@article{Reps_J-2021_26425,
title = {Implementation of the COVID-19 vulnerability index across an international network of health care data sets: Collaborative external validation study},
author = {Reps, J. and Kim, C. and Williams, R. and Markus, A. and Yang, C. and Salles, T. and Falconer, T. and Jonnagaddala, J. and Williams, A. and Fernández-Bertolín, S. and DuVall, S. and Kostka, K. and Rao, G. and Shoaibi, A. and Ostropolets, A. and Spotnitz, M. and Zhang, L. and Casajust, P. and Steyerberg, E. and Nyberg, F. and Kaas-Hansen, B. and Choi, Y. and Morales, D. and Liaw, S. and Abrahão, M. and Areia, C. and Matheny, M. and Aragón, M. and Park, R. and Hripcsak, G. and Reich, C. and Suchard, M. and You, S. and Ryan, P. and Prieto-Alhambra, D. and Rijnbeek, P.},
month = {apr},
year = {2021},
abstract = {Background: SARS-CoV-2 is straining health care systems globally. The burden on hospitals during the pandemic could be reduced by implementing prediction models that can discriminate patients who require hospitalization from those who do not. The COVID-19 vulnerability (C-19) index, a model that predicts which patients will be admitted to hospital for treatment of pneumonia or pneumonia proxies, has been developed and proposed as a valuable tool for decision-making during the pandemic. However, the model is at high risk of bias according to the "prediction model risk of bias assessment" criteria, and it has not been externally validated.},
volume = {9},
issue = {4},
journal = {JMIR Medical Informatics },
publisher = {JMIR Publications},
url = {https://doi.org/10.2196/21547},
}
