Don't be misled: 3 misconceptions about external validation of clinical prediction models

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 vali...

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Bibliographic Details
Published in:Journal of clinical epidemiology 2024-08, Vol.172, p.111387, Article 111387
Main Authors: la Roi-Teeuw, Hannah M., van Royen, Florien S., de Hond, Anne, Zahra, Anum, de Vries, Sjoerd, Bartels, Richard, Carriero, Alex J., van Doorn, Sander, Dunias, Zoë S., Kant, Ilse, Leeuwenberg, Tuur, Peters, Ruben, Veerhoek, Laura, van Smeden, Maarten, Luijken, Kim
Format: Article
Language:English
Subjects:
Artificial intelligence
Clinical algorithm
Clinical prediction model
Decision Support Techniques
Design
External validation
Health risks
Humans
Internal validation
Machine learning
Model updating
Models, Statistical
Nursing homes
Prediction model
Prediction models
Pulmonary embolisms
Regression modelling
Reproducibility of Results
Semantics
Study design
Validation studies
Validation Studies as Topic
Validity
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Summary: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.
ISSN:0895-4356
1878-5921
1878-5921
DOI:10.1016/j.jclinepi.2024.111387