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...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	BMC medicine Ročník 21; číslo 1; s. 70 - 8
Hlavní autoři:	Van Calster, Ben, Steyerberg, Ewout W., Wynants, Laure, van Smeden, Maarten
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London BioMed Central 24.02.2023 BioMed Central Ltd Springer Nature B.V BMC
Témata:	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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Popis
Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1741-7015 1741-7015
DOI:	10.1186/s12916-023-02779-w