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What do we mean by validating a prognostic model?

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Development and Validation of a Scoring System to Predict Outcomes of Vedolizumab Treatment in Patients With Crohn’s Disease

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Meta-Analysis Comparing Established Risk Prediction Models (EuroSCORE II, STS Score, and ACEF Score) for Perioperative Mortality During Cardiac Surgery

Sullivan, P. G., Wallach, J. D., & Ioannidis, J. P. A. (2016) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2016.08.024 · Cited by 144 · see citing works

Penalization and shrinkage methods produced unreliable clinical prediction models especially when sample size was small

Riley, R. D., Snell, K. I. E., Martin, G. P., Whittle, R., Archer, L., Sperrin, M., & Collins, G. S. (2021) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2020.12.005 · Cited by 142 · see citing works

Comparison of Multivariable Logistic Regression and Other Machine Learning Algorithms for Prognostic Prediction Studies in Pregnancy Care: Systematic Review and Meta-Analysis

Sufriyana, H., Husnayain, A., Chen, Y.-L., Kuo, C.-Y., Singh, O., Yeh, T.-Y., Wu, Y., & Su, E. C. (2020) · JMIR Medical Informatics · doi:10.2196/16503 · Cited by 145 · see citing works

Prediction of Persistent Post-Concussion Symptoms after Mild Traumatic Brain Injury

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The Kidney Failure Risk Equation for prediction of end stage renal disease in UK primary care: An external validation and clinical impact projection cohort study

Major, R., Shepherd, D. J., Medcalf, J. F., Xu, G., Gray, L. J., & Brunskill, N. J. (2019) · PLoS Medicine · doi:10.1371/journal.pmed.1002955 · Cited by 135 · see citing works

Embryo selection with artificial intelligence: how to evaluate and compare methods?

Kragh, M. F., & Karstoft, H. (2021) · Journal of Assisted Reproduction and Genetics · doi:10.1007/s10815-021-02254-6 · Cited by 125 · see citing works

Impact of predictor measurement heterogeneity across settings on the performance of prediction models: A measurement error perspective

Luijken, K., Groenwold, R. H. H., CALSTER, B. V., Steyerberg, E. W., & van Smeden, M. (2019) · Statistics in Medicine · doi:10.1002/sim.8183 · Cited by 124 · see citing works

External validation of clinical prediction models: simulation-based sample size calculations were more reliable than rules-of-thumb

Snell, K. I. E., Archer, L., Ensor, J., Bonnett, L. J., Debray, T. P. A., Phillips, B., Collins, P. G. S., & Riley, R. D. (2021) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2021.02.011 · Cited by 125 · see citing works

Performance of Firth-and logF-type penalized methods in risk prediction for small or sparse binary data

Rahman, M. S., & Sultana, M. (2017) · BMC Medical Research Methodology · doi:10.1186/s12874-017-0313-9 · Cited by 120 · see citing works

Comparison of Prehospital Scales for Predicting Large Anterior Vessel Occlusion in the Ambulance Setting

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Minimum sample size calculations for external validation of a clinical prediction model with a time‐to‐event outcome

Riley, R. D., Collins, P. G. S., Ensor, J., Archer, L., Booth, S., Mozumder, S. I., Rutherford, M. J., van Smeden, M., Lambert, P. C., & Snell, K. I. E. (2021) · Statistics in Medicine · doi:10.1002/sim.9275 · Cited by 116 · see citing works

Towards the best kidney failure prediction tool: a systematic review and selection aid

Ramspek, C. L., de Jong, Y., Dekker, F. W., & van Diepen, M. (2019) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfz018 · Cited by 106 · see citing works

Evaluation of performance measures in predictive artificial intelligence models to support medical decisions: overview and guidance

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Validation of prediction models in the presence of competing risks: a guide through modern methods

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Transdiagnostic Risk Calculator for the Automatic Detection of Individuals at Risk and the Prediction of Psychosis: Second Replication in an Independent National Health Service Trust

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Cardiovascular risk prediction in type 2 diabetes: a comparison of 22 risk scores in primary care settings

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Key steps and common pitfalls in developing and validating risk models

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Using natural language processing on electronic health records to enhance detection and prediction of psychosis risk.

Irving, J., Patel, R., Oliver, D., Colling, C., Pritchard, M., Broadbent, M., Baldwin, H., Stahl, D., Stewart, R., & Fusar-Poli, P. (2021) · Schizophrenia Bulletin · doi:10.1093/schbul/sbaa126 · Cited by 92 · see citing works

Impact of random oversampling and random undersampling on the performance of prediction models developed using observational health data

Yang, C. B., Fridgeirsson, E. A., Kors, J. A., Reps, J., & Rijnbeek, P. R. (2024) · Journal Of Big Data · doi:10.1186/s40537-023-00857-7 · Cited by 91 · see citing works

Transparent reporting of multivariable prediction models developed or validated using clustered data: TRIPOD-Cluster checklist

Debray, T. P. A., Collins, P. G. S., Riley, R. D., Snell, K. I. E., CALSTER, B. V., Reitsma, J. B., & Moons, K. G. M. (2023) · BMJ · doi:10.1136/bmj-2022-071018 · Cited by 97 · see citing works

Prediction models for the risk of spontaneous preterm birth based on maternal characteristics: a systematic review and independent external validation

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Prognostic prediction models for chronic postsurgical pain in adults: a systematic review

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Blood vessel segmentation in retinal fundus images using Gabor filters, fractional derivatives, and Expectation Maximization

Aguirre–Ramos, H., Avina‐Cervantes, J. G., Cruz-Aceves, I., Ruiz-Pinales, J., & Ledesma, S. (2018) · Applied Mathematics and Computation · doi:10.1016/j.amc.2018.07.057 · Cited by 85 · see citing works

Systematic review of prognostic models for recurrent venous thromboembolism (VTE) post-treatment of first unprovoked VTE

Ensor, J., Riley, R. D., Moore, D., Snell, K. I. E., Bayliss, S., & Fitzmaurice, D. (2016) · BMJ Open · doi:10.1136/bmjopen-2016-011190 · Cited by 84 · see citing works

Delirium prediction in the intensive care unit: comparison of two delirium prediction models

Wassenaar, A., Schoonhoven, L., Devlin, J. W., van Haren, F. M. P., Slooter, A. J. C., Jorens, P. G., van der Jagt, M., Simons, K. S., Egerod, I., Burry, L. D., et al. (2018) · Critical Care · doi:10.1186/s13054-018-2037-6 · Cited by 84 · see citing works

Arrhythmic risk prediction in arrhythmogenic right ventricular cardiomyopathy: external validation of the arrhythmogenic right ventricular cardiomyopathy risk calculator

Jordà, P., Bosman, L. P., Gasperetti, A., Mazzanti, A., Gourraud, J., Davies, B., Frederiksen, T. C., Weidmann, Z. M., Marco, A. D., Roberts, J. D., et al. (2022) · European Heart Journal · doi:10.1093/eurheartj/ehac289 · Cited by 84 · see citing works

Just How Confident Can We Be in Predicting Sports Injuries? A Systematic Review of the Methodological Conduct and Performance of Existing Musculoskeletal Injury Prediction Models in Sport

Bullock, G. S., Mylott, J. A., Hughes, T., Nicholson, K. F., Riley, R. D., & Collins, P. G. S. (2022) · Sports Medicine · doi:10.1007/s40279-022-01698-9 · Cited by 88 · see citing works

Trends in the conduct and reporting of clinical prediction model development and validation: a systematic review

Yang, C. B., Kors, J. A., Ioannou, S., John, L. H., Markus, A. F., Rekkas, A., de Ridder, M. A. J., Seinen, T. M., Williams, R. D., & Rijnbeek, P. R. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocac002 · Cited by 78 · see citing works

External Validation of the Oakland Score to Assess Safe Hospital Discharge Among Adult Patients With Acute Lower Gastrointestinal Bleeding in the US

Oakland, K., Kothiwale, S., Forehand, T., Jackson, E. S., Bucknall, C. A., Sey, M. S. L., Singh, S., Jairath, V., & Perlin, J. B. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.9630 · Cited by 71 · see citing works

Development and validation of a multivariate predictive model for rheumatoid arthritis mortality using a machine learning approach

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SCORE2 cardiovascular risk prediction models in an ethnic and socioeconomic diverse population in the Netherlands: an external validation study

Kist, J. M., Vos, R. C., Mairuhu, A. T. A., Struijs, J. N., van Peet, P. G., Vos, H. M. M., van Os, H. J. A., Beishuizen, E. D., Sijpkens, Y. W. J., Faiq, M. A., et al. (2023) · EClinicalMedicine · doi:10.1016/j.eclinm.2023.101862 · Cited by 68 · see citing works

Automatic Triage of 12‐Lead ECGs Using Deep Convolutional Neural Networks

van de Leur, R. R., Blom, L. J., Gavves, E., Hof, I. E., van der Heijden, J. F., Clappers, N., Doevendans, P. A., Hassink, R. J., & van Es, R. (2020) · Journal of the American Heart Association · doi:10.1161/jaha.119.015138 · Cited by 67 · see citing works

Sample Size Analysis for Machine Learning Clinical Validation Studies

Goldenholz, D. M., Sun, H., Ganglberger, W., & Westover, M. B. (2023) · Biomedicines · doi:10.3390/biomedicines11030685 · Cited by 67 · see citing works

Sensitivity of the Kaiser Permanente early-onset sepsis calculator: A systematic review and meta-analysis

Pettinger, K. J., Mayers, K., McKechnie, L., & Phillips, B. (2019) · EClinicalMedicine · doi:10.1016/j.eclinm.2019.11.020 · Cited by 67 · see citing works

Performance of Cardiovascular Disease Risk Scores in People Diagnosed With Type 2 Diabetes: External Validation Using Data From the National Scottish Diabetes Register

Read, S. H., van Diepen, M., Colhoun, H. M., Halbesma, N., Lindsay, R. S., McKnight, J. A., McAllister, D., Pearson, E. R., Petrie, J. R., Philip, S., et al. (2018) · Diabetes Care · doi:10.2337/dc18-0578 · Cited by 66 · see citing works

A comparison of injury scoring systems in predicting burn mortality.

Halgas, B. J., Bay, R. C., & Foster, K. N. (2018) · Europe PMC (PubMed Central) · Cited by 66 · see citing works

Correction: The Kidney Failure Risk Equation for prediction of end stage renal disease in UK primary care: An external validation and clinical impact projection cohort study

Major, R., Shepherd, D. J., Medcalf, J. F., Xu, G., Gray, L. J., & Brunskill, N. J. (2020) · PLoS Medicine · doi:10.1371/journal.pmed.1003313 · Cited by 66 · see citing works

External validation of the Revised Cardiac Risk Index and National Surgical Quality Improvement Program Myocardial Infarction and Cardiac Arrest calculator in noncardiac vascular surgery

Fronczek, J., Polok, K., Devereaux, P. J., Górka, J., Archbold, A., Biccard, B. M., Duceppe, E., Manach, Y. L., Sessler, D. I., Duchińska, M., et al. (2019) · British Journal of Anaesthesia · doi:10.1016/j.bja.2019.05.029 · Cited by 62 · see citing works

PRECISE-DAPT score for bleeding risk prediction in patients on dual or single antiplatelet regimens: insights from the GLOBAL LEADERS and GLASSY

Gragnano, F., Heg, D., Franzone, A., McFadden, E. P., Leonardi, S., Piccolo, R., Vranckx, P., Branca, M., Serruys, P. W. J. C., Benit, E., et al. (2020) · European Heart Journal - Cardiovascular Pharmacotherapy · doi:10.1093/ehjcvp/pvaa106 · Cited by 62 · see citing works

Predicting Myocardial Injury and Other Cardiac Complications After Elective Noncardiac Surgery with the Revised Cardiac Risk Index: The VISION Study

Roshanov, P. S., Sessler, D. I., Chow, C. K., Garg, A. X., Walsh, M. W., Lam, N. N., Hildebrand, A. M., Biccard, B. M., Acedillo, R. R., MacNeil, S. D., et al. (2021) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2021.03.015 · Cited by 61 · see citing works

Clinical prediction in defined populations: a simulation study investigating when and how to aggregate existing models

Martin, G. P., Mamas, M. A., Peek, N. B., Buchan, I. E., & Sperrin, M. (2017) · BMC Medical Research Methodology · doi:10.1186/s12874-016-0277-1 · Cited by 60 · see citing works

Transparent reporting of multivariable prediction models developed or validated using clustered data (TRIPOD-Cluster): explanation and elaboration

Debray, T. P. A., Collins, P. G. S., Riley, R. D., Snell, K. I. E., CALSTER, B. V., Reitsma, J. B., & Moons, K. G. M. (2023) · BMJ · doi:10.1136/bmj-2022-071058 · Cited by 62 · see citing works

Hypotension Prediction Index with non-invasive continuous arterial pressure waveforms (ClearSight): clinical performance in Gynaecologic Oncologic Surgery

Frassanito, L., Giuri, P. P., Vassalli, F., Piersanti, A., Longo, A., Zanfini, B. A., Catarci, S., Fagotti, A., Scambia, G., & Draisci, G. (2021) · Journal of Clinical Monitoring and Computing · doi:10.1007/s10877-021-00763-4 · Cited by 58 · see citing works

Black Box Prediction Methods in Sports Medicine Deserve a Red Card for Reckless Practice: A Change of Tactics is Needed to Advance Athlete Care

Bullock, G. S., Hughes, T., Arundale, A. J. H., Ward, P., Collins, P. G. S., & Kluzek, S. (2022) · Sports Medicine · doi:10.1007/s40279-022-01655-6 · Cited by 57 · see citing works

Analysis of lung cancer risk model (PLCOM2012 and LLPv2) performance in a community-based lung cancer screening programme

Lebrett, M. B., Balata, H., Evison, M., Colligan, D., Duerden, R., Elton, P., Greaves, M., Howells, J., Irion, K., Karunaratne, D., et al. (2020) · Thorax · doi:10.1136/thoraxjnl-2020-214626 · Cited by 57 · see citing works

Neural network and logistic regression diagnostic prediction models for giant cell arteritis: development and validation

Ing, E. B., Miller, N. R., Nguyen, A. M., Su, W., Bursztyn, L. L. C. D., Poole, M., Kansal, V., Toren, A., Albreiki, D., Mouhanna, J. G., et al. (2019) · Clinical ophthalmology · doi:10.2147/opth.s193460 · Cited by 57 · see citing works

Estimation of required sample size for external validation of risk models for binary outcomes

Pavlou, M., Chen, Q., Omar, R., Seaman, S. R., Steyerberg, E. W., White, I. R., & Ambler, G. (2021) · Statistical Methods in Medical Research · doi:10.1177/09622802211007522 · Cited by 58 · see citing works

Machine learning to predict early recurrence after oesophageal cancer surgery

Rahman, S., Walker, R., Lloyd, M. A., Grace, B. L., van Boxel, G. I., Kingma, B. F., Ruurda, J. P. H., van Hillegersberg, R., Harris, S., Parsons, S. L., et al. (2020) · British journal of surgery · doi:10.1002/bjs.11461 · Cited by 56 · see citing works

The new simplified MARIA score applies beyond clinical trials: A suitable clinical practice tool for Crohn’s disease that parallels a simple endoscopic index and fecal calprotectin

Roseira, J., Ventosa, A. R., de Sousa, H. T., & Brito, J. B. (2020) · United European Gastroenterology Journal · doi:10.1177/2050640620943089 · Cited by 57 · see citing works

Metabolomic and genomic prediction of common diseases in 700,217 participants in three national biobanks

Nightingale Health Biobank Collaborative Group, Barrett, J. C., Esko, T., Fischer, K., Jostins-Dean, L., Jousilahti, P., Julkunen, H., Jääskeläinen, T., Kangas, A. J., Kerimov, N., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-54357-0 · Cited by 72 · see citing works

Prediction models for the mortality risk in chronic dialysis patients: a systematic review and independent external validation study

Ramspek, C. L., Voskamp, P. W. M., van Ittersum, F. J., Krediet, R. T., Dekker, F. W., & van Diepen, M. (2017) · Clinical Epidemiology · doi:10.2147/clep.s139748 · Cited by 55 · see citing works

Predicting Chemotherapy Toxicity in Older Patients with Cancer: A Multicenter Prospective Study

Feliú, J., Jiménez-Munárriz, B., Basterretxea, L., Paredero, I., Llabrés, E., Rebollo, M. A., Losada, B., Espinosa, E., Gironés, R., Custodio, A., et al. (2020) · The Oncologist · doi:10.1634/theoncologist.2019-0701 · Cited by 50 · see citing works

Derivation and Validation of a Biomarker-Based Clinical Algorithm to Rule Out Sepsis From Noninfectious Systemic Inflammatory Response Syndrome at Emergency Department Admission: A Multicenter Prospective Study*

Mearelli, F., Fiotti, N., Giansante, C., Casarsa, C., Orso, D., Helmersen, M. D., Altamura, N., Ruscio, M., Castello, L. M., Colonetti, E., et al. (2018) · Critical Care Medicine · doi:10.1097/ccm.0000000000003206 · Cited by 50 · see citing works

Development and external validation of DISPAIR fistula risk score for clinically relevant postoperative pancreatic fistula risk after distal pancreatectomy

Bonsdorff, A., Ghorbani, P., Helanterä, I., Tarvainen, T., Kontio, T., Belfrage, H., Sirén, J., Kokkola, A., Sparrelid, E., & Sallinen, V. J. (2022) · British journal of surgery · doi:10.1093/bjs/znac266 · Cited by 49 · see citing works

Show and tell: A critical review on robustness and uncertainty for a more responsible medical AI

Marconi, L., & Cabitza, F. (2025) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2025.105970 · Cited by 49 · see citing works

Development and external validation of the Psychosis Metabolic Risk Calculator (PsyMetRiC): a cardiometabolic risk prediction algorithm for young people with psychosis

Perry, B. I., Osimo, E. F., Upthegrove, R., Mallikarjun, P. K., Yorke, J., Štochl, J., Pérez, J., Zammit, S., Howes, O. D., Jones, P. B., et al. (2021) · The Lancet Psychiatry · doi:10.1016/s2215-0366(21)00114-0 · Cited by 49 · see citing works

Prediction modelling in the early detection of neonatal sepsis

Sahu, P., Stanly, E. A. R., Lewis, L., Prabhu, K., Rao, M., & Kunhikatta, V. (2022) · World Journal of Pediatrics · doi:10.1007/s12519-021-00505-1 · Cited by 53 · see citing works

Assessment of predictive performance of caries risk assessment models based on a systematic review and meta-analysis

Su, N., Lagerweij, M. D., & van der Heijden, G. J. M. G. (2021) · Journal of Dentistry · doi:10.1016/j.jdent.2021.103664 · Cited by 49 · see citing works

Validation and updating of risk models based on multinomial logistic regression

CALSTER, B. V., Hoorde, K. V., Vergouwe, Y., Bobdiwala, S., Condous, G. S., Kirk, E., Bourne, T., & Steyerberg, E. W. (2017) · Diagnostic and Prognostic Research · doi:10.1186/s41512-016-0002-x · Cited by 47 · see citing works

A novel risk calculator to predict outcome after surgery for symptomatic spinal metastases; use of a large prospective patient database to personalise surgical management

Choi, D., Pavlou, M., Omar, R., Arts, M. P., Balabaud, L., Buchowski, J. M., Bünger, C. E., Chung, C. K., Coppes, M. H., Depreitere, B., et al. (2018) · European Journal of Cancer · doi:10.1016/j.ejca.2018.11.011 · Cited by 47 · see citing works

Assessment and comparison of the max-ICH score and ICH score by external validation

Schmidt, F., Liotta, E. M., Prabhakaran, S., Naidech, A. M., & Maas, M. B. (2018) · Neurology · doi:10.1212/wnl.0000000000006117 · Cited by 46 · see citing works

A practical solution to estimate the sample size required for clinical prediction models generated from observational research on data

Baeza-Delgado, C., Alberich, L. C., Sierra, J. M. C., Veiga-Canuto, D., Heras, B. M. D. L., Raza, B., & Martí‐Bonmatí, L. (2022) · European Radiology Experimental · doi:10.1186/s41747-022-00276-y · Cited by 45 · see citing works

Discrimination and calibration properties of the hypotension probability indicator during cardiac and vascular surgery

Ranucci, M., Barile, L., Ambrogi, F., & Pistuddi, V. (2019) · Minerva Anestesiologica · doi:10.23736/s0375-9393.18.12620-4 · Cited by 45 · see citing works

Prediction models for development of retinopathy in people with type 2 diabetes: systematic review and external validation in a Dutch primary care setting

van der Heijden, A. A. W. A., Nijpels, G., Badloe, F. M. S., Lovejoy, H. L., Peelen, L. M., Feenstra, T., Moons, K. G. M., Slieker, R. C., Herings, R. M. C., Elders, P. J. M., et al. (2020) · Diabetologia · doi:10.1007/s00125-020-05134-3 · Cited by 45 · see citing works

Evaluation of clinical prediction models (part 3): calculating the sample size required for an external validation study

Riley, R. D., Snell, K. I. E., Archer, L., Ensor, J., Debray, T. P. A., CALSTER, B. V., van Smeden, M., & Collins, P. G. S. (2024) · BMJ · doi:10.1136/bmj-2023-074821 · Cited by 213 · see citing works

Minimum sample size for external validation of a clinical prediction model with a continuous outcome

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Minimum sample size for external validation of a clinical prediction model with a binary outcome

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Substantial effective sample sizes were required for external validation studies of predictive logistic regression models

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Estimation of required sample size for external validation of risk models for binary outcomes

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