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White N, Parsons R, Borg D, Collins G, Barnett A. Planned but ever published? A retrospective analysis of clinical prediction model studies registered on clinicaltrials.gov since 2000. J Clin Epidemiol 2024; 173:111433. [PMID: 38897482 DOI: 10.1016/j.jclinepi.2024.111433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 06/10/2024] [Accepted: 06/12/2024] [Indexed: 06/21/2024]
Abstract
OBJECTIVES To describe the characteristics and publication outcomes of clinical prediction model studies registered on clinicaltrials.gov since 2000. STUDY DESIGN AND SETTING Observational studies registered on clinicaltrials.gov between January 1, 2000, and March 2, 2022, describing the development of a new clinical prediction model or the validation of an existing model for predicting individual-level prognostic or diagnostic risk were analyzed. Eligible clinicaltrials.gov records were classified by modeling study type (development, validation) and the model outcome being predicted (prognostic, diagnostic). Recorded characteristics included study status, sample size information, Medical Subject Headings, and plans to share individual participant data. Publication outcomes were analyzed by linking National Clinical Trial numbers for eligible records with PubMed abstracts. RESULTS Nine hundred twenty-eight records were analyzed from a possible 89,896 observational study records. Publications searches found 170 matching peer-reviewed publications for 137 clinicaltrials.gov records. The estimated proportion of records with 1 or more matching publications after accounting for time since study start was 2.8% at 2 years (95% CI: 1.7%, 3.9%), 12.3% at 5 years (9.8% to 14.9%) and 27% at 10 years (23% to 33%). Stratifying records by study start year indicated that publication proportions improved over time. Records tended to prioritize the development of new prediction models over the validation of existing models (76%; 704/928 vs. 24%; 182/928). At the time of download, 27% of records were marked as complete, 35% were still recruiting, and 14.7% had unknown status. Only 7.4% of records stated plans to share individual participant data. CONCLUSION Published clinical prediction model studies are only a fraction of overall research efforts, with many studies planned but not completed or published. Improving the uptake of study preregistration and follow-up will increase the visibility of planned research. Introducing additional registry features and guidance may improve the identification of clinical prediction model studies posted to clinical registries.
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Affiliation(s)
- Nicole White
- Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Queensland University of Technology, Kelvin Grove, Queensland, Australia.
| | - Rex Parsons
- Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Queensland University of Technology, Kelvin Grove, Queensland, Australia
| | - David Borg
- Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Queensland University of Technology, Kelvin Grove, Queensland, Australia; School of Exercise and Nutrition Sciences, Queensland University of Technology, Kelvin Grove, Queensland, Australia
| | - Gary Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology & Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Oxford, United Kingdom
| | - Adrian Barnett
- Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Queensland University of Technology, Kelvin Grove, Queensland, Australia
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la Roi-Teeuw HM, van Royen FS, de Hond A, Zahra A, de Vries S, Bartels R, Carriero AJ, van Doorn S, Dunias ZS, Kant I, Leeuwenberg T, Peters R, Veerhoek L, van Smeden M, Luijken K. Don't be misled: 3 misconceptions about external validation of clinical prediction models. J Clin Epidemiol 2024; 172:111387. [PMID: 38729274 DOI: 10.1016/j.jclinepi.2024.111387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 04/24/2024] [Accepted: 05/02/2024] [Indexed: 05/12/2024]
Abstract
Clinical prediction models provide risks of health outcomes that can inform patients and support medical decisions. However, most models never make it to actual implementation in practice. A commonly heard reason for this lack of implementation is that prediction models are often not externally validated. While we generally encourage external validation, we argue that an external validation is often neither sufficient nor required as an essential step before implementation. As such, any available external validation should not be perceived as a license for model implementation. We clarify this argument by discussing 3 common misconceptions about external validation. We argue that there is not one type of recommended validation design, not always a necessity for external validation, and sometimes a need for multiple external validations. The insights from this paper can help readers to consider, design, interpret, and appreciate external validation studies.
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Affiliation(s)
- Hannah M la Roi-Teeuw
- Department of General Practice and Nursing Science, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands.
| | - Florien S van Royen
- Department of General Practice and Nursing Science, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands
| | - Anne de Hond
- Department of Epidemiology and Health Economics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands
| | - Anum Zahra
- Department of Epidemiology and Health Economics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands
| | - Sjoerd de Vries
- Department of Digital Health, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands; Department of Information and Computing Sciences, Utrecht University, Princetonplein 5, 3584 CC, Utrecht, The Netherlands
| | - Richard Bartels
- Department of Digital Health, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands; Department of Data Science and Biostatistics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands
| | - Alex J Carriero
- Department of Epidemiology and Health Economics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands
| | - Sander van Doorn
- Department of General Practice and Nursing Science, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands
| | - Zoë S Dunias
- Department of Epidemiology and Health Economics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands
| | - Ilse Kant
- Department of Digital Health, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands
| | - Tuur Leeuwenberg
- Department of Epidemiology and Health Economics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands
| | - Ruben Peters
- Department of Digital Health, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands
| | - Laura Veerhoek
- Department of Digital Health, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands
| | - Maarten van Smeden
- Department of Epidemiology and Health Economics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands; Department of Data Science and Biostatistics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands
| | - Kim Luijken
- Department of Epidemiology and Health Economics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands
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Bate S, McGovern D, Costigliolo F, Tan PG, Kratky V, Scott J, Chapman GB, Brown N, Floyd L, Brilland B, Martín-Nares E, Aydın MF, Ilyas D, Butt A, Nic an Riogh E, Kollar M, Lees JS, Yildiz A, Hinojosa-Azaola A, Dhaygude A, Roberts SA, Rosenberg A, Wiech T, Pusey CD, Jones RB, Jayne DR, Bajema I, Jennette JC, Stevens KI, Augusto JF, Mejía-Vilet JM, Dhaun N, McAdoo SP, Tesar V, Little MA, Geetha D, Brix SR. The Improved Kidney Risk Score in ANCA-Associated Vasculitis for Clinical Practice and Trials. J Am Soc Nephrol 2024; 35:335-346. [PMID: 38082490 PMCID: PMC10914211 DOI: 10.1681/asn.0000000000000274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 11/03/2023] [Indexed: 01/27/2024] Open
Abstract
SIGNIFICANCE STATEMENT Reliable prediction tools are needed to personalize treatment in ANCA-associated GN. More than 1500 patients were collated in an international longitudinal study to revise the ANCA kidney risk score. The score showed satisfactory performance, mimicking the original study (Harrell's C=0.779). In the development cohort of 959 patients, no additional parameters aiding the tool were detected, but replacing the GFR with creatinine identified an additional cutoff. The parameter interstitial fibrosis and tubular atrophy was modified to allow wider access, risk points were reweighted, and a fourth risk group was created, improving predictive ability (C=0.831). In the validation, the new model performed similarly well with excellent calibration and discrimination ( n =480, C=0.821). The revised score optimizes prognostication for clinical practice and trials. BACKGROUND Reliable prediction tools are needed to personalize treatment in ANCA-associated GN. A retrospective international longitudinal cohort was collated to revise the ANCA renal risk score. METHODS The primary end point was ESKD with patients censored at last follow-up. Cox proportional hazards were used to reweight risk factors. Kaplan-Meier curves, Harrell's C statistic, receiver operating characteristics, and calibration plots were used to assess model performance. RESULTS Of 1591 patients, 1439 were included in the final analyses, 2:1 randomly allocated per center to development and validation cohorts (52% male, median age 64 years). In the development cohort ( n =959), the ANCA renal risk score was validated and calibrated, and parameters were reinvestigated modifying interstitial fibrosis and tubular atrophy allowing semiquantitative reporting. An additional cutoff for kidney function (K) was identified, and serum creatinine replaced GFR (K0: <250 µ mol/L=0, K1: 250-450 µ mol/L=4, K2: >450 µ mol/L=11 points). The risk points for the percentage of normal glomeruli (N) and interstitial fibrosis and tubular atrophy (T) were reweighted (N0: >25%=0, N1: 10%-25%=4, N2: <10%=7, T0: none/mild or <25%=0, T1: ≥ mild-moderate or ≥25%=3 points), and four risk groups created: low (0-4 points), moderate (5-11), high (12-18), and very high (21). Discrimination was C=0.831, and the 3-year kidney survival was 96%, 79%, 54%, and 19%, respectively. The revised score performed similarly well in the validation cohort with excellent calibration and discrimination ( n =480, C=0.821). CONCLUSIONS The updated score optimizes clinicopathologic prognostication for clinical practice and trials.
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Affiliation(s)
- Sebastian Bate
- Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust, Manchester, United Kingdom
- Division of Population Health, Health Services Research, and Primary Care, Centre for Biostatistics, University of Manchester, Manchester, United Kingdom
| | - Dominic McGovern
- Glasgow Renal and Transplant Unit, Queen Elizabeth University Hospital, Glasgow, United Kingdom
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, United Kingdom
- Department of Medicine, University of Cambridge, Cambridge, United Kingdom
- Department of Renal Medicine, Vasculitis Clinic, Addenbrooke's Hospital, Cambridge, United Kingdom
| | - Francesca Costigliolo
- Division of Nephrology, Dialysis and Transplantation, University of Genova, Genova, Italy
- Department of Internal Medicine and IRCCS Ospedale Policlinico San Martino, Genova, Italy
| | - Pek Ghe Tan
- Imperial College Renal and Transplant Centre, Hammersmith Hospital, Imperial College Healthcare NHS Trust, London, United Kingdom
- Renal Unit, Northern Health, Victoria, Australia
| | - Vojtech Kratky
- 1st Faculty of Medicine, Charles University, Prague, Czechia
- Department of Nephrology, General University Hospital, Prague, Czechia
| | - Jennifer Scott
- Trinity Kidney Centre, Trinity College Dublin, Dublin, Ireland
| | - Gavin B. Chapman
- University/BHF Centre for Cardiovascular Science, University of Edinburgh and Department of Renal Medicine, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom
| | - Nina Brown
- Division of Cardiovascular Sciences, University of Manchester, Manchester, United Kingdom
- Renal Department, Salford Royal Hospital, Northern Care Alliance NHS Foundation Trust, Salford, United Kingdom
| | - Lauren Floyd
- Division of Cardiovascular Sciences, University of Manchester, Manchester, United Kingdom
- Renal Department, Royal Preston Hospital, Lancashire Teaching Hospitals NHS Foundation Trust, Preston, United Kingdom
| | - Benoit Brilland
- Service de Néphrologie-Dialyse-Transplantation, CHU d’Angers, Angers, France
| | - Eduardo Martín-Nares
- Departments of Immunology and Rheumatology, Nephrology and Mineral Metabolism, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | | | - Duha Ilyas
- Division of Cardiovascular Sciences, University of Manchester, Manchester, United Kingdom
- Renal, Transplantation and Urology Unit, Manchester University NHS Foundation Trust, Manchester, United Kingdom
| | - Arslan Butt
- Renal Department, Salford Royal Hospital, Northern Care Alliance NHS Foundation Trust, Salford, United Kingdom
| | | | - Marek Kollar
- Department of Pathology, Institute for Clinical and Experimental Medicine, Prague, Czechia
| | - Jennifer S. Lees
- Glasgow Renal and Transplant Unit, Queen Elizabeth University Hospital, Glasgow, United Kingdom
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, United Kingdom
| | - Abdülmecit Yildiz
- Division of Nephrology, Bursa Uludağ University School of Medicine, Bursa, Turkey
| | - Andrea Hinojosa-Azaola
- Departments of Immunology and Rheumatology, Nephrology and Mineral Metabolism, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Ajay Dhaygude
- Renal Department, Royal Preston Hospital, Lancashire Teaching Hospitals NHS Foundation Trust, Preston, United Kingdom
| | - Stephen A. Roberts
- Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust, Manchester, United Kingdom
- Division of Population Health, Health Services Research, and Primary Care, Centre for Biostatistics, University of Manchester, Manchester, United Kingdom
| | - Avi Rosenberg
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Thorsten Wiech
- University Medical Center Hamburg-Eppendorf, Institute of Pathology, Hamburg, Germany
| | - Charles D. Pusey
- Imperial College Renal and Transplant Centre, Hammersmith Hospital, Imperial College Healthcare NHS Trust, London, United Kingdom
- Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom
| | - Rachel B. Jones
- Department of Medicine, University of Cambridge, Cambridge, United Kingdom
- Department of Renal Medicine, Vasculitis Clinic, Addenbrooke's Hospital, Cambridge, United Kingdom
| | - David R.W. Jayne
- Department of Medicine, University of Cambridge, Cambridge, United Kingdom
- Department of Renal Medicine, Vasculitis Clinic, Addenbrooke's Hospital, Cambridge, United Kingdom
| | - Ingeborg Bajema
- Department of Pathology, Groningen University Medical Center, Groningen, The Netherlands
| | - J. Charles Jennette
- Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Kate I. Stevens
- Glasgow Renal and Transplant Unit, Queen Elizabeth University Hospital, Glasgow, United Kingdom
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, United Kingdom
| | | | - Juan Manuel Mejía-Vilet
- Departments of Immunology and Rheumatology, Nephrology and Mineral Metabolism, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Neeraj Dhaun
- University/BHF Centre for Cardiovascular Science, University of Edinburgh and Department of Renal Medicine, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom
| | - Stephen P. McAdoo
- Imperial College Renal and Transplant Centre, Hammersmith Hospital, Imperial College Healthcare NHS Trust, London, United Kingdom
- Centre for Inflammatory Disease, Department of Immunology and Inflammation, Imperial College London, London, United Kingdom
| | - Vladimir Tesar
- 1st Faculty of Medicine, Charles University, Prague, Czechia
- Department of Nephrology, General University Hospital, Prague, Czechia
| | - Mark A. Little
- Trinity Kidney Centre, Trinity College Dublin, Dublin, Ireland
| | - Duruvu Geetha
- Division of Nephrology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Silke R. Brix
- Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust, Manchester, United Kingdom
- Renal, Transplantation and Urology Unit, Manchester University NHS Foundation Trust, Manchester, United Kingdom
- Division of Cell Matrix Biology and Regenerative Medicine, University of Manchester, Manchester, United Kingdom
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4
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Deisenhofer AK, Barkham M, Beierl ET, Schwartz B, Aafjes-van Doorn K, Beevers CG, Berwian IM, Blackwell SE, Bockting CL, Brakemeier EL, Brown G, Buckman JEJ, Castonguay LG, Cusack CE, Dalgleish T, de Jong K, Delgadillo J, DeRubeis RJ, Driessen E, Ehrenreich-May J, Fisher AJ, Fried EI, Fritz J, Furukawa TA, Gillan CM, Gómez Penedo JM, Hitchcock PF, Hofmann SG, Hollon SD, Jacobson NC, Karlin DR, Lee CT, Levinson CA, Lorenzo-Luaces L, McDanal R, Moggia D, Ng MY, Norris LA, Patel V, Piccirillo ML, Pilling S, Rubel JA, Salazar-de-Pablo G, Schleider JL, Schnurr PP, Schueller SM, Siegle GJ, Uher R, Watkins E, Webb CA, Wiltsey Stirman S, Wynants L, Youn SJ, Zilcha-Mano S, Lutz W, Cohen ZD. Implementing precision methods in personalizing psychological therapies: Barriers and possible ways forward. Behav Res Ther 2024; 172:104443. [PMID: 38086157 DOI: 10.1016/j.brat.2023.104443] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 11/21/2023] [Accepted: 11/27/2023] [Indexed: 12/26/2023]
Affiliation(s)
| | | | | | | | | | | | | | | | - Claudi L Bockting
- AmsterdamUMC, Department of Psychiatry, Research Program Amsterdam Public Health and Centre for Urban Mental Health, University of Amsterdam, the Netherlands
| | | | | | | | | | | | | | - Kim de Jong
- Leiden University, Institute of Psychology, USA
| | | | | | | | | | | | | | - Jessica Fritz
- University of Cambridge, UK; Philipps University of Marburg, Germany
| | | | - Claire M Gillan
- School of Psychology, Trinity College Institute for Neuroscience, And Global Brain Health Institute, Trinity College Dublin, USA
| | | | | | | | | | | | | | | | | | | | | | | | - Mei Yi Ng
- Florida International University, USA
| | | | | | | | | | | | | | - Jessica L Schleider
- Stony Brook University and Feinberg School of Medicine Northwestern University, USA
| | - Paula P Schnurr
- National Center for PTSD and Geisel School of Medicine at Dartmouth, USA
| | | | | | | | | | | | | | | | - Soo Jeong Youn
- Reliant Medical Group, OptumCare and Harvard Medical School, USA
| | | | | | - Zachary D Cohen
- University of California, Los Angeles and University of Arizona, USA.
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5
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Lou SS, Liu Y, Cohen ME, Ko CY, Hall BL, Kannampallil T. National Multi-Institutional Validation of a Surgical Transfusion Risk Prediction Model. J Am Coll Surg 2024; 238:99-105. [PMID: 37737660 DOI: 10.1097/xcs.0000000000000874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/23/2023]
Abstract
BACKGROUND Accurate estimation of surgical transfusion risk is important for many aspects of surgical planning, yet few methods for estimating are available for estimating such risk. There is a need for reliable validated methods for transfusion risk stratification to support effective perioperative planning and resource stewardship. STUDY DESIGN This study was conducted using the American College of Surgeons NSQIP datafile from 2019. S-PATH performance was evaluated at each contributing hospital, with and without hospital-specific model tuning. Linear regression was used to assess the relationship between hospital characteristics and area under the receiver operating characteristic (AUROC) curve. RESULTS A total of 1,000,927 surgical cases from 414 hospitals were evaluated. Aggregate AUROC was 0.910 (95% CI 0.904 to 0.916) without model tuning and 0.925 (95% CI 0.919 to 0.931) with model tuning. AUROC varied across individual hospitals (median 0.900, interquartile range 0.849 to 0.944), but no statistically significant relationships were found between hospital-level characteristics studied and model AUROC. CONCLUSIONS S-PATH demonstrated excellent discriminative performance, although there was variation across hospitals that was not well-explained by hospital-level characteristics. These results highlight the S-PATH's viability as a generalizable surgical transfusion risk prediction tool.
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Affiliation(s)
- Sunny S Lou
- From the Department of Anesthesiology, Washington University School of Medicine, St Louis, MO (Lou, Kannampallil)
| | - Yaoming Liu
- Division of Research and Optimal Patient Care, American College of Surgeons, Chicago, IL (Liu, Ko, Hall, Cohen)
| | - Mark E Cohen
- Division of Research and Optimal Patient Care, American College of Surgeons, Chicago, IL (Liu, Ko, Hall, Cohen)
| | - Clifford Y Ko
- Division of Research and Optimal Patient Care, American College of Surgeons, Chicago, IL (Liu, Ko, Hall, Cohen)
- Department of Surgery, David Geffen School of Medicine, University of California Los Angeles, and the VA Greater Los Angeles Health System, Los Angeles, CA (Ko)
| | - Bruce L Hall
- Division of Research and Optimal Patient Care, American College of Surgeons, Chicago, IL (Liu, Ko, Hall, Cohen)
- Department of Surgery, Washington University School of Medicine; Center for Health Policy and the Olin Business School at Washington University in St Louis; John Cochran Veterans Affairs Medical Center; and BJC Healthcare, St Louis, MO (Hall)
| | - Thomas Kannampallil
- From the Department of Anesthesiology, Washington University School of Medicine, St Louis, MO (Lou, Kannampallil)
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McLean KA, Goel T, Lawday S, Riad A, Simoes J, Knight SR, Ghosh D, Glasbey JC, Bhangu A, Harrison EM. Prognostic models for surgical-site infection in gastrointestinal surgery: systematic review. Br J Surg 2023; 110:1441-1450. [PMID: 37433918 PMCID: PMC10564404 DOI: 10.1093/bjs/znad187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 04/11/2023] [Accepted: 05/20/2023] [Indexed: 07/13/2023]
Abstract
BACKGROUND Identification of patients at high risk of surgical-site infection may allow clinicians to target interventions and monitoring to minimize associated morbidity. The aim of this systematic review was to identify and evaluate prognostic tools for the prediction of surgical-site infection in gastrointestinal surgery. METHODS This systematic review sought to identify original studies describing the development and validation of prognostic models for 30-day SSI after gastrointestinal surgery (PROSPERO: CRD42022311019). MEDLINE, Embase, Global Health, and IEEE Xplore were searched from 1 January 2000 to 24 February 2022. Studies were excluded if prognostic models included postoperative parameters or were procedure specific. A narrative synthesis was performed, with sample-size sufficiency, discriminative ability (area under the receiver operating characteristic curve), and prognostic accuracy compared. RESULTS Of 2249 records reviewed, 23 eligible prognostic models were identified. A total of 13 (57 per cent) reported no internal validation and only 4 (17 per cent) had undergone external validation. Most identified operative contamination (57 per cent, 13 of 23) and duration (52 per cent, 12 of 23) as important predictors; however, there remained substantial heterogeneity in other predictors identified (range 2-28). All models demonstrated a high risk of bias due to the analytic approach, with overall low applicability to an undifferentiated gastrointestinal surgical population. Model discrimination was reported in most studies (83 per cent, 19 of 23); however, calibration (22 per cent, 5 of 23) and prognostic accuracy (17 per cent, 4 of 23) were infrequently assessed. Of externally validated models (of which there were four), none displayed 'good' discrimination (area under the receiver operating characteristic curve greater than or equal to 0.7). CONCLUSION The risk of surgical-site infection after gastrointestinal surgery is insufficiently described by existing risk-prediction tools, which are not suitable for routine use. Novel risk-stratification tools are required to target perioperative interventions and mitigate modifiable risk factors.
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Affiliation(s)
- Kenneth A McLean
- Department of Clinical Surgery, Royal Infirmary of Edinburgh, Edinburgh, UK
| | - Tanvi Goel
- India Hub, NIHR Global Health Research Unit on Global Surgery, Ludhiana, India
| | - Samuel Lawday
- Bristol Centre for Surgical Research, University of Bristol, Bristol, UK
| | - Aya Riad
- Department of Clinical Surgery, Royal Infirmary of Edinburgh, Edinburgh, UK
| | - Joana Simoes
- Institute of Translational Medicine, University of Birmingham, Birmingham, UK
| | - Stephen R Knight
- Department of Clinical Surgery, Royal Infirmary of Edinburgh, Edinburgh, UK
| | - Dhruva Ghosh
- India Hub, NIHR Global Health Research Unit on Global Surgery, Ludhiana, India
| | - James C Glasbey
- Institute of Translational Medicine, University of Birmingham, Birmingham, UK
| | - Aneel Bhangu
- Institute of Translational Medicine, University of Birmingham, Birmingham, UK
| | - Ewen M Harrison
- Department of Clinical Surgery, Royal Infirmary of Edinburgh, Edinburgh, UK
- Centre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, UK
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7
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Spencer KL, Absolom KL, Allsop MJ, Relton SD, Pearce J, Liao K, Naseer S, Salako O, Howdon D, Hewison J, Velikova G, Faivre-Finn C, Bekker HL, van der Veer SN. Fixing the Leaky Pipe: How to Improve the Uptake of Patient-Reported Outcomes-Based Prognostic and Predictive Models in Cancer Clinical Practice. JCO Clin Cancer Inform 2023; 7:e2300070. [PMID: 37976441 PMCID: PMC10681558 DOI: 10.1200/cci.23.00070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 09/08/2023] [Accepted: 09/29/2023] [Indexed: 11/19/2023] Open
Abstract
PURPOSE This discussion paper outlines challenges and proposes solutions for successfully implementing prediction models that incorporate patient-reported outcomes (PROs) in cancer practice. METHODS We organized a full-day multidisciplinary meeting of people with expertise in cancer care delivery, PRO collection, PRO use in prediction modeling, computing, implementation, and decision science. The discussions presented here focused on identifying challenges to the development, implementation and use of prediction models incorporating PROs, and suggesting possible solutions. RESULTS Specific challenges and solutions were identified across three broad areas. (1) Understanding decision making and implementation: necessitating multidisciplinary collaboration in the early stages and throughout; early stakeholder engagement to define the decision problem and ensure acceptability of PROs in prediction; understanding patient/clinician interpretation of PRO predictions and uncertainty to optimize prediction impact; striving for model integration into existing electronic health records; and early regulatory alignment. (2) Recognizing the limitations to PRO collection and their impact on prediction: incorporating validated, clinically important PROs to maximize model generalizability and clinical engagement; and minimizing missing PRO data (resulting from both structural digital exclusion and time-varying factors) to avoid exacerbating existing inequalities. (3) Statistical and modeling challenges: incorporating statistical methods to address missing data; ensuring predictive modeling recognizes complex causal relationships; and considering temporal and geographic recalibration so that model predictions reflect the relevant population. CONCLUSION Developing and implementing PRO-based prediction models in cancer care requires extensive multidisciplinary working from the earliest stages, recognition of implementation challenges because of PRO collection and model presentation, and robust statistical methods to manage missing data, causality, and calibration. Prediction models incorporating PROs should be viewed as complex interventions, with their development and impact assessment carried out to reflect this.
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Affiliation(s)
- Katie L. Spencer
- Leeds Institute of Health Sciences, University of Leeds, Leeds, United Kingdom
- Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom
| | - Kate L. Absolom
- Leeds Institute of Health Sciences, University of Leeds, Leeds, United Kingdom
| | - Matthew J. Allsop
- Leeds Institute of Health Sciences, University of Leeds, Leeds, United Kingdom
| | - Samuel D. Relton
- Leeds Institute of Data Analytics, University of Leeds, Leeds, United Kingdom
| | - Jessica Pearce
- Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom
- Leeds Institute of Medical Research, University of Leeds, Leeds, United Kingdom
| | - Kuan Liao
- Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, Centre for Health Informatics, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom
| | - Sairah Naseer
- School of Medicine, University of Leeds, Leeds, United Kingdom
| | - Omolola Salako
- College of Medicine, University of Lagos, Lagos, Nigeria
| | - Daniel Howdon
- Leeds Institute of Health Sciences, University of Leeds, Leeds, United Kingdom
| | - Jenny Hewison
- Leeds Institute of Health Sciences, University of Leeds, Leeds, United Kingdom
| | - Galina Velikova
- Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom
- Leeds Institute of Medical Research, University of Leeds, Leeds, United Kingdom
| | - Corinne Faivre-Finn
- Institute of Cancer Sciences, University of Manchester, Manchester, United Kingdom
| | - Hilary L. Bekker
- Leeds Institute of Health Sciences, University of Leeds, Leeds, United Kingdom
| | - Sabine N. van der Veer
- Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, Centre for Health Informatics, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom
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8
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Rademaker MM, Smit AL, Stokroos RJ, van Smeden M, Stegeman I. Development and internal validation of a prediction model for the presence of tinnitus in a Dutch population-based cohort. Front Neurol 2023; 14:1213687. [PMID: 37602261 PMCID: PMC10434772 DOI: 10.3389/fneur.2023.1213687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 07/17/2023] [Indexed: 08/22/2023] Open
Abstract
Objectives In this study we aim to develop and internally validate a prediction model on tinnitus experience in a representative sample of the Dutch general population. Methods We developed a multivariable prediction model using elastic net logistic regression with data from the Dutch Lifelines Cohort Study. This is a multigenerational cohort study on adults who are located in the northern parts of the Netherlands. The model was internally validated using 10-fold cross-validation. The outcome of the model was tinnitus presence, for which we used 24 candidate predictors on different domains (among others demographic, hearing specific, and mental health variables). We assessed the overall predictive performance, discrimination, and calibration of the model. Results Data on 122.884 different participants were included, of which 7,965 (6.5%, 0 missing) experienced tinnitus. Nine variables were included in the final model: sex, hearing aids, hearing limitations, arterial blood pressure, quality of sleep, general health, symptom checklist of somatic complaints, cardiovascular risk factors, and age. In the final model, the Brier score was 0.056 and 0.787 in internal validation. Conclusion We developed and internally validated a prediction model on tinnitus presence in a multigenerational cohort of the Dutch general population. From the 24 candidate predictors, the final model included nine predictors.
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Affiliation(s)
- Maaike M. Rademaker
- Department of Otorhinolaryngology and Head and Neck Surgery, University Medical Center Utrecht, Utrecht, Netherlands
- UMC Utrecht Brain Center, Utrecht University, Utrecht, Netherlands
| | - Adriana L. Smit
- Department of Otorhinolaryngology and Head and Neck Surgery, University Medical Center Utrecht, Utrecht, Netherlands
- UMC Utrecht Brain Center, Utrecht University, Utrecht, Netherlands
| | - Robert J. Stokroos
- Department of Otorhinolaryngology and Head and Neck Surgery, University Medical Center Utrecht, Utrecht, Netherlands
- UMC Utrecht Brain Center, Utrecht University, Utrecht, Netherlands
| | - Maarten van Smeden
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Inge Stegeman
- Department of Otorhinolaryngology and Head and Neck Surgery, University Medical Center Utrecht, Utrecht, Netherlands
- UMC Utrecht Brain Center, Utrecht University, Utrecht, Netherlands
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9
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Blythe R, Parsons R, Barnett AG, McPhail SM, White NM. Vital signs-based deterioration prediction model assumptions can lead to losses in prediction performance. J Clin Epidemiol 2023; 159:106-115. [PMID: 37245699 DOI: 10.1016/j.jclinepi.2023.05.020] [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: 02/17/2023] [Revised: 04/11/2023] [Accepted: 05/22/2023] [Indexed: 05/30/2023]
Abstract
OBJECTIVE Vital signs-based models are complicated by repeated measures per patient and frequently missing data. This paper investigated the impacts of common vital signs modeling assumptions during clinical deterioration prediction model development. STUDY DESIGN AND SETTING Electronic medical record (EMR) data from five Australian hospitals (1 January 2019-31 December 2020) were used. Summary statistics for each observation's prior vital signs were created. Missing data patterns were investigated using boosted decision trees, then imputed with common methods. Two example models predicting in-hospital mortality were developed, as follows: logistic regression and eXtreme Gradient Boosting. Model discrimination and calibration were assessed using the C-statistic and nonparametric calibration plots. RESULTS The data contained 5,620,641 observations from 342,149 admissions. Missing vitals were associated with observation frequency, vital sign variability, and patient consciousness. Summary statistics improved discrimination slightly for logistic regression and markedly for eXtreme Gradient Boosting. Imputation method led to notable differences in model discrimination and calibration. Model calibration was generally poor. CONCLUSION Summary statistics and imputation methods can improve model discrimination and reduce bias during model development, but it is questionable whether these differences are clinically significant. Researchers should consider why data are missing during model development and how this may impact clinical utility.
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Affiliation(s)
- Robin Blythe
- Australian Centre for Health Services Innovation, Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, 60 Musk Ave, Kelvin Grove, Queensland, 4059, Australia
| | - Rex Parsons
- Australian Centre for Health Services Innovation, Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, 60 Musk Ave, Kelvin Grove, Queensland, 4059, Australia
| | - Adrian G Barnett
- Australian Centre for Health Services Innovation, Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, 60 Musk Ave, Kelvin Grove, Queensland, 4059, Australia
| | - Steven M McPhail
- Australian Centre for Health Services Innovation, Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, 60 Musk Ave, Kelvin Grove, Queensland, 4059, Australia; Digital Health and Informatics, Metro South Health, 199 Ipswich Road, Brisbane, Queensland, 4102, Australia
| | - Nicole M White
- Australian Centre for Health Services Innovation, Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, 60 Musk Ave, Kelvin Grove, Queensland, 4059, Australia.
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10
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Papachristou N, Kotronoulas G, Dikaios N, Allison SJ, Eleftherochorinou H, Rai T, Kunz H, Barnaghi P, Miaskowski C, Bamidis PD. Digital Transformation of Cancer Care in the Era of Big Data, Artificial Intelligence and Data-Driven Interventions: Navigating the Field. Semin Oncol Nurs 2023; 39:151433. [PMID: 37137770 DOI: 10.1016/j.soncn.2023.151433] [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: 03/28/2023] [Accepted: 03/29/2023] [Indexed: 05/05/2023]
Abstract
OBJECTIVES To navigate the field of digital cancer care and define and discuss key aspects and applications of big data analytics, artificial intelligence (AI), and data-driven interventions. DATA SOURCES Peer-reviewed scientific publications and expert opinion. CONCLUSION The digital transformation of cancer care, enabled by big data analytics, AI, and data-driven interventions, presents a significant opportunity to revolutionize the field. An increased understanding of the lifecycle and ethics of data-driven interventions will enhance development of innovative and applicable products to advance digital cancer care services. IMPLICATIONS FOR NURSING PRACTICE As digital technologies become integrated into cancer care, nurse practitioners and scientists will be required to increase their knowledge and skills to effectively use these tools to the patient's benefit. An enhanced understanding of the core concepts of AI and big data, confident use of digital health platforms, and ability to interpret the outputs of data-driven interventions are key competencies. Nurses in oncology will play a crucial role in patient education around big data and AI, with a focus on addressing any arising questions, concerns, or misconceptions to foster trust in these technologies. Successful integration of data-driven innovations into oncology nursing practice will empower practitioners to deliver more personalized, effective, and evidence-based care.
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Affiliation(s)
- Nikolaos Papachristou
- Medical Physics and Digital Innovation Laboratory, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece.
| | | | - Nikolaos Dikaios
- Centre for Vision Speech and Signal Processing, University of Surrey, Guildford, UK; Mathematics Research Centre, Academy of Athens, Athens, Greece
| | - Sarah J Allison
- Department of Sport, Exercise and Rehabilitation, Faculty of Health and Life Sciences, Northumbria University, Newcastle, UK; School of Bioscience and Medicine, Faculty of Health & Medical Sciences, University of Surrey, Guildford, UK
| | | | - Taranpreet Rai
- Centre for Vision Speech and Signal Processing, University of Surrey, Guildford, UK; Datalab, The Veterinary Health Innovation Engine (vHive), Guildford, UK
| | - Holger Kunz
- Institute of Health Informatics, University College London, London, UK
| | - Payam Barnaghi
- UK Dementia Research Institute Care Research and Technology Centre, Imperial College London, London, UK
| | - Christine Miaskowski
- School of Nursing, University California San Francisco, San Francisco, California, USA
| | - Panagiotis D Bamidis
- Medical Physics and Digital Innovation Laboratory, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
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11
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Finlayson SG, Beam AL, van Smeden M. Machine Learning and Statistics in Clinical Research Articles-Moving Past the False Dichotomy. JAMA Pediatr 2023; 177:448-450. [PMID: 36939696 DOI: 10.1001/jamapediatrics.2023.0034] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/21/2023]
Abstract
This Viewpoint describes the false dichotomy between statistics and machine learning and suggests considerations in building and evaluating clinical prediction models.
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Affiliation(s)
- Samuel G Finlayson
- Department of Pediatrics, Seattle Children's Hospital, Seattle, Washington.,Department of Genetics, University of Washington, Seattle
| | - Andrew L Beam
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Maarten van Smeden
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
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12
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Rademaker MM, Meijers SM, Smit AL, Stegeman I. Prediction Models for Tinnitus Presence and the Impact of Tinnitus on Daily Life: A Systematic Review. J Clin Med 2023; 12:jcm12020695. [PMID: 36675624 PMCID: PMC9861218 DOI: 10.3390/jcm12020695] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 12/27/2022] [Accepted: 01/03/2023] [Indexed: 01/17/2023] Open
Abstract
The presence of tinnitus does not necessarily imply associated suffering. Prediction models on the impact of tinnitus on daily life could aid medical professionals to direct specific medical resources to those (groups of) tinnitus patients with specific levels of impact. Models of tinnitus presence could possibly identify risk factors for tinnitus. We systematically searched the PubMed and EMBASE databases for articles published up to January 2021. We included all studies that reported on multivariable prediction models for tinnitus presence or the impact of tinnitus on daily life. Twenty-one development studies were included, with a total of 31 prediction models. Seventeen studies made a prediction model for the impact of tinnitus on daily life, three studies made a prediction model for tinnitus presence and one study made models for both. The risk of bias was high and reporting was poor in all studies. The most used predictors in the final impact on daily life models were depression- or anxiety-associated questionnaire scores. Demographic predictors were most common in final presence models. No models were internally or externally validated. All published prediction models were poorly reported and had a high risk of bias. This hinders the usability of the current prediction models. Methodological guidance is available for the development and validation of prediction models. Researchers should consider the importance and clinical relevance of the models they develop and should consider validation of existing models before developing new ones.
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Affiliation(s)
- Maaike M. Rademaker
- Department of Otorhinolaryngology, Head and Neck Surgery, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands
- UMC Utrecht Brain Center, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands
| | - Sebastiaan M. Meijers
- Department of Otorhinolaryngology, Head and Neck Surgery, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands
- UMC Utrecht Brain Center, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands
| | - Adriana L. Smit
- Department of Otorhinolaryngology, Head and Neck Surgery, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands
- UMC Utrecht Brain Center, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands
| | - Inge Stegeman
- Department of Otorhinolaryngology, Head and Neck Surgery, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands
- UMC Utrecht Brain Center, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands
- Correspondence:
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