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Improving and Externally Validating Mortality Prediction Models for COVID-19 Using Publicly Available Data

The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands
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BioMed 2022, 2(1), 13-26; https://doi.org/10.3390/biomed2010002
Received: 22 October 2021 / Revised: 11 December 2021 / Accepted: 31 December 2021 / Published: 5 January 2022
We conducted a systematic survey of COVID-19 endpoint prediction literature to: (a) identify publications that include data that adhere to FAIR (findability, accessibility, interoperability, and reusability) principles and (b) develop and reuse mortality prediction models that best generalize to these datasets. The largest such cohort data we knew of was used for model development. The associated published prediction model was subjected to recursive feature elimination to find a minimal logistic regression model which had statistically and clinically indistinguishable predictive performance. This model could still not be applied to the four external validation sets that were identified, due to complete absence of needed model features in some external sets. Thus, a generalizable model (GM) was built which could be applied to all four external validation sets. An age-only model was used as a benchmark, as it is the simplest, effective, and robust predictor of mortality currently known in COVID-19 literature. While the GM surpassed the age-only model in three external cohorts, for the fourth external cohort, there was no statistically significant difference. This study underscores: (1) the paucity of FAIR data being shared by researchers despite the glut of COVID-19 prediction models and (2) the difficulty of creating any model that consistently outperforms an age-only model due to the cohort diversity of available datasets. View Full-Text
Keywords: COVID-19; prediction modeling; machine learning; external validation; replicability; FAIR data COVID-19; prediction modeling; machine learning; external validation; replicability; FAIR data
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MDPI and ACS Style

Chatterjee, A.; Wilmink, G.; Woodruff, H.; Lambin, P. Improving and Externally Validating Mortality Prediction Models for COVID-19 Using Publicly Available Data. BioMed 2022, 2, 13-26. https://doi.org/10.3390/biomed2010002

AMA Style

Chatterjee A, Wilmink G, Woodruff H, Lambin P. Improving and Externally Validating Mortality Prediction Models for COVID-19 Using Publicly Available Data. BioMed. 2022; 2(1):13-26. https://doi.org/10.3390/biomed2010002

Chicago/Turabian Style

Chatterjee, Avishek, Guus Wilmink, Henry Woodruff, and Philippe Lambin. 2022. "Improving and Externally Validating Mortality Prediction Models for COVID-19 Using Publicly Available Data" BioMed 2, no. 1: 13-26. https://doi.org/10.3390/biomed2010002

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