Implementation of the COVID-19 Vulnerability Index Across an International Network of Health Care Data Sets: Collaborative External Validation Study.
Jenna Marie RepsChungsoo KimRoss D WilliamsAniek F MarkusCynthia YangTalita Duarte SallesThomas FalconerJitendra JonnagaddalaAndrew E WilliamsSergio Fernandez-BertolinScott L DuvallKristin KostkaGowtham RaoAzza ShoaibAnna OstropoletsMatthew E SpotnitzLin ZhangPaula CasajustEwout Willem SteyerbergFredrik NybergBenjamin Skov Kaas-HansenYoung Hwa ChoiDaniel R MoralesSiaw-Teng LiawMaria Tereza Fernandes AbrahãoCarlos Morgado AreiaMichael E MathenyKristine E LynchMaria AragónRae Woong ParkGeorge HripcsakChristian G ReichMarc A SuchardSeng Chan YouPatrick B RyanDaniel Prieto AlhambraPeter R RijnbeekPublished in: JMIR medical informatics (2021)
Our results show that the discriminative performance of the C-19 index model is low for influenza cohorts and even worse among patients with COVID-19 in the United States, Spain, and South Korea. These results suggest that C-19 should not be used to aid decision-making during the COVID-19 pandemic. Our findings highlight the importance of performing external validation across a range of settings, especially when a prediction model is being extrapolated to a different population. In the field of prediction, extensive validation is required to create appropriate trust in a model.