General Practice and Primary Care, University of Helsinki, Helsinki, Finland
| | - Paul Little
- Primary Care & Population Sciences Unit, Aldermoor Health Centre, University of Southampton, Southampton, UK
| | - Heiner Cc Bucher
- Basel Institute for Clinical Epidemiology, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Johannes B Reitsma
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
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Becattini C, Maraziti G, Vinson DR, Ng ACC, den Exter PL, Côté B, Vanni S, Doukky R, Khemasuwan D, Weekes AJ, Soares TH, Ozsu S, Polo Friz H, Erol S, Agnelli G, Jiménez D. Right ventricle assessment in patients with pulmonary embolism at low risk for death based on clinical models: an individual patient data meta-analysis. Eur Heart J 2021; 42:3190-3199. [PMID: 34179965 DOI: 10.1093/eurheartj/ehab329] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/18/2021] [Indexed: 01/01/2023] Open
Abstract
AIMS Patients with acute pulmonary embolism (PE) at low risk for short-term death are candidates for home treatment or short-hospital stay. We aimed at determining whether the assessment of right ventricle dysfunction (RVD) or elevated troponin improves identification of low-risk patients over clinical models alone. METHODS AND RESULTS Individual patient data meta-analysis of studies assessing the relationship between RVD or elevated troponin and short-term mortality in patients with acute PE at low risk for death based on clinical models (Pulmonary Embolism Severity Index, simplified Pulmonary Embolism Severity Index or Hestia). The primary study outcome was short-term death defined as death occurring in hospital or within 30 days. Individual data of 5010 low-risk patients from 18 studies were pooled. Short-term mortality was 0.7% [95% confidence interval (CI) 0.4-1.3]. RVD at echocardiography, computed tomography or B-type natriuretic peptide (BNP)/N-terminal pro BNP (NT-proBNP) was associated with increased risk for short-term death (1.5 vs. 0.3%; OR 4.81, 95% CI 1.98-11.68), death within 3 months (1.6 vs. 0.4%; OR 4.03, 95% CI 2.01-8.08), and PE-related death (1.1 vs. 0.04%; OR 22.9, 95% CI 2.89-181). Elevated troponin was associated with short-term death (OR 2.78, 95% CI 1.06-7.26) and death within 3 months (OR 3.68, 95% CI 1.75-7.74). CONCLUSION RVD assessed by echocardiography, computed tomography, or elevated BNP/NT-proBNP levels and increased troponin are associated with short-term death in patients with acute PE at low risk based on clinical models. RVD assessment, mainly by BNP/NT-proBNP or echocardiography, should be considered to improve identification of low-risk patients that may be candidates for outpatient management or short hospital stay.
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Affiliation(s)
- Cecilia Becattini
- Internal and Cardiovascular Medicine-Stroke Unit, University of Perugia, Ospedale Santa Maria della Misericordia, Via G. Dottori 1, 06129 Perugia, Italy
| | - Giorgio Maraziti
- Internal and Cardiovascular Medicine-Stroke Unit, University of Perugia, Ospedale Santa Maria della Misericordia, Via G. Dottori 1, 06129 Perugia, Italy
| | - David R Vinson
- Department of Emergency Medicine, The Permanente Medical Group and the Kaiser Permanente Division of Research, Oakland, CA, USA
| | - Austin C C Ng
- Cardiology Department, Concord Hospital, The University of Sydney, Concord, NSW, Australia
| | - Paul L den Exter
- Department of Thrombosis and Hemostasis, Leiden University Medical Center, Leiden, the Netherlands
| | - Benoit Côté
- Département de Médecine Interne, Hôpital de l'Enfant-Jésus du CHU de Québec, Université Laval, Québec, Canada
| | - Simone Vanni
- Emergency Medicine Unit, Empoli, Azienda Usl Toscana Centro, Italy
| | - Rami Doukky
- Division of Cardiology, Cook County Health, Chicago, IL, USA
| | - Danai Khemasuwan
- Division of Pulmonary and Critical Care Medicine, Virginia Commonwealth University, Richmond, VA, USA
| | - Anthony J Weekes
- Department of Emergency Medicine, Carolinas Medical Center, Charlotte, NC, USA
| | - Thiago Horta Soares
- Internal Medicine Division, Rede Mater Dei de Saúde, Belo Horizonte, Minas Gerais, Brazil
| | - Savas Ozsu
- Department of Pulmonary Medicine, School of Medicine, Karadeniz Technical University, Trabzon, Turkey
| | - Hernan Polo Friz
- Internal Medicine Division, Medical Department, Vimercate Hospital, Vimercate, Italy
| | - Serhat Erol
- University of Ankara School of Medicine, Pulmonary Diseases Department, Ankara, Turkey
| | - Giancarlo Agnelli
- Internal and Cardiovascular Medicine-Stroke Unit, University of Perugia, Ospedale Santa Maria della Misericordia, Via G. Dottori 1, 06129 Perugia, Italy
| | - David Jiménez
- Respiratory Department, Ramón y Cajal Hospital and Universidad de Alcalá (IRYCIS), Madrid, Spain.,CIBER de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
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38
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de Jong VMT, Moons KGM, Eijkemans MJC, Riley RD, Debray TPA. Developing more generalizable prediction models from pooled studies and large clustered data sets. Stat Med 2021; 40:3533-3559. [PMID: 33948970 PMCID: PMC8252590 DOI: 10.1002/sim.8981] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Revised: 02/16/2021] [Accepted: 03/22/2021] [Indexed: 12/14/2022]
Abstract
Prediction models often yield inaccurate predictions for new individuals. Large data sets from pooled studies or electronic healthcare records may alleviate this with an increased sample size and variability in sample characteristics. However, existing strategies for prediction model development generally do not account for heterogeneity in predictor‐outcome associations between different settings and populations. This limits the generalizability of developed models (even from large, combined, clustered data sets) and necessitates local revisions. We aim to develop methodology for producing prediction models that require less tailoring to different settings and populations. We adopt internal‐external cross‐validation to assess and reduce heterogeneity in models' predictive performance during the development. We propose a predictor selection algorithm that optimizes the (weighted) average performance while minimizing its variability across the hold‐out clusters (or studies). Predictors are added iteratively until the estimated generalizability is optimized. We illustrate this by developing a model for predicting the risk of atrial fibrillation and updating an existing one for diagnosing deep vein thrombosis, using individual participant data from 20 cohorts (N = 10 873) and 11 diagnostic studies (N = 10 014), respectively. Meta‐analysis of calibration and discrimination performance in each hold‐out cluster shows that trade‐offs between average and heterogeneity of performance occurred. Our methodology enables the assessment of heterogeneity of prediction model performance during model development in multiple or clustered data sets, thereby informing researchers on predictor selection to improve the generalizability to different settings and populations, and reduce the need for model tailoring. Our methodology has been implemented in the R package metamisc.
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Affiliation(s)
- Valentijn M T de Jong
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.,Cochrane Netherlands, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Karel G M Moons
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.,Cochrane Netherlands, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Marinus J C Eijkemans
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Richard D Riley
- Centre for Prognosis Research, School of Medicine, Keele University, Staffordshire, UK
| | - Thomas P A Debray
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.,Cochrane Netherlands, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
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Gupta RK, Harrison EM, Ho A, Docherty AB, Knight SR, van Smeden M, Abubakar I, Lipman M, Quartagno M, Pius R, Buchan I, Carson G, Drake TM, Dunning J, Fairfield CJ, Gamble C, Green CA, Halpin S, Hardwick HE, Holden KA, Horby PW, Jackson C, Mclean KA, Merson L, Nguyen-Van-Tam JS, Norman L, Olliaro PL, Pritchard MG, Russell CD, Scott-Brown J, Shaw CA, Sheikh A, Solomon T, Sudlow C, Swann OV, Turtle L, Openshaw PJM, Baillie JK, Semple MG, Noursadeghi M. Development and validation of the ISARIC 4C Deterioration model for adults hospitalised with COVID-19: a prospective cohort study. THE LANCET. RESPIRATORY MEDICINE 2021; 9:349-359. [PMID: 33444539 PMCID: PMC7832571 DOI: 10.1016/s2213-2600(20)30559-2] [Citation(s) in RCA: 133] [Impact Index Per Article: 44.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 11/25/2020] [Accepted: 11/25/2020] [Indexed: 01/19/2023]
Abstract
BACKGROUND Prognostic models to predict the risk of clinical deterioration in acute COVID-19 cases are urgently required to inform clinical management decisions. METHODS We developed and validated a multivariable logistic regression model for in-hospital clinical deterioration (defined as any requirement of ventilatory support or critical care, or death) among consecutively hospitalised adults with highly suspected or confirmed COVID-19 who were prospectively recruited to the International Severe Acute Respiratory and Emerging Infections Consortium Coronavirus Clinical Characterisation Consortium (ISARIC4C) study across 260 hospitals in England, Scotland, and Wales. Candidate predictors that were specified a priori were considered for inclusion in the model on the basis of previous prognostic scores and emerging literature describing routinely measured biomarkers associated with COVID-19 prognosis. We used internal-external cross-validation to evaluate discrimination, calibration, and clinical utility across eight National Health Service (NHS) regions in the development cohort. We further validated the final model in held-out data from an additional NHS region (London). FINDINGS 74 944 participants (recruited between Feb 6 and Aug 26, 2020) were included, of whom 31 924 (43·2%) of 73 948 with available outcomes met the composite clinical deterioration outcome. In internal-external cross-validation in the development cohort of 66 705 participants, the selected model (comprising 11 predictors routinely measured at the point of hospital admission) showed consistent discrimination, calibration, and clinical utility across all eight NHS regions. In held-out data from London (n=8239), the model showed a similarly consistent performance (C-statistic 0·77 [95% CI 0·76 to 0·78]; calibration-in-the-large 0·00 [-0·05 to 0·05]); calibration slope 0·96 [0·91 to 1·01]), and greater net benefit than any other reproducible prognostic model. INTERPRETATION The 4C Deterioration model has strong potential for clinical utility and generalisability to predict clinical deterioration and inform decision making among adults hospitalised with COVID-19. FUNDING National Institute for Health Research (NIHR), UK Medical Research Council, Wellcome Trust, Department for International Development, Bill & Melinda Gates Foundation, EU Platform for European Preparedness Against (Re-)emerging Epidemics, NIHR Health Protection Research Unit (HPRU) in Emerging and Zoonotic Infections at University of Liverpool, NIHR HPRU in Respiratory Infections at Imperial College London.
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Affiliation(s)
- Rishi K Gupta
- Institute for Global Health, University College London, London, UK
| | - Ewen M Harrison
- Centre for Medical Informatics, The Usher Institute, University of Edinburgh, Edinburgh, UK; Department of Clinical Surgery, University of Edinburgh, Edinburgh, UK
| | - Antonia Ho
- Medical Research Council, University of Glasgow Centre for Virus Research, Glasgow, UK; Department of Infectious Diseases, Queen Elizabeth University Hospital, Glasgow, UK
| | - Annemarie B Docherty
- Centre for Medical Informatics, The Usher Institute, University of Edinburgh, Edinburgh, UK; Intensive Care Unit, Royal Infirmary Edinburgh, Edinburgh, UK
| | - Stephen R Knight
- Centre for Medical Informatics, The Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Maarten van Smeden
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Ibrahim Abubakar
- Institute for Global Health, University College London, London, UK
| | - Marc Lipman
- UCL Respiratory, Division of Medicine, University College London, London, UK; Royal Free Hospitals NHS Trust, London, UK
| | - Matteo Quartagno
- MRC Clinical Trials Unit, Institute of Clinical Trials and Methodology, University College London, London, UK
| | - Riinu Pius
- Centre for Medical Informatics, The Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Iain Buchan
- Institute of Population Health Sciences, University of Liverpool, Liverpool, UK
| | - Gail Carson
- ISARIC Global Support Centre, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Thomas M Drake
- Centre for Medical Informatics, The Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Jake Dunning
- National Infection Service, Public Health England, London, UK; National Heart and Lung Institute, Imperial College London, London, UK
| | - Cameron J Fairfield
- Centre for Medical Informatics, The Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Carrol Gamble
- Liverpool Clinical Trials Centre, University of Liverpool, Liverpool, UK
| | - Christopher A Green
- Institute of Microbiology and Infection, University of Birmingham, Birmingham, UK
| | - Sophie Halpin
- Liverpool Clinical Trials Centre, University of Liverpool, Liverpool, UK
| | - Hayley E Hardwick
- NIHR Health Protection Research Unit, Institute of Infection, Veterinary, and Ecological Sciences, Faculty of Health and Life Sciences, University of Liverpool, Liverpool, UK
| | - Karl A Holden
- NIHR Health Protection Research Unit, Institute of Infection, Veterinary, and Ecological Sciences, Faculty of Health and Life Sciences, University of Liverpool, Liverpool, UK
| | - Peter W Horby
- ISARIC Global Support Centre, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Clare Jackson
- Liverpool Clinical Trials Centre, University of Liverpool, Liverpool, UK
| | - Kenneth A Mclean
- Centre for Medical Informatics, The Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Laura Merson
- ISARIC Global Support Centre, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Jonathan S Nguyen-Van-Tam
- Division of Epidemiology and Public Health, University of Nottingham School of Medicine, Nottingham, UK
| | - Lisa Norman
- Centre for Medical Informatics, The Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Piero L Olliaro
- ISARIC Global Support Centre, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Mark G Pritchard
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Clark D Russell
- Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK
| | | | - Catherine A Shaw
- Centre for Medical Informatics, The Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Aziz Sheikh
- Centre for Medical Informatics, The Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Tom Solomon
- NIHR Health Protection Research Unit, Institute of Infection, Veterinary, and Ecological Sciences, Faculty of Health and Life Sciences, University of Liverpool, Liverpool, UK; Walton Centre NHS Foundation Trust, Liverpool, UK
| | | | - Olivia V Swann
- Department of Child Life and Health, University of Edinburgh, Edinburgh, UK
| | - Lance Turtle
- NIHR Health Protection Research Unit, Institute of Infection, Veterinary, and Ecological Sciences, Faculty of Health and Life Sciences, University of Liverpool, Liverpool, UK; Tropical and Infectious Disease Unit, Royal Liverpool University Hospital, Liverpool, UK
| | | | - J Kenneth Baillie
- Roslin Institute, University of Edinburgh, Edinburgh, UK; Intensive Care Unit, Royal Infirmary Edinburgh, Edinburgh, UK
| | - Malcolm G Semple
- NIHR Health Protection Research Unit, Institute of Infection, Veterinary, and Ecological Sciences, Faculty of Health and Life Sciences, University of Liverpool, Liverpool, UK; Respiratory Medicine, Alder Hey Children's Hospital, Institute in The Park, University of Liverpool, Liverpool, UK.
| | - Mahdad Noursadeghi
- Division of Infection and Immunity, University College London, London, UK.
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van Lier LI, Bosmans JE, van der Roest HG, Heymans MW, Garms-Homolová V, Declercq A, V Jónsson P, van Hout HP. Development and Validation of a Prediction Model for 6-Month Societal Costs in Older Community Care-Recipients in Multiple Countries; the IBenC Study. Health Serv Insights 2021; 13:1178632920980462. [PMID: 33488092 PMCID: PMC7768843 DOI: 10.1177/1178632920980462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2020] [Accepted: 11/18/2020] [Indexed: 11/16/2022] Open
Abstract
This study aims to develop and validate a prediction model of societal costs during a period of 6-months in older community care-recipients across multiple European countries. Participants were older community care-recipients from 5 European countries. The outcome measure was mean 6-months total societal costs of resource utilisation (healthcare and informal care). Potential predictors included sociodemographic characteristics, functional limitations, clinical conditions, and diseases/disorders. The model was developed by performing Linear Mixed Models with a random intercept for the effect of country and validated by an internal-external validation procedure. Living alone, caregiver distress, (I)ADL impairment, required level of care support, health instability, presence of pain, behavioural problems, urinary incontinence and multimorbidity significantly predicted societal costs during 6 months. The model explained 32% of the variation within societal costs and showed good calibration in Iceland, Finland and Germany. Minor model adaptations improved model performance in The Netherland and Italy. The results can provide a valuable orientation for policymakers to better understand cost development among older community care-recipients. Despite substantial differences of countries’ care systems, a validated cross-national set of key predictors could be identified.
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Affiliation(s)
- Lisanne I van Lier
- Department of General Practice and Medicine of Older People, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, and Department on Aging, Netherlands Institute of Mental Health and Addiction (Trimbos Institute), Utrecht, Utrecht, The Netherlands
| | - Judith E Bosmans
- Department of Health Sciences, Faculty of Science, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Henriëtte G van der Roest
- Department of General Practice and Medicine of Older People, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, and Department on Aging, Netherlands Institute of Mental Health and Addiction (Trimbos Institute), Utrecht, Utrecht, The Netherlands
| | - Martijn W Heymans
- Department of Epidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Vjenka Garms-Homolová
- Department III, Economy and Law, Hochschule für Technik und Wirtschaft Berlin, Berlin, Germany
| | - Anja Declercq
- LUCAS, Centre for Care Research and Consultancy, and CESO, Center for Sociological Research, KU Leuven (University of Leuven), Leuven, Belgium
| | - Pálmi V Jónsson
- Department of Geriatrics, Landspitali University Hospital, Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Hein Pj van Hout
- Department of General Practice and Medicine of Older People, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, and Department on Aging, Netherlands Institute of Mental Health and Addiction (Trimbos Institute), Utrecht, Utrecht, The Netherlands
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Jenkins DA, Martin GP, Sperrin M, Riley RD, Debray TPA, Collins GS, Peek N. Continual updating and monitoring of clinical prediction models: time for dynamic prediction systems? Diagn Progn Res 2021; 5:1. [PMID: 33431065 PMCID: PMC7797885 DOI: 10.1186/s41512-020-00090-3] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Accepted: 12/08/2020] [Indexed: 01/01/2023] Open
Abstract
Clinical prediction models (CPMs) have become fundamental for risk stratification across healthcare. The CPM pipeline (development, validation, deployment, and impact assessment) is commonly viewed as a one-time activity, with model updating rarely considered and done in a somewhat ad hoc manner. This fails to address the fact that the performance of a CPM worsens over time as natural changes in populations and care pathways occur. CPMs need constant surveillance to maintain adequate predictive performance. Rather than reactively updating a developed CPM once evidence of deteriorated performance accumulates, it is possible to proactively adapt CPMs whenever new data becomes available. Approaches for validation then need to be changed accordingly, making validation a continuous rather than a discrete effort. As such, "living" (dynamic) CPMs represent a paradigm shift, where the analytical methods dynamically generate updated versions of a model through time; one then needs to validate the system rather than each subsequent model revision.
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Affiliation(s)
- David A Jenkins
- Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, UK.
- NIHR Greater Manchester Patient Safety Translational Research Centre, The University of Manchester, Manchester, UK.
| | - Glen P Martin
- Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Matthew Sperrin
- Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Richard D Riley
- Centre for Prognosis Research, School of Primary, Community and Social Care, Keele University, Staffordshire, UK
| | - Thomas P A Debray
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Gary S Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Niels Peek
- Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
- NIHR Greater Manchester Patient Safety Translational Research Centre, The University of Manchester, Manchester, UK
- NIHR Manchester Biomedical Research Centre, The University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
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Wang J, Keusters WR, Wen L, Leeflang MMG. IPDmada: An R Shiny tool for analyzing and visualizing individual patient data meta-analyses of diagnostic test accuracy. Res Synth Methods 2021; 12:45-54. [PMID: 32808437 PMCID: PMC7821168 DOI: 10.1002/jrsm.1444] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2020] [Revised: 06/18/2020] [Accepted: 08/11/2020] [Indexed: 11/16/2022]
Abstract
BACKGROUND Individual patient data meta-analyses (IPD-MA) are regarded as the gold standard for systematic reviews, which also applies to systematic reviews of diagnostic test accuracy (DTA) studies. An increasing number of DTA systematic reviews with IPD-MA have been published in recent years, but there is much variation in how these IPD-MA were performed. A number of existing methods were found, but there is no consensus as to which methods are preferred as the standard methods for statistical analysis in DTA IPD-MA. OBJECTIVES To create a web-based tool which integrates recommended statistical analyses for DTA IPD-MA, and allows researchers to analyse the data and visualize the results with interactive plots. METHODS A systematic methodological review was performed to identify statistical analyses and data visualization methods used in DTA IPD-MA. Methods were evaluated by the authors and recommended analyses were integrated into the IPDmada tool which is freely available online with the user interface developed with R Shiny package. RESULTS IPDmada allows users to upload their own data, perform the meta-analysis with both continuous and dichotomized tests, and incorporate individual level covariate-adjusted analysis. All tables and figures can be exported as .csv or .pdf files. A hypothetical dataset was used to illustrate the application of IPDmada. CONCLUSIONS IPDmada will be very helpful to researchers doing DTA IPD-MA, since it not only facilitates the statistical analysis but also provides a standard framework. The introduction of IPDmada will harmonize the methods used in DTA IPD-MA and ensure the quality of such analyses. HIGHLIGHTS IPDmada is a newly developed web-based tool for performing statistical analysis of individual patient data meta-analysis of diagnostic accuracy and visualizing the results. The tool is freely available to all the researchers, and requiring no installation of statistical software/packages. The tool has an user-friendly interface, and allows meta-analysis on both dichotomized and continuous test results. Researchers can easily use this tool to investigate the threshold effect and covariate effect on the summary accuracy. The introduction and implementation of IPDmada will serve as a useful tool for DTA IPD-MA and increase the quality of such studies.
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Affiliation(s)
- Junfeng Wang
- Julius Center for Health Sciences and Primary CareUMC Utrecht, Utrecht UniversityUtrechtThe Netherlands
| | - Willem R. Keusters
- Julius Center for Health Sciences and Primary CareUMC Utrecht, Utrecht UniversityUtrechtThe Netherlands
| | - Lingzi Wen
- Centre for Evidence‐Based Chinese MedicineBeijing University of Chinese MedicineBeijingChina
| | - Mariska M. G. Leeflang
- Department of Clinical Epidemiology, Biostatistics and BioinformaticsAmsterdam UMC, Amsterdam Public Health, University of AmsterdamAmsterdamThe Netherlands
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Gupta RK, Marks M, Samuels TH, Luintel A, Rampling T, Chowdhury H, Quartagno M, Nair A, Lipman M, Abubakar I, van Smeden M, Wong WK, Williams B, Noursadeghi M. Systematic evaluation and external validation of 22 prognostic models among hospitalised adults with COVID-19: an observational cohort study. Eur Respir J 2020; 56:2003498. [PMID: 32978307 PMCID: PMC7518075 DOI: 10.1183/13993003.03498-2020] [Citation(s) in RCA: 123] [Impact Index Per Article: 30.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 09/17/2020] [Indexed: 12/15/2022]
Abstract
The number of proposed prognostic models for coronavirus disease 2019 (COVID-19) is growing rapidly, but it is unknown whether any are suitable for widespread clinical implementation.We independently externally validated the performance of candidate prognostic models, identified through a living systematic review, among consecutive adults admitted to hospital with a final diagnosis of COVID-19. We reconstructed candidate models as per original descriptions and evaluated performance for their original intended outcomes using predictors measured at the time of admission. We assessed discrimination, calibration and net benefit, compared to the default strategies of treating all and no patients, and against the most discriminating predictors in univariable analyses.We tested 22 candidate prognostic models among 411 participants with COVID-19, of whom 180 (43.8%) and 115 (28.0%) met the endpoints of clinical deterioration and mortality, respectively. Highest areas under receiver operating characteristic (AUROC) curves were achieved by the NEWS2 score for prediction of deterioration over 24 h (0.78, 95% CI 0.73-0.83), and a novel model for prediction of deterioration <14 days from admission (0.78, 95% CI 0.74-0.82). The most discriminating univariable predictors were admission oxygen saturation on room air for in-hospital deterioration (AUROC 0.76, 95% CI 0.71-0.81), and age for in-hospital mortality (AUROC 0.76, 95% CI 0.71-0.81). No prognostic model demonstrated consistently higher net benefit than these univariable predictors, across a range of threshold probabilities.Admission oxygen saturation on room air and patient age are strong predictors of deterioration and mortality among hospitalised adults with COVID-19, respectively. None of the prognostic models evaluated here offered incremental value for patient stratification to these univariable predictors.
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Affiliation(s)
- Rishi K. Gupta
- Institute for Global Health, University College London, London, UK
- University College London Hospitals NHS Trust, London, UK
| | - Michael Marks
- University College London Hospitals NHS Trust, London, UK
- Clinical Research Dept, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | | | - Akish Luintel
- University College London Hospitals NHS Trust, London, UK
| | - Tommy Rampling
- University College London Hospitals NHS Trust, London, UK
| | | | - Matteo Quartagno
- MRC Clinical Trials Unit, Institute of Clinical Trials and Methodology, University College London, London, UK
| | - Arjun Nair
- University College London Hospitals NHS Trust, London, UK
| | - Marc Lipman
- UCL Respiratory, Division of Medicine, University College London, London, UK
| | - Ibrahim Abubakar
- Institute for Global Health, University College London, London, UK
| | - Maarten van Smeden
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Wai Keong Wong
- University College London Hospitals NHS Trust, London, UK
| | - Bryan Williams
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
- University College London, London, UK
| | - Mahdad Noursadeghi
- University College London Hospitals NHS Trust, London, UK
- Division of Infection and Immunity, University College London, London, UK
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Gupta RK, Calderwood CJ, Yavlinsky A, Krutikov M, Quartagno M, Aichelburg MC, Altet N, Diel R, Dobler CC, Dominguez J, Doyle JS, Erkens C, Geis S, Haldar P, Hauri AM, Hermansen T, Johnston JC, Lange C, Lange B, van Leth F, Muñoz L, Roder C, Romanowski K, Roth D, Sester M, Sloot R, Sotgiu G, Woltmann G, Yoshiyama T, Zellweger JP, Zenner D, Aldridge RW, Copas A, Rangaka MX, Lipman M, Noursadeghi M, Abubakar I. Discovery and validation of a personalized risk predictor for incident tuberculosis in low transmission settings. Nat Med 2020; 26:1941-1949. [PMID: 33077958 PMCID: PMC7614810 DOI: 10.1038/s41591-020-1076-0] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Accepted: 08/26/2020] [Indexed: 12/12/2022]
Abstract
The risk of tuberculosis (TB) is variable among individuals with latent Mycobacterium tuberculosis infection (LTBI), but validated estimates of personalized risk are lacking. In pooled data from 18 systematically identified cohort studies from 20 countries, including 80,468 individuals tested for LTBI, 5-year cumulative incident TB risk among people with untreated LTBI was 15.6% (95% confidence interval (CI), 8.0-29.2%) among child contacts, 4.8% (95% CI, 3.0-7.7%) among adult contacts, 5.0% (95% CI, 1.6-14.5%) among migrants and 4.8% (95% CI, 1.5-14.3%) among immunocompromised groups. We confirmed highly variable estimates within risk groups, necessitating an individualized approach to risk stratification. Therefore, we developed a personalized risk predictor for incident TB (PERISKOPE-TB) that combines a quantitative measure of T cell sensitization and clinical covariates. Internal-external cross-validation of the model demonstrated a random effects meta-analysis C-statistic of 0.88 (95% CI, 0.82-0.93) for incident TB. In decision curve analysis, the model demonstrated clinical utility for targeting preventative treatment, compared to treating all, or no, people with LTBI. We challenge the current crude approach to TB risk estimation among people with LTBI in favor of our evidence-based and patient-centered method, in settings aiming for pre-elimination worldwide.
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Affiliation(s)
- Rishi K Gupta
- Institute for Global Health, University College London, London, UK
| | | | - Alexei Yavlinsky
- Institute of Health Informatics, University College London, London, UK
| | - Maria Krutikov
- Institute for Global Health, University College London, London, UK
| | - Matteo Quartagno
- MRC Clinical Trials Unit, Institute of Clinical Trials and Methodology, University College London, London, UK
| | | | - Neus Altet
- Unitat de Tuberculosis, Hospital Universitari Vall d'Hebron-Drassanes, Barcelona, Spain
- Unitat de TDO de la Tuberculosis 'Servicios Clínicos', Barcelona, Spain
| | - Roland Diel
- Institute for Epidemiology, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany
- Lung Clinic Grosshansdorf, Airway Research Center North (ARCN), Großhansdorf, Germany
| | - Claudia C Dobler
- Institute for Evidence-Based Healthcare, Faculty of Health Sciences and Medicine, Bond University, Gold Coast, Queensland, Australia
- Department of Respiratory Medicine, Liverpool Hospital, Sydney, Australia
| | - Jose Dominguez
- Institut d'Investigació Germans Trias i Pujol, Badalona, Barcelona, Spain
- CIBER Enfermedades Respiratorias, Badalona, Barcelona, Spain
- Universitat Autònoma de Barcelona, Badalona, Barcelona, Spain
| | - Joseph S Doyle
- Department of Infectious Diseases, The Alfred and Monash University, Melbourne, Australia
- Disease Elimination Program, Burnet Institute, Melbourne, Australia
| | - Connie Erkens
- KNCV Tuberculosis Foundation, The Hague, The Netherlands
| | - Steffen Geis
- Institute for Medical Microbiology and Hospital Hygiene, Philipps University of Marburg, Marburg, Germany
| | - Pranabashis Haldar
- Respiratory Biomedical Research Centre, Institute for Lung Health, Department of Respiratory Sciences, University of Leicester, Leicester, UK
| | | | - Thomas Hermansen
- International Reference Laboratory of Mycobacteriology, Statens Serum Institut, Copenhagen, Denmark
| | - James C Johnston
- British Columbia Centre for Disease Control, Vancouver, British Columbia, Canada
| | - Christoph Lange
- Division of Clinical Infectious Diseases, Research Center Borstel, Borstel, Germany
- German Center for Infection Research (DZIF), Clinical Tuberculosis Center, Borstel, Germany
- Tuberculosis Network European Trials Group (TBnet), Borstel, Germany
- Department of Medicine, Karolinska Institute, Stockholm, Sweden
| | - Berit Lange
- Department of Epidemiology, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Frank van Leth
- Tuberculosis Network European Trials Group (TBnet), Borstel, Germany
- Amsterdam Institute for Global Health and Development, Amsterdam, the Netherlands
- Department of Global Health, Amsterdam University Medical Centres, Amsterdam, the Netherlands
| | - Laura Muñoz
- Department of Clinical Sciences, University of Barcelona, Barcelona, Spain
| | - Christine Roder
- Department of Infectious Diseases, The Alfred and Monash University, Melbourne, Australia
- Disease Elimination Program, Burnet Institute, Melbourne, Australia
| | - Kamila Romanowski
- British Columbia Centre for Disease Control, Vancouver, British Columbia, Canada
| | - David Roth
- British Columbia Centre for Disease Control, Vancouver, British Columbia, Canada
| | - Martina Sester
- Tuberculosis Network European Trials Group (TBnet), Borstel, Germany
- Department of Transplant and Infection Immunology, Saarland University, Homburg, Germany
| | - Rosa Sloot
- Department of Paediatrics and Child Health, Desmond Tutu TB Centre, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Giovanni Sotgiu
- Tuberculosis Network European Trials Group (TBnet), Borstel, Germany
- Clinical Epidemiology and Medical Statistics Unit, Department of Medical, Surgical and Experimental Sciences, Uniiversity of Sassari, Sassari, Italy
| | - Gerrit Woltmann
- Respiratory Biomedical Research Centre, Institute for Lung Health, Department of Respiratory Sciences, University of Leicester, Leicester, UK
| | | | - Jean-Pierre Zellweger
- Tuberculosis Network European Trials Group (TBnet), Borstel, Germany
- Swiss Lung Association, Berne, Switzerland
| | - Dominik Zenner
- Institute for Global Health, University College London, London, UK
| | - Robert W Aldridge
- Institute of Health Informatics, University College London, London, UK
| | - Andrew Copas
- Institute for Global Health, University College London, London, UK
- MRC Clinical Trials Unit, Institute of Clinical Trials and Methodology, University College London, London, UK
| | - Molebogeng X Rangaka
- Institute for Global Health, University College London, London, UK
- MRC Clinical Trials Unit, Institute of Clinical Trials and Methodology, University College London, London, UK
- Wellcome Centre for Infectious Diseases Research in Africa, Institute of Infectious Diseases and Molecular Medicine, University of Cape Town, Cape Town, South Africa
- Division of Epidemiology and Biostatistics, School of Public Health, University of Cape Town, Cape Town, South Africa
| | - Marc Lipman
- UCL-TB and UCL Respiratory, University College London, London, UK
- Royal Free London NHS Foundation Trust, London, UK
| | | | - Ibrahim Abubakar
- Institute for Global Health, University College London, London, UK.
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Gosling CJ, Pinabiaux C, Caparos S, Delorme R, Cortese S. Influence of the month of birth on persistence of ADHD in prospective studies: protocol for an individual patient data meta-analysis. BMJ Open 2020; 10:e040952. [PMID: 33199424 PMCID: PMC7670948 DOI: 10.1136/bmjopen-2020-040952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
Abstract
INTRODUCTION Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder with symptoms, especially the hyperactive ones, that tend to decrease in severity with age. Interestingly, children born just before the school-entry cut-off date (ie, the youngest pupils of a classroom) are at higher risk of being diagnosed with ADHD compared with children born just after the cut-off date. Noteworthy, this month-of-birth effect tends to disappear with increasing absolute age. Therefore, it is possible that young children erroneously diagnosed with ADHD due to their month of birth present a lower chance to have their diagnosis confirmed at a later age, artificially reinforcing the low persistence of ADHD across the lifespan. This protocol outlines an individual patient data (IPD) meta-analysis of prospective observational studies to explore the role of the month of birth in the low persistence of ADHD across the lifespan. METHODS AND ANALYSIS Five databases will be systematically searched in order to find prospective observational studies where the presence of ADHD is assessed both at baseline and at a follow-up of at least 4 years. We will use a two-stage IPD meta-analytic approach to estimate the role of the month of birth in the persistence of ADHD. Various sensitivity analyses will be performed to assess the robustness of the results. ETHICS AND DISSEMINATION No additional data will be collected and no de-identified raw data will be used. Ethics approval is thus not required for the present study. Results of this IPD meta-analysis will be submitted for publication in a peer-reviewed journal. PROSPERO REGISTRATION NUMBER CRD42020212650.
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Affiliation(s)
- Corentin J Gosling
- Department of Psychology, DysCo Lab, Paris-Nanterre University, Nanterre, France
- Department of Psychology, EA 4057, Université de Paris, Paris, France
| | - Charlotte Pinabiaux
- Department of Psychology, DysCo Lab, Paris-Nanterre University, Nanterre, France
| | - Serge Caparos
- Department of Psychology, DysCo Lab, Paris 8 University, Saint-Denis, France
- Institut Universitaire de France, Paris, France
| | - Richard Delorme
- Department of Child and Adolescent Psychiatry, Assistance Publique - Hopitaux de Paris, Paris, France
- Human Genetics and Cognitive Functions, Institut Pasteur, Paris, France
| | - Samuele Cortese
- Center for Innovation in Mental Health, School of Psychology, Faculty of Environmental and Life Sciences, University of Southampton, Southampton, UK
- Clinical and Experimental Sciences (CNS and Psychiatry), Faculty of Medicine, University of Southampton, Southampton, UK
- Solent NHS Trust, Southampton, UK
- Hassenfeld Children's Hospital at NYU Langone, New York University Child Study Center, New York City, New York, USA
- Division of Psychiatry and Applied Psychology, School of Medicine, University of Nottingham, Nottingham, UK
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46
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Venekamp R, Hansen JG, Reitsma JB, Ebell MH, Lindbaek M. Accuracy of signs, symptoms and blood tests for diagnosing acute bacterial rhinosinusitis and CT-confirmed acute rhinosinusitis in adults: protocol of an individual patient data meta-analysis. BMJ Open 2020; 10:e040988. [PMID: 33148765 PMCID: PMC7640527 DOI: 10.1136/bmjopen-2020-040988] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
INTRODUCTION This protocol outlines a diagnostic individual patient data (IPD) meta-analysis aimed at developing simple prediction models based on readily available signs, symptoms and blood tests to accurately predict acute bacterial rhinosinusitis and CT-confirmed (fluid level or total opacification in any sinus) acute rhinosinusitis (ARS) in adults presenting to primary care with clinically diagnosed ARS, target conditions associated with antibiotic benefit. METHODS AND ANALYSIS The systematic searches of PubMed and Embase of a review on the accuracy of signs and symptoms for diagnosing ARS in ambulatory care will be updated to April 2020 to identify relevant studies. Authors of eligible studies will be contacted and invited to provide IPD. Methodological quality of the studies will be assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Candidate predictor selection will be based on knowledge from existing literature, clinical reasoning and availability. Multivariable logistic regression analyses will be used to develop prediction models aimed at calculating absolute risk estimates. Large unexplained between-study heterogeneity in predictive accuracy of the models will be explored and may lead to either model adjustment or derivation of separate context-specific models. Calibration and discrimination will be evaluated to assess the models' performance. Bootstrap resampling techniques will be used to assess internal validation and to inform on possible adjustment for overfitting. In addition, we aim to perform internal-external cross-validation procedures. ETHICS AND DISSEMINATION In this IPD meta-analysis, no identifiable patient data will be used. As such, the Medical Research Involving Humans Subject Act does not apply, and official ethical approval is not required. Findings will be published in international peer-reviewed journals and presented at scientific conferences. PROSPERO REGISTRATION NUMBER PROSPERO CRD42020175659.
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Affiliation(s)
- Roderick Venekamp
- Julius Center for Health Sciences and Primary Care, UMC Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Jens Georg Hansen
- Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark
| | - Johannes B Reitsma
- Julius Center for Health Sciences and Primary Care, UMC Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Mark H Ebell
- Department of Epidemiology and Biostatistics, University of Georgia College of Public Health, Athens, Georgia, USA
| | - Morten Lindbaek
- Department of General Practice, Institute for Health and Society, University of Oslo, Oslo, Norway
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Snell KIE, Allotey J, Smuk M, Hooper R, Chan C, Ahmed A, Chappell LC, Von Dadelszen P, Green M, Kenny L, Khalil A, Khan KS, Mol BW, Myers J, Poston L, Thilaganathan B, Staff AC, Smith GCS, Ganzevoort W, Laivuori H, Odibo AO, Arenas Ramírez J, Kingdom J, Daskalakis G, Farrar D, Baschat AA, Seed PT, Prefumo F, da Silva Costa F, Groen H, Audibert F, Masse J, Skråstad RB, Salvesen KÅ, Haavaldsen C, Nagata C, Rumbold AR, Heinonen S, Askie LM, Smits LJM, Vinter CA, Magnus P, Eero K, Villa PM, Jenum AK, Andersen LB, Norman JE, Ohkuchi A, Eskild A, Bhattacharya S, McAuliffe FM, Galindo A, Herraiz I, Carbillon L, Klipstein-Grobusch K, Yeo SA, Browne JL, Moons KGM, Riley RD, Thangaratinam S. External validation of prognostic models predicting pre-eclampsia: individual participant data meta-analysis. BMC Med 2020; 18:302. [PMID: 33131506 PMCID: PMC7604970 DOI: 10.1186/s12916-020-01766-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Accepted: 08/26/2020] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND Pre-eclampsia is a leading cause of maternal and perinatal mortality and morbidity. Early identification of women at risk during pregnancy is required to plan management. Although there are many published prediction models for pre-eclampsia, few have been validated in external data. Our objective was to externally validate published prediction models for pre-eclampsia using individual participant data (IPD) from UK studies, to evaluate whether any of the models can accurately predict the condition when used within the UK healthcare setting. METHODS IPD from 11 UK cohort studies (217,415 pregnant women) within the International Prediction of Pregnancy Complications (IPPIC) pre-eclampsia network contributed to external validation of published prediction models, identified by systematic review. Cohorts that measured all predictor variables in at least one of the identified models and reported pre-eclampsia as an outcome were included for validation. We reported the model predictive performance as discrimination (C-statistic), calibration (calibration plots, calibration slope, calibration-in-the-large), and net benefit. Performance measures were estimated separately in each available study and then, where possible, combined across studies in a random-effects meta-analysis. RESULTS Of 131 published models, 67 provided the full model equation and 24 could be validated in 11 UK cohorts. Most of the models showed modest discrimination with summary C-statistics between 0.6 and 0.7. The calibration of the predicted compared to observed risk was generally poor for most models with observed calibration slopes less than 1, indicating that predictions were generally too extreme, although confidence intervals were wide. There was large between-study heterogeneity in each model's calibration-in-the-large, suggesting poor calibration of the predicted overall risk across populations. In a subset of models, the net benefit of using the models to inform clinical decisions appeared small and limited to probability thresholds between 5 and 7%. CONCLUSIONS The evaluated models had modest predictive performance, with key limitations such as poor calibration (likely due to overfitting in the original development datasets), substantial heterogeneity, and small net benefit across settings. The evidence to support the use of these prediction models for pre-eclampsia in clinical decision-making is limited. Any models that we could not validate should be examined in terms of their predictive performance, net benefit, and heterogeneity across multiple UK settings before consideration for use in practice. TRIAL REGISTRATION PROSPERO ID: CRD42015029349 .
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Affiliation(s)
- Kym I E Snell
- Centre for Prognosis Research, School of Primary, Community and Social Care, Keele University, Keele, UK.
| | - John Allotey
- Barts Research Centre for Women's Health (BARC), Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- Pragmatic Clinical Trials Unit, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Melanie Smuk
- Pragmatic Clinical Trials Unit, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Richard Hooper
- Pragmatic Clinical Trials Unit, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Claire Chan
- Pragmatic Clinical Trials Unit, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Asif Ahmed
- MirZyme Therapeutics, Innovation Birmingham Campus, Birmingham, UK
| | - Lucy C Chappell
- Department of Women and Children's Health, School of Life Course Sciences, King's College London, London, UK
| | - Peter Von Dadelszen
- Department of Women and Children's Health, School of Life Course Sciences, King's College London, London, UK
| | - Marcus Green
- Action on Pre-eclampsia (APEC) Charity, Worcestershire, UK
| | - Louise Kenny
- Faculty Health & Life Sciences, University of Liverpool, Liverpool, UK
| | - Asma Khalil
- Fetal Medicine Unit, St George's University Hospitals NHS Foundation Trust and Molecular and Clinical Sciences Research Institute, St George's University of London, London, UK
| | - Khalid S Khan
- Barts Research Centre for Women's Health (BARC), Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- Pragmatic Clinical Trials Unit, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Ben W Mol
- Department of Obstetrics and Gynaecology, Monash University, Monash Medical Centre, Clayton, Victoria, Australia
| | - Jenny Myers
- Maternal and Fetal Health Research Centre, Manchester Academic Health Science Centre, University of Manchester, Central Manchester NHS Trust, Manchester, UK
| | - Lucilla Poston
- Department of Women and Children's Health, School of Life Course Sciences, King's College London, London, UK
| | - Basky Thilaganathan
- Fetal Medicine Unit, St George's University Hospitals NHS Foundation Trust and Molecular and Clinical Sciences Research Institute, St George's University of London, London, UK
| | - Anne C Staff
- Division of Obstetrics and Gynaecology, Oslo University Hospital, and Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Gordon C S Smith
- Department of Obstetrics and Gynaecology, NIHR Biomedical Research Centre, Cambridge University, Cambridge, UK
| | - Wessel Ganzevoort
- Department of Obstetrics, Amsterdam UMC University of Amsterdam, Amsterdam, The Netherlands
| | - Hannele Laivuori
- Department of Medical and Clinical Genetics, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Department of Obstetrics and Gynecology, Faculty of Medicine and Health Technology, Tampere University Hospital and Tampere University, Tampere, Finland
| | | | - Javier Arenas Ramírez
- Department of Obstetrics and Gynaecology, University Hospital de Cabueñes, Gijón, Spain
| | - John Kingdom
- Maternal-Fetal Medicine Division, Department OBGYN, Mount Sinai Hospital, University of Toronto, Toronto, Canada
| | - George Daskalakis
- Department of Obstetrics and Gynecology, National and Kapodistrian University of Athens, Alexandra Hospital, Athens, Greece
| | - Diane Farrar
- Bradford Institute for Health Research, Bradford Teaching Hospitals, Bradford, UK
| | - Ahmet A Baschat
- Johns Hopkins Center for Fetal Therapy, Department of Gynecology & Obstetrics, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Paul T Seed
- Department of Women and Children's Health, School of Life Course Sciences, King's College London, London, UK
| | - Federico Prefumo
- Department of Obstetrics and Gynaecology, University of Brescia, Brescia, Italy
| | - Fabricio da Silva Costa
- Department of Gynecology and Obstetrics, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
| | - Henk Groen
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Francois Audibert
- Department of Obstetrics and Gynecology, CHU Ste Justine, Université de Montréal, Montreal, Canada
| | - Jacques Masse
- Department of Molecular Biology, Medical Biochemistry and Pathology, Laval University, Quebec City, Canada
| | - Ragnhild B Skråstad
- Department of Clinical and Molecular Medicine, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology - NTNU, Trondheim, Norway
- Department of Clinical Pharmacology, St. Olav University Hospital, Trondheim, Norway
| | - Kjell Å Salvesen
- Department of Obstetrics and Gynecology, Trondheim University Hospital, Trondheim, Norway
- Department of Laboratory Medicine, Children's and Women's Health, Norwegian University of Science and Technology, Trondheim, Norway
| | - Camilla Haavaldsen
- Department of Obstetrics and Gynaecology, Akershus University Hospital, Lørenskog, Norway
| | - Chie Nagata
- Department of Education for Clinical Research, National Center for Child Health and Development, Tokyo, Japan
| | - Alice R Rumbold
- South Australian Health and Medical Research Institute and Robinson Research Institute, The University of Adelaide, Adelaide, Australia
| | - Seppo Heinonen
- Department of Obstetrics and Gynaecology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Lisa M Askie
- NHMRC Clinical Trials Centre, University of Sydney, Sydney, Australia
| | - Luc J M Smits
- Care and Public Health Research Institute, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Christina A Vinter
- Department of Gynecology and Obstetrics, Odense University Hospital, University of Southern Denmark, Odense, Denmark
| | - Per Magnus
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Kajantie Eero
- National Institute for Health and Welfare, Helsinki, Finland
- Children's Hospital, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Pia M Villa
- Department of Obstetrics and Gynaecology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Anne K Jenum
- General Practice Research Unit (AFE), Department of General Practice, Institute of Health and Society, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Louise B Andersen
- Institute for Clinical Research, University of Southern Denmark, Odense, Denmark
- Department of Obstetrics and Gynecology, Odense University Hospital, Odense, Denmark
| | - Jane E Norman
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh, UK
| | - Akihide Ohkuchi
- Department of Obstetrics and Gynecology, Jichi Medical University School of Medicine, Shimotsuke-shi, Tochigi, Japan
| | - Anne Eskild
- Department of Obstetrics and Gynaecology, Akershus University Hospital, Lørenskog, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Sohinee Bhattacharya
- Obstetrics & Gynaecology, Institute of Applied Health Sciences, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK
| | - Fionnuala M McAuliffe
- UCD Perinatal Research Centre, School of Medicine, University College Dublin, National Maternity Hospital, Dublin, Ireland
| | - Alberto Galindo
- Fetal Medicine Unit, Maternal and Child Health and Development Network (SAMID), Department of Obstetrics and Gynaecology, Hospital Universitario, Instituto de Investigación Hospital, Universidad Complutense de Madrid, Madrid, Spain
| | - Ignacio Herraiz
- Fetal Medicine Unit, Maternal and Child Health and Development Network (SAMID), Department of Obstetrics and Gynaecology, Hospital Universitario, Instituto de Investigación Hospital, Universidad Complutense de Madrid, Madrid, Spain
| | - Lionel Carbillon
- Department of Obstetrics and Gynecology, Assistance Publique-Hôpitaux de Paris Université Paris, Paris, France
| | - Kerstin Klipstein-Grobusch
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Seon Ae Yeo
- University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Joyce L Browne
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Karel G M Moons
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands
- Cochrane Netherlands, Utrecht, The Netherlands
| | - Richard D Riley
- Centre for Prognosis Research, School of Primary, Community and Social Care, Keele University, Keele, UK
| | - Shakila Thangaratinam
- Institute of Metabolism and Systems Research, WHO Collaborating Centre for Women's Health, University of Birmingham, Birmingham, UK
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van Rijn MHC, van de Luijtgaarden M, van Zuilen AD, Blankestijn PJ, Wetzels JFM, Debray TPA, van den Brand JAJG. Prognostic models for chronic kidney disease: a systematic review and external validation. Nephrol Dial Transplant 2020; 36:1837-1850. [PMID: 33051669 DOI: 10.1093/ndt/gfaa155] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Accurate risk prediction is needed in order to provide personalized healthcare for chronic kidney disease (CKD) patients. An overload of prognosis studies is being published, ranging from individual biomarker studies to full prediction studies. We aim to systematically appraise published prognosis studies investigating multiple biomarkers and their role in risk predictions. Our primary objective was to investigate if the prognostic models that are reported in the literature were of sufficient quality and to externally validate them. METHODS We undertook a systematic review and appraised the quality of studies reporting multivariable prognosis models for end-stage renal disease (ESRD), cardiovascular (CV) events and mortality in CKD patients. We subsequently externally validated these models in a randomized trial that included patients from a broad CKD population. RESULTS We identified 91 papers describing 36 multivariable models for prognosis of ESRD, 50 for CV events, 46 for mortality and 17 for a composite outcome. Most studies were deemed of moderate quality. Moreover, they often adopted different definitions for the primary outcome and rarely reported full model equations (21% of the included studies). External validation was performed in the Multifactorial Approach and Superior Treatment Efficacy in Renal Patients with the Aid of Nurse Practitioners trial (n = 788, with 160 events for ESRD, 79 for CV and 102 for mortality). The 24 models that reported full model equations showed a great variability in their performance, although calibration remained fairly adequate for most models, except when predicting mortality (calibration slope >1.5). CONCLUSIONS This review shows that there is an abundance of multivariable prognosis models for the CKD population. Most studies were considered of moderate quality, and they were reported and analysed in such a manner that their results cannot directly be used in follow-up research or in clinical practice.
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Affiliation(s)
- Marieke H C van Rijn
- Department of Nephrology, Radboud Institute of Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Moniek van de Luijtgaarden
- Department of Nephrology, Radboud Institute of Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Arjan D van Zuilen
- Department of Nephrology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Peter J Blankestijn
- Department of Nephrology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Jack F M Wetzels
- Department of Nephrology, Radboud Institute of Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Thomas P A Debray
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Jan A J G van den Brand
- Department of Nephrology, Radboud Institute of Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
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Furukawa TA, Debray TPA, Akechi T, Yamada M, Kato T, Seo M, Efthimiou O. Can personalized treatment prediction improve the outcomes, compared with the group average approach, in a randomized trial? Developing and validating a multivariable prediction model in a pragmatic megatrial of acute treatment for major depression. J Affect Disord 2020; 274:690-697. [PMID: 32664003 DOI: 10.1016/j.jad.2020.05.141] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Revised: 03/25/2020] [Accepted: 05/26/2020] [Indexed: 02/09/2023]
Abstract
BACKGROUND Clinical trials have traditionally been analysed at the aggregate level, assuming that the group average would be applicable to all eligible and similar patients. We re-analyzed a mega-trial of antidepressant therapy for major depression to explore whether a multivariable prediction model may lead to different treatment recommendations for individual participants. METHODS The trial compared the second-line treatment strategies of continuing sertraline, combining it with mirtazapine or switching to mirtazapine after initial failure to remit on sertraline among 1,544 patients with major depression. The outcome was the Personal Health Questionnaire-9 (PHQ-9) at week 9: the original analyses showed that both combining and switching resulted in greater reduction in PHQ-9 by 1.0 point than continuing. We considered several models of penalized regression or machine learning. RESULTS Models using support vector machines (SVMs) provided the best performance. Using SVMs, continuing sertraline was predicted to be the best treatment for 123 patients, combining for 696 patients, and switching for 725 patients. In the last two subgroups, both combining and switching were equally superior to continuing by 1.2 to 1.4 points, resulting in the same treatment recommendations as with the original aggregate data level analyses; in the first subgroup, however, switching was substantively inferior to combining (-3.1, 95%CI: -5.4 to -0.5). LIMITATIONS Stronger predictors are needed to make more precise predictions. CONCLUSIONS The multivariable prediction models led to improved recommendations for a minority of participants than the group average approach in a megatrial.
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Affiliation(s)
- Toshi A Furukawa
- Departments of Health Promotion and Human Behavior and of Clinical Epidemiology, Kyoto University Graduate School of Medicine / School of Public Health, Kyoto, Japan.
| | - Thomas P A Debray
- Julius Center for Health Sciences and Primary Care, UMC Utrecht, Utrecht University, The Netherlands.
| | - Tatsuo Akechi
- Department of Psychiatry and Cognitive-Behavioral Medicine, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan.
| | - Mitsuhiko Yamada
- Department of Neuropsychopharmacology, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan.
| | | | - Michael Seo
- Institute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland.
| | - Orestis Efthimiou
- Institute of Social and Preventive Medicine, University of Bern, Switzerland.
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50
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Kemp J, Barker D, Benito K, Herren J, Freeman J. Moderators of Psychosocial Treatment for Pediatric Obsessive-Compulsive Disorder: Summary and Recommendations for Future Directions. JOURNAL OF CLINICAL CHILD AND ADOLESCENT PSYCHOLOGY 2020; 50:478-485. [DOI: 10.1080/15374416.2020.1790378] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Affiliation(s)
- Joshua Kemp
- Pediatric Anxiety Research Center, Alpert Medical School of Brown University
| | - David Barker
- Pediatric Anxiety Research Center, Alpert Medical School of Brown University
| | - Kristen Benito
- Pediatric Anxiety Research Center, Alpert Medical School of Brown University
| | - Jennifer Herren
- Pediatric Anxiety Research Center, Alpert Medical School of Brown University
| | - Jennifer Freeman
- Pediatric Anxiety Research Center, Alpert Medical School of Brown University
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