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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Bibliographic Details
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
Online Access:Get full text
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Summary: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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ISSN:1741-7015
1741-7015
DOI:10.1186/s12916-023-02779-w