There is no such thing as a validated prediction model

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 cont...

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Published in:	BMC medicine Vol. 21; no. 1; pp. 70 - 8
Main Authors:	Van Calster, Ben, Steyerberg, Ewout W., Wynants, Laure, van Smeden, Maarten
Format:	Journal Article
Language:	English
Published:	London BioMed Central 24.02.2023 BioMed Central Ltd Springer Nature B.V BMC
Subjects:	Biomarkers Biomedicine Calibration Clinical medicine Datasets Decision making Delirium Estimates External validation Fractures Health aspects Health risk assessment Heterogeneity Hospitals Internal validation Medicine Medicine & Public Health Methods Model performance Mortality Oncology Opinion Ovarian cancer Patients Populations Practice Prediction models Predictive analytics Pulmonary embolisms Risk prediction models Scanners Variables Netherlands Heterogeneity Model performance Discrimination External validation Risk prediction models Calibration Internal validation Predictive analytics
ISSN:	1741-7015, 1741-7015
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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.
AbstractList	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? 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. 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. 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. Keywords: Risk prediction models, Predictive analytics, Internal validation, External validation, Heterogeneity, Model performance, Calibration, Discrimination 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. 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. 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?BACKGROUNDClinical 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?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.MAIN BODYWe 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.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.CONCLUSIONPrincipled 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. 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? 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. BackgroundClinical 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 bodyWe 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.ConclusionPrincipled 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.
ArticleNumber	70
Audience	Academic
Author	van Smeden, Maarten Van Calster, Ben Steyerberg, Ewout W. Wynants, Laure
Author_xml	– sequence: 1 givenname: Ben orcidid: 0000-0003-1613-7450 surname: Van Calster fullname: Van Calster, Ben organization: Department of Development and Regeneration, KU Leuven, EPI-Center, KU Leuven, Department of Biomedical Data Sciences, Leiden University Medical Center – sequence: 2 givenname: Ewout W. orcidid: 0000-0002-7787-0122 surname: Steyerberg fullname: Steyerberg, Ewout W. organization: Department of Development and Regeneration, KU Leuven – sequence: 3 givenname: Laure orcidid: 0000-0002-3037-122X surname: Wynants fullname: Wynants, Laure organization: Department of Development and Regeneration, KU Leuven, EPI-Center, KU Leuven, Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University – sequence: 4 givenname: Maarten orcidid: 0000-0002-5529-1541 surname: van Smeden fullname: van Smeden, Maarten email: M.vanSmeden@umcutrecht.nl organization: Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University
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PublicationDecade	2020
PublicationPlace	London
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PublicationTitle	BMC medicine
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PublicationTitleAlternate	BMC Med
PublicationYear	2023
Publisher	BioMed Central BioMed Central Ltd Springer Nature B.V BMC
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Snippet	Background Clinical prediction models should be validated before implementation in clinical practice. But is favorable performance at internal validation or... Clinical prediction models should be validated before implementation in clinical practice. But is favorable performance at internal validation or one external... Background Clinical prediction models should be validated before implementation in clinical practice. But is favorable performance at internal validation or... BackgroundClinical prediction models should be validated before implementation in clinical practice. But is favorable performance at internal validation or one... Abstract Background Clinical prediction models should be validated before implementation in clinical practice. But is favorable performance at internal...
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SubjectTerms	Biomarkers Biomedicine Calibration Clinical medicine Datasets Decision making Delirium Estimates External validation Fractures Health aspects Health risk assessment Heterogeneity Hospitals Internal validation Medicine Medicine & Public Health Methods Model performance Mortality Oncology Opinion Ovarian cancer Patients Populations Practice Prediction models Predictive analytics Pulmonary embolisms Risk prediction models Scanners Variables
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Title	There is no such thing as a validated prediction model
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