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Nayak SP, Sánchez-Rosado M, Reis JD, Brown LS, Mangona KL, Sharma P, Nelson DB, Wyckoff MH, Pandya S, Mir IN, Brion LP. Development of a Prediction Model for Surgery or Early Mortality at the Time of Initial Assessment for Necrotizing Enterocolitis. Am J Perinatol 2024; 41:1714-1727. [PMID: 38272063 DOI: 10.1055/a-2253-8656] [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] [Indexed: 01/27/2024]
Abstract
OBJECTIVE No available scale, at the time of initial evaluation for necrotizing enterocolitis (NEC), accurately predicts, that is, with an area under the curve (AUC) ≥0.9, which preterm infants will undergo surgery for NEC stage III or die within a week. STUDY DESIGN This is a retrospective cohort study (n = 261) of preterm infants with <33 weeks' gestation or <1,500 g birth weight with either suspected or with definite NEC born at Parkland Hospital between 2009 and 2021. A prediction model using the new HASOFA score (Hyperglycemia, Hyperkalemia, use of inotropes for Hypotension during the prior week, Acidemia, Neonatal Sequential Organ Failure Assessment [nSOFA] score) was compared with a similar model using the nSOFA score. RESULTS Among 261 infants, 112 infants had NEC stage I, 68 with NEC stage II, and 81 with NEC stage III based on modified Bell's classification. The primary outcome, surgery for NEC stage III or death within a week, occurred in 81 infants (surgery in 66 infants and death in 38 infants). All infants with pneumoperitoneum or abdominal compartment syndrome either died or had surgery. The HASOFA and the nSOFA scores were evaluated in 254 and 253 infants, respectively, at the time of the initial workup for NEC. Both models were internally validated. The HASOFA model was a better predictor of surgery for NEC stage III or death within a week than the nSOFA model, with greater AUC 0.909 versus 0.825, respectively, p < 0.001. Combining HASOFA at initial assessment with concurrent or later presence of abdominal wall erythema or portal gas improved the prediction surgery for NEC stage III or death with AUC 0.942 or 0.956, respectively. CONCLUSION Using this new internally validated prediction model, surgery for NEC stage III or death within a week can be accurately predicted at the time of initial assessment for NEC. KEY POINTS · No available scale, at initial evaluation, accurately predicts which preterm infants will undergo surgery for NEC stage III or die within a week.. · In this retrospective cohort study of 261 preterm infants with either suspected or definite NEC we developed a new prediction model (HASOFA score).. · The HASOFA-model had high discrimination (AUC: 0.909) and excellent calibration and was internally validated..
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Affiliation(s)
- Sujir P Nayak
- Division of Neonatal-Perinatal Medicine, Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Mariela Sánchez-Rosado
- Division of Neonatal-Perinatal Medicine, Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, Texas
- Division of Neonatology, Joe DiMaggio Children's Hospital, Hollywood, Florida
| | - Jordan D Reis
- Division of Neonatal-Perinatal Medicine, Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, Texas
- Department of Pediatrics, Baylor Scott and White, Dallas, Texas
| | - L Steven Brown
- Department of Research, Parkland Health and Hospital System, Dallas, Texas
| | - Kate L Mangona
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Priya Sharma
- Division of Neonatal-Perinatal Medicine, Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, Texas
- Department of Pediatrics, Baylor Scott and White, Dallas, Texas
| | - David B Nelson
- Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, and Parkland Health, Dallas, Texas
| | - Myra H Wyckoff
- Division of Neonatal-Perinatal Medicine, Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Samir Pandya
- Division of Pediatric Surgery, Department of Surgery, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Imran N Mir
- Division of Neonatal-Perinatal Medicine, Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Luc P Brion
- Division of Neonatal-Perinatal Medicine, Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, Texas
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Zhuo XY, Lei SH, Sun L, Bai YW, Wu J, Zheng YJ, Liu KX, Liu WF, Zhao BC. Preoperative risk prediction models for acute kidney injury after noncardiac surgery: an independent external validation cohort study. Br J Anaesth 2024; 133:508-518. [PMID: 38527923 DOI: 10.1016/j.bja.2024.02.018] [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: 08/30/2023] [Revised: 02/24/2024] [Accepted: 02/27/2024] [Indexed: 03/27/2024] Open
Abstract
BACKGROUND Numerous models have been developed to predict acute kidney injury (AKI) after noncardiac surgery, yet there is a lack of independent validation and comparison among them. METHODS We conducted a systematic literature search to review published risk prediction models for AKI after noncardiac surgery. An independent external validation was performed using a retrospective surgical cohort at a large Chinese hospital from January 2019 to October 2022. The cohort included patients undergoing a wide range of noncardiac surgeries with perioperative creatinine measurements. Postoperative AKI was defined according to the Kidney Disease Improving Global Outcomes creatinine criteria. Model performance was assessed in terms of discrimination (area under the receiver operating characteristic curve, AUROC), calibration (calibration plot), and clinical utility (net benefit), before and after model recalibration through intercept and slope updates. A sensitivity analysis was conducted by including patients without postoperative creatinine measurements in the validation cohort and categorising them as non-AKI cases. RESULTS Nine prediction models were evaluated, each with varying clinical and methodological characteristics, including the types of surgical cohorts used for model development, AKI definitions, and predictors. In the validation cohort involving 13,186 patients, 650 (4.9%) developed AKI. Three models demonstrated fair discrimination (AUROC between 0.71 and 0.75); other models had poor or failed discrimination. All models exhibited some miscalibration; five of the nine models were well-calibrated after intercept and slope updates. Decision curve analysis indicated that the three models with fair discrimination consistently provided a positive net benefit after recalibration. The results were confirmed in the sensitivity analysis. CONCLUSIONS We identified three models with fair discrimination and potential clinical utility after recalibration for assessing the risk of acute kidney injury after noncardiac surgery.
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Affiliation(s)
- Xiao-Yu Zhuo
- Department of Anaesthesiology, Nanfang Hospital, Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Precision Anaesthesia and Perioperative Organ Protection, Guangzhou, China
| | - Shao-Hui Lei
- Department of Anaesthesiology, Nanfang Hospital, Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Precision Anaesthesia and Perioperative Organ Protection, Guangzhou, China; College of Anaesthesiology, Southern Medical University, Guangzhou, China
| | - Lan Sun
- Department of Anaesthesiology, Nanfang Hospital, Southern Medical University, Guangzhou, China; Department of Biostatistics, Lejiu Healthcare Technology Co., Ltd, Hangzhou, China
| | - Ya-Wen Bai
- College of Anaesthesiology, Southern Medical University, Guangzhou, China
| | - Jiao Wu
- College of Anaesthesiology, Southern Medical University, Guangzhou, China
| | - Yong-Jia Zheng
- College of Anaesthesiology, Southern Medical University, Guangzhou, China
| | - Ke-Xuan Liu
- Department of Anaesthesiology, Nanfang Hospital, Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Precision Anaesthesia and Perioperative Organ Protection, Guangzhou, China; College of Anaesthesiology, Southern Medical University, Guangzhou, China; Outcomes Research Consortium, Cleveland, OH, USA.
| | - Wei-Feng Liu
- Department of Anaesthesiology, Nanfang Hospital, Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Precision Anaesthesia and Perioperative Organ Protection, Guangzhou, China; College of Anaesthesiology, Southern Medical University, Guangzhou, China.
| | - Bing-Cheng Zhao
- Department of Anaesthesiology, Nanfang Hospital, Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Precision Anaesthesia and Perioperative Organ Protection, Guangzhou, China; College of Anaesthesiology, Southern Medical University, Guangzhou, China; Outcomes Research Consortium, Cleveland, OH, USA.
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3
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Song MH, Xiang BX, Yang CY, Lee CH, Yan YX, Yang QJ, Yin WJ, Zhou Y, Zuo XC, Xie YL. A pilot clinical risk model to predict polymyxin-induced nephrotoxicity: a real-world, retrospective cohort study. J Antimicrob Chemother 2024; 79:1919-1928. [PMID: 38946304 DOI: 10.1093/jac/dkae185] [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: 03/04/2024] [Accepted: 05/21/2024] [Indexed: 07/02/2024] Open
Abstract
OBJECTIVES Polymyxin-induced nephrotoxicity (PIN) is a major safety concern and challenge in clinical practice, which limits the clinical use of polymyxins. This study aims to investigate the risk factors and to develop a scoring tool for the early prediction of PIN. METHODS Data on critically ill patients who received intravenous polymyxin B or colistin sulfate for over 24 h were collected. Logistic regression with the least absolute shrinkage and selection operator (LASSO) was used to identify variables that are associated with outcomes. The eXtreme Gradient Boosting (XGB) classifier algorithm was used to further visualize factors with significant differences. A prediction model for PIN was developed through binary logistic regression analysis and the model was assessed by temporal validation and external validation. Finally, a risk-scoring system was developed based on the prediction model. RESULTS Of 508 patients, 161 (31.6%) patients developed PIN. Polymyxin type, loading dose, septic shock, concomitant vasopressors and baseline blood urea nitrogen (BUN) level were identified as significant predictors of PIN. All validation exhibited great discrimination, with the AUC of 0.742 (95% CI: 0.696-0.787) for internal validation, of 0.708 (95% CI: 0.605-0.810) for temporal validation and of 0.874 (95% CI: 0.759-0.989) for external validation, respectively. A simple risk-scoring tool was developed with a total risk score ranging from -3 to 4, corresponding to a risk of PIN from 0.79% to 81.24%. CONCLUSIONS This study established a prediction model for PIN. Before using polymyxins, the simple risk-scoring tool can effectively identify patients at risk of developing PIN within a range of 7% to 65%.
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Affiliation(s)
- Mong-Hsiu Song
- Department of Pharmacy, The Third Xiangya Hospital of Central South University, Changsha, Hunan 410013, China
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, Hunan 410013, China
| | - Bi-Xiao Xiang
- Department of Pharmacy, The Third Xiangya Hospital of Central South University, Changsha, Hunan 410013, China
- College of Pharmacy, Zunyi Medical University, Zunyi, Guizhou 563003, China
| | - Chien-Yi Yang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, Hunan 410013, China
| | - Chou-Hsi Lee
- Xiangya School of Medicine, Central South University, Changsha, Hunan 410013, China
| | - Yu-Xuan Yan
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, Hunan 410013, China
| | - Qin-Jie Yang
- Department of Pharmacy, The Third Xiangya Hospital of Central South University, Changsha, Hunan 410013, China
| | - Wen-Jun Yin
- Department of Pharmacy, The Third Xiangya Hospital of Central South University, Changsha, Hunan 410013, China
- Department of Pharmacy and Center of Clinical Pharmacology, The Third Xiangya Hospital, Central South University, Changsha, Hunan 410013, China
| | - Yangang Zhou
- Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China
| | - Xiao-Cong Zuo
- Department of Pharmacy, The Third Xiangya Hospital of Central South University, Changsha, Hunan 410013, China
- Department of Pharmacy and Center of Clinical Pharmacology, The Third Xiangya Hospital, Central South University, Changsha, Hunan 410013, China
| | - Yue-Liang Xie
- Department of Pharmacy, The Third Xiangya Hospital of Central South University, Changsha, Hunan 410013, China
- Department of Pharmacy and Center of Clinical Pharmacology, The Third Xiangya Hospital, Central South University, Changsha, Hunan 410013, China
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Siegler JE, Koneru M, Qureshi MM, Doheim M, Nogueira RG, Martinez‐Majander N, Nagel S, Penckofer M, Demeestere J, Puetz V, Ribo M, Abdalkader M, Marto JP, Al‐Bayati AR, Yamagami H, Haussen DC, Olive‐Gadea M, Winzer S, Mohammaden MH, Lemmens R, Tanaka K, Virtanen P, Dusart A, Bellante F, Kaiser DPO, Caparros F, Henon H, Ramos JN, Ortega‐Gutierrez S, Sheth SA, Nannoni S, Vandewalle L, Kaesmacher J, Salazar‐Marioni S, Tomppo L, Ventura R, Zaidi SF, Jumaa M, Castonguay AC, Galecio‐Castillo M, Puri AS, Mujanovic A, Klein P, Shu L, Farzin B, Moomey H, Masoud HE, Jesser J, Möhlenbruch MA, Ringleb PA, Strbian D, Zaidat OO, Yaghi S, Strambo D, Michel P, Roy D, Yoshimura S, Uchida K, Raymond J, Nguyen TN. CLEAR Thrombectomy Score: An Index to Estimate the Probability of Good Functional Outcome With or Without Endovascular Treatment in the Late Window for Anterior Circulation Occlusion. J Am Heart Assoc 2024; 13:e034948. [PMID: 38979812 PMCID: PMC11292751 DOI: 10.1161/jaha.124.034948] [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: 02/09/2024] [Accepted: 06/07/2024] [Indexed: 07/10/2024]
Abstract
BACKGROUND With the expanding eligibility for endovascular therapy (EVT) of patients presenting in the late window (6-24 hours after last known well), we aimed to derive a score to predict favorable outcomes associated with EVT versus best medical management. METHODS AND RESULTS A multinational observational cohort of patients from the CLEAR (Computed Tomography for Late Endovascular Reperfusion) study with proximal intracranial occlusion (2014-2022) was queried (n=58 sites). Logistic regression analyses were used to derive a 9-point score for predicting good functional outcome (modified Rankin Scale score 0-2 or return to premorbid modified Rankin Scale score) at 90 days, with sensitivity analyses for prespecified subgroups conducted using bootstrapped random forest regressions. Secondary outcomes included 90-day functional independence (modified Rankin Scale score 0-2), poor outcome (modified Rankin Scale score 5-6), and 90-day survival. The score was externally validated with a single-center cohort (2014-2023). Of the 3231 included patients (n=2499 EVT), a 9-point score included age, early computed tomography ischemic changes, and stroke severity, with higher points indicating a higher probability of a good functional outcome. The areas under the curve for the primary outcome among EVT and best medical management subgroups were 0.72 (95% CI, 0.70-0.74) and 0.87 (95% CI, 0.84-0.90), respectively, with similar performance in the external validation cohort (area under the curve, 0.71 [95% CI, 0.66-0.76]). There was a significant interaction between the score and EVT for good functional outcome, functional independence, and poor outcome (all Pinteraction<0.001), with greater benefit favoring patients with lower and midrange scores. CONCLUSIONS This score is a pragmatic tool that can estimate the probability of a good outcome with EVT in the late window. REGISTRATION URL: https://www.Clinicaltrials.gov; Unique identifier: NCT04096248.
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Blanco-Pintos T, Regueira-Iglesias A, Relvas M, Alonso-Sampedro M, Chantada-Vázquez MP, Balsa-Castro C, Tomás I. Using SWATH-MS to identify new molecular biomarkers in gingival crevicular fluid for detecting periodontitis and its response to treatment. J Clin Periodontol 2024. [PMID: 38987231 DOI: 10.1111/jcpe.14037] [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: 01/10/2024] [Revised: 05/12/2024] [Accepted: 06/10/2024] [Indexed: 07/12/2024]
Abstract
AIM To identify new biomarkers to detect untreated and treated periodontitis in gingival crevicular fluid (GCF) using sequential window acquisition of all theoretical mass spectra (SWATH-MS). MATERIALS AND METHODS GCF samples were collected from 44 periodontally healthy subjects and 40 with periodontitis (Stages III-IV). In the latter, 25 improved clinically 2 months after treatment. Samples were analysed using SWATH-MS, and proteins were identified by the UniProt human-specific database. The diagnostic capability of the proteins was determined with generalized additive models to distinguish the three clinical conditions. RESULTS In the untreated periodontitis vs. periodontal health modelling, five proteins showed excellent or good bias-corrected (bc)-sensitivity/bc-specificity values of >80%. These were GAPDH, ZG16B, carbonic anhydrase 1, plasma protease inhibitor C1 and haemoglobin subunit beta. GAPDH with MMP-9, MMP-8, zinc-α-2-glycoprotein and neutrophil gelatinase-associated lipocalin and ZG16B with cornulin provided increased bc-sensitivity/bc-specificity of >95%. For distinguishing treated periodontitis vs. periodontal health, most of these proteins and their combinations revealed a predictive ability similar to previous modelling. No model obtained relevant results to differentiate between periodontitis conditions. CONCLUSIONS New single and dual GCF protein biomarkers showed outstanding results in discriminating untreated and treated periodontitis from periodontal health. Periodontitis conditions were indistinguishable. Future research must validate these findings.
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Affiliation(s)
- T Blanco-Pintos
- Oral Sciences Research Group, Special Needs Unit, Department of Surgery and Medical-Surgical Specialties, School of Medicine and Dentistry, Universidade de Santiago de Compostela, Health Research Institute of Santiago (IDIS), Santiago de Compostela, Spain
| | - A Regueira-Iglesias
- Oral Sciences Research Group, Special Needs Unit, Department of Surgery and Medical-Surgical Specialties, School of Medicine and Dentistry, Universidade de Santiago de Compostela, Health Research Institute of Santiago (IDIS), Santiago de Compostela, Spain
| | - M Relvas
- Oral Pathology and Rehabilitation Research Unit (UNIPRO), University Institute of Health Sciences (IUCS-CESPU), Gandra, Portugal
| | - M Alonso-Sampedro
- Department of Internal Medicine and Clinical Epidemiology, Complejo Hospitalario Universitario, Health Research Institute of Santiago (IDIS), Santiago de Compostela, Spain
| | - M P Chantada-Vázquez
- Proteomic Unit, Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain
| | - C Balsa-Castro
- Oral Sciences Research Group, Special Needs Unit, Department of Surgery and Medical-Surgical Specialties, School of Medicine and Dentistry, Universidade de Santiago de Compostela, Health Research Institute of Santiago (IDIS), Santiago de Compostela, Spain
| | - I Tomás
- Oral Sciences Research Group, Special Needs Unit, Department of Surgery and Medical-Surgical Specialties, School of Medicine and Dentistry, Universidade de Santiago de Compostela, Health Research Institute of Santiago (IDIS), Santiago de Compostela, Spain
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Collins GS, Moons KGM, Dhiman P, Riley RD, Beam AL, Van Calster B, Ghassemi M, Liu X, Reitsma JB, van Smeden M, Boulesteix AL, Camaradou JC, Celi LA, Denaxas S, Denniston AK, Glocker B, Golub RM, Harvey H, Heinze G, Hoffman MM, Kengne AP, Lam E, Lee N, Loder EW, Maier-Hein L, Mateen BA, McCradden MD, Oakden-Rayner L, Ordish J, Parnell R, Rose S, Singh K, Wynants L, Logullo P. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ 2024; 385:e078378. [PMID: 38626948 PMCID: PMC11019967 DOI: 10.1136/bmj-2023-078378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/17/2024] [Indexed: 04/19/2024]
Affiliation(s)
- Gary S Collins
- Centre for Statistics in Medicine, UK EQUATOR Centre, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
| | - Karel G M Moons
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Paula Dhiman
- Centre for Statistics in Medicine, UK EQUATOR Centre, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
| | - Richard D Riley
- Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
- National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, Birmingham, UK
| | - Andrew L Beam
- Department of Epidemiology, Harvard T H Chan School of Public Health, Boston, MA, USA
| | - Ben Van Calster
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- Department of Biomedical Data Science, Leiden University Medical Centre, Leiden, Netherlands
| | - Marzyeh Ghassemi
- Department of Electrical Engineering and Computer Science, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Xiaoxuan Liu
- Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Johannes B Reitsma
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Maarten van Smeden
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Anne-Laure Boulesteix
- Institute for Medical Information Processing, Biometry and Epidemiology, Faculty of Medicine, Ludwig-Maximilians-University of Munich and Munich Centre of Machine Learning, Germany
| | - Jennifer Catherine Camaradou
- Patient representative, Health Data Research UK patient and public involvement and engagement group
- Patient representative, University of East Anglia, Faculty of Health Sciences, Norwich Research Park, Norwich, UK
| | - Leo Anthony Celi
- Beth Israel Deaconess Medical Center, Boston, MA, USA
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Biostatistics, Harvard T H Chan School of Public Health, Boston, MA, USA
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, UK
- British Heart Foundation Data Science Centre, London, UK
| | - Alastair K Denniston
- National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, Birmingham, UK
- Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
| | - Ben Glocker
- Department of Computing, Imperial College London, London, UK
| | - Robert M Golub
- Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | | | - Georg Heinze
- Section for Clinical Biometrics, Centre for Medical Data Science, Medical University of Vienna, Vienna, Austria
| | - Michael M Hoffman
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Vector Institute for Artificial Intelligence, Toronto, ON, Canada
| | | | - Emily Lam
- Patient representative, Health Data Research UK patient and public involvement and engagement group
| | - Naomi Lee
- National Institute for Health and Care Excellence, London, UK
| | - Elizabeth W Loder
- The BMJ, London, UK
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Lena Maier-Hein
- Department of Intelligent Medical Systems, German Cancer Research Centre, Heidelberg, Germany
| | - Bilal A Mateen
- Institute of Health Informatics, University College London, London, UK
- Wellcome Trust, London, UK
- Alan Turing Institute, London, UK
| | - Melissa D McCradden
- Department of Bioethics, Hospital for Sick Children Toronto, ON, Canada
- Genetics and Genome Biology, SickKids Research Institute, Toronto, ON, Canada
| | - Lauren Oakden-Rayner
- Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia
| | - Johan Ordish
- Medicines and Healthcare products Regulatory Agency, London, UK
| | - Richard Parnell
- Patient representative, Health Data Research UK patient and public involvement and engagement group
| | - Sherri Rose
- Department of Health Policy and Center for Health Policy, Stanford University, Stanford, CA, USA
| | - Karandeep Singh
- Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Maastricht, Netherlands
| | - Laure Wynants
- Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Maastricht, Netherlands
| | - Patricia Logullo
- Centre for Statistics in Medicine, UK EQUATOR Centre, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
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7
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Boyd LNC, Ali M, Comandatore A, Brandi G, Tavolari S, Gaeta R, Meijer LL, Le Large TYS, Riefolo M, Vasuri F, Morelli L, van Laarhoven HWM, Giovannetti E, Kazemier G, Garajová I. Prognostic value of microRNA-21 in intra- and extrahepatic cholangiocarcinoma after radical resection: cohort study. BJS Open 2024; 8:zrae031. [PMID: 38637298 PMCID: PMC11026099 DOI: 10.1093/bjsopen/zrae031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 01/12/2024] [Accepted: 02/25/2024] [Indexed: 04/20/2024] Open
Affiliation(s)
- Lenka N C Boyd
- Department of Surgery, Amsterdam UMC, Location Vrije Universiteit, Amsterdam, The Netherlands
- Department of Medical Oncology, Lab of Medical Oncology, Amsterdam UMC, Location Vrije Universiteit, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | - Mahsoem Ali
- Department of Surgery, Amsterdam UMC, Location Vrije Universiteit, Amsterdam, The Netherlands
- Department of Medical Oncology, Lab of Medical Oncology, Amsterdam UMC, Location Vrije Universiteit, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | - Annalisa Comandatore
- Department of Surgery, Amsterdam UMC, Location Vrije Universiteit, Amsterdam, The Netherlands
- Department of Medical Oncology, Lab of Medical Oncology, Amsterdam UMC, Location Vrije Universiteit, Amsterdam, The Netherlands
- General Surgery Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Giovanni Brandi
- Center for Applied Biomedical Research, Sant'Orsola-Malpighi University Hospital, Bologna, Italy
| | - Simona Tavolari
- Center for Applied Biomedical Research, Sant'Orsola-Malpighi University Hospital, Bologna, Italy
| | - Raffaele Gaeta
- Second Division of Surgical Pathology, University Hospital of Pisa, Pisa, Italy
| | - Laura L Meijer
- Department of Surgery, Amsterdam UMC, Location Vrije Universiteit, Amsterdam, The Netherlands
| | - Tessa Y S Le Large
- Department of Surgery, Amsterdam UMC, Location Vrije Universiteit, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | - Mattia Riefolo
- Pathology Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Francesco Vasuri
- Pathology Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Luca Morelli
- General Surgery Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Hanneke W M van Laarhoven
- Department of Medical Oncology, Lab of Medical Oncology, Amsterdam UMC, Location Vrije Universiteit, Amsterdam, The Netherlands
- Department of Medical Oncology, Amsterdam UMC, Location University of Amsterdam, Amsterdam, The Netherlands
| | - Elisa Giovannetti
- Department of Medical Oncology, Lab of Medical Oncology, Amsterdam UMC, Location Vrije Universiteit, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
- Cancer Pharmacology Lab, Fondazione Pisa per la Scienza, Pisa, Italy
| | - Geert Kazemier
- Department of Surgery, Amsterdam UMC, Location Vrije Universiteit, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | - Ingrid Garajová
- Department of Medical Oncology, Lab of Medical Oncology, Amsterdam UMC, Location Vrije Universiteit, Amsterdam, The Netherlands
- Medical Oncology Unit, University Hospital of Parma, Parma, Italy
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Kyriacou C, Ledger A, Bobdiwala S, Ayim F, Kirk E, Abughazza O, Guha S, Vathanan V, Gould D, Timmerman D, Van Calster B, Bourne T. Updating M6 pregnancy of unknown location risk-prediction model including evaluation of clinical factors. ULTRASOUND IN OBSTETRICS & GYNECOLOGY : THE OFFICIAL JOURNAL OF THE INTERNATIONAL SOCIETY OF ULTRASOUND IN OBSTETRICS AND GYNECOLOGY 2024; 63:408-418. [PMID: 37842861 DOI: 10.1002/uog.27515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 09/19/2023] [Accepted: 10/05/2023] [Indexed: 10/17/2023]
Abstract
OBJECTIVES Ectopic pregnancy (EP) is a major high-risk outcome following a pregnancy of unknown location (PUL) classification. Biochemical markers are used to triage PUL as high vs low risk to guide appropriate follow-up. The M6 model is currently the best risk-prediction model. We aimed to update the M6 model and evaluate whether performance can be improved by including clinical factors. METHODS This prospective cohort study recruited consecutive PUL between January 2015 and January 2017 at eight units (Phase 1), with two centers continuing recruitment between January 2017 and March 2021 (Phase 2). Serum samples were collected routinely and sent for β-human chorionic gonadotropin (β-hCG) and progesterone measurement. Clinical factors recorded were maternal age, pain score, bleeding score and history of EP. Based on transvaginal ultrasonography and/or biochemical confirmation during follow-up, PUL were classified subsequently as failed PUL (FPUL), intrauterine pregnancy (IUP) or EP (including persistent PUL (PPUL)). The M6 models with (M6P ) and without (M6NP ) progesterone were refitted and extended with clinical factors. Model validation was performed using internal-external cross-validation (IECV) (Phase 1) and temporal external validation (EV) (Phase 2). Missing values were handled using multiple imputation. RESULTS Overall, 5473 PUL were recruited over both phases. A total of 709 PUL were excluded because maternal age was < 16 years or initial β-hCG was ≤ 25 IU/L, leaving 4764 (87%) PUL for analysis (2894 in Phase 1 and 1870 in Phase 2). For the refitted M6P model, the area under the receiver-operating-characteristics curve (AUC) for EP/PPUL vs IUP/FPUL was 0.89 for IECV and 0.84-0.88 for EV, with respective sensitivities of 94% and 92-93%. For the refitted M6NP model, the AUCs were 0.85 for IECV and 0.82-0.86 for EV, with respective sensitivities of 92% and 93-94%. Calibration performance was good overall, but with heterogeneity between centers. Net Benefit confirmed clinical utility. The change in AUC when M6P was extended to include maternal age, bleeding score and history of EP was between -0.02 and 0.01, depending on center and phase. The corresponding change in AUC when M6NP was extended was between -0.01 and 0.03. At the 5% threshold to define high risk of EP/PPUL, extending M6P altered sensitivity by -0.02 to -0.01, specificity by 0.03 to 0.04 and Net Benefit by -0.005 to 0.006. Extending M6NP altered sensitivity by -0.03 to -0.01, specificity by 0.05 to 0.07 and Net Benefit by -0.005 to 0.006. CONCLUSIONS The updated M6 model offers accurate diagnostic performance, with excellent sensitivity for EP. Adding clinical factors to the model improved performance in some centers, especially when progesterone levels were not suitable or unavailable. © 2023 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)
- C Kyriacou
- Tommy's National Centre for Miscarriage Research, Queen Charlotte's and Chelsea Hospital, Imperial College London, London, UK
| | - A Ledger
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
| | - S Bobdiwala
- Tommy's National Centre for Miscarriage Research, Queen Charlotte's and Chelsea Hospital, Imperial College London, London, UK
| | - F Ayim
- Department of Gynaecology, Hillingdon Hospital NHS Trust, London, UK
| | - E Kirk
- Department of Gynaecology, Royal Free NHS Foundation Trust, London, UK
| | - O Abughazza
- Department of Gynaecology, Royal Surrey County Hospital, Guildford, UK
| | - S Guha
- Department of Gynaecology, Chelsea and Westminster NHS Trust, London, UK
| | - V Vathanan
- Department of Gynaecology, Wexham Park Hospital, London, UK
| | - D Gould
- Department of Gynaecology, St Mary's Hospital, London, UK
| | - D Timmerman
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- Department of Gynecology, University Hospital Leuven, Leuven, Belgium
| | - B Van Calster
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - T Bourne
- Tommy's National Centre for Miscarriage Research, Queen Charlotte's and Chelsea Hospital, Imperial College London, London, UK
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- Department of Gynecology, University Hospital Leuven, Leuven, Belgium
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Collins GS, Dhiman P, Ma J, Schlussel MM, Archer L, Van Calster B, Harrell FE, Martin GP, Moons KGM, van Smeden M, Sperrin M, Bullock GS, Riley RD. Evaluation of clinical prediction models (part 1): from development to external validation. BMJ 2024; 384:e074819. [PMID: 38191193 PMCID: PMC10772854 DOI: 10.1136/bmj-2023-074819] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/04/2023] [Indexed: 01/10/2024]
Affiliation(s)
- Gary S Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
| | - Paula Dhiman
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
| | - Jie Ma
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
| | - Michael M Schlussel
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
| | - Lucinda Archer
- Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
- National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, UK
| | - Ben Van Calster
- KU Leuven, Department of Development and Regeneration, Leuven, Belgium
- Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, Netherlands
- EPI-Centre, KU Leuven, Belgium
| | - Frank E Harrell
- Department of Biostatistics, Vanderbilt University, Nashville, TN, USA
| | - Glen P Martin
- Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Karel G M Moons
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Maarten van Smeden
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Matthew Sperrin
- Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Garrett S Bullock
- Department of Orthopaedic Surgery, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Centre for Sport, Exercise and Osteoarthritis Research Versus Arthritis, University of Oxford, Oxford, UK
| | - Richard D Riley
- Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
- National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, UK
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Kim P, Rothberg MB, Nowacki AS, Yu PC, Gugliotti D, Deshpande A. Derivation and external validation of a prediction model for pneumococcal urinary antigen test positivity in patients with community-acquired pneumonia. ANTIMICROBIAL STEWARDSHIP & HEALTHCARE EPIDEMIOLOGY : ASHE 2023; 3:e166. [PMID: 38028917 PMCID: PMC10644161 DOI: 10.1017/ash.2023.421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 06/23/2023] [Accepted: 06/28/2023] [Indexed: 12/01/2023]
Abstract
Objective Derive and externally validate a prediction model for pneumococcal urinary antigen test (pUAT) positivity. Methods Retrospective cohort study of adults admitted with community-acquired pneumonia (CAP) to 177 U.S. hospitals in the Premier Database (derivation and internal validation samples) or 12 Cleveland Clinic hospitals (external validation sample). We utilized multivariable logistic regression to predict pUAT positivity in the derivation dataset, followed by model performance evaluation in both validation datasets. Potential predictors included demographics, comorbidities, clinical findings, and markers of disease severity. Results Of 198,130 Premier patients admitted with CAP, 27,970 (14.1%) underwent pUAT; 1962 (7.0%) tested positive. The strongest predictors of pUAT positivity were history of pneumococcal infection in the previous year (OR 6.99, 95% CI 4.27-11.46), severe CAP on admission (OR 1.76, 95% CI 1.56-1.98), substance abuse (OR 1.57, 95% CI 1.27-1.93), smoking (OR 1.23, 95% CI 1.09-1.39), and hyponatremia (OR 1.35, 95% CI 1.17-1.55). Negative predictors included IV antibiotic use in past year (OR 0.65, 95% CI 0.52-0.82), congestive heart failure (OR 0.72, 95% CI 0.63-0.83), obesity (OR 0.71, 95% CI 0.60-0.85), and admission from skilled nursing facility (OR 0.60, 95% CI 0.45-0.78). Model c-statistics were 0.60 and 0.67 in the internal and external validation cohorts, respectively. Compared to guideline-recommended testing of severe CAP patients, our model would have detected 23% more cases with 5% fewer tests. Conclusion Readily available data can identify patients most likely to have a positive pUAT. Our model could be incorporated into automated clinical decision support to improve test efficiency and antimicrobial stewardship.
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Affiliation(s)
- Priscilla Kim
- Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH, USA
| | - Michael B. Rothberg
- Center for Value-Based Care Research, Primary Care Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Amy S. Nowacki
- Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH, USA
- Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, USA
| | - Pei-Chun Yu
- Vaccine and Infectious Disease and Public Health Sciences Divisions, Fred Hutch Cancer Research Center, Seattle, WA, USA
| | - David Gugliotti
- Department of Hospital Medicine, Cleveland Clinic, Cleveland, OH, USA
| | - Abhishek Deshpande
- Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH, USA
- Center for Value-Based Care Research, Primary Care Institute, Cleveland Clinic, Cleveland, OH, USA
- Department of Infectious Diseases, Cleveland Clinic, Cleveland, OH, USA
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Abstract
BACKGROUND Clinical prediction models should be validated before implementation in clinical practice. But is favorable performance at internal validation or one external validation sufficient to claim that a prediction model works well in the intended clinical context? MAIN BODY We argue to the contrary because (1) patient populations vary, (2) measurement procedures vary, and (3) populations and measurements change over time. Hence, we have to expect heterogeneity in model performance between locations and settings, and across time. It follows that prediction models are never truly validated. This does not imply that validation is not important. Rather, the current focus on developing new models should shift to a focus on more extensive, well-conducted, and well-reported validation studies of promising models. CONCLUSION Principled validation strategies are needed to understand and quantify heterogeneity, monitor performance over time, and update prediction models when appropriate. Such strategies will help to ensure that prediction models stay up-to-date and safe to support clinical decision-making.
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