TY - JOUR
AU - Reps, Jenna M.
AU - Kim, Chungsoo
AU - Williams, Ross D.
AU - Markus, Aniek F.
AU - Yang, Cynthia
AU - Salles, Talita Duarte
AU - Falconer, Thomas
AU - Jonnagaddala, Jitendra
AU - Williams, Andrew
AU - Fernández-Bertolín, Sergio
AU - DuVall, Scott L.
AU - Kostka, Kristin
AU - Rao, Gowtham A.
AU - Shoaibi, Azza
AU - Ostropolets, Anna
AU - Spotnitz, Matthew E.
AU - Zhang, Lin
AU - Casajust, Paula
AU - Steyerberg, Ewout W.
AU - Nyberg, Fredrik
AU - Kaas-Hansen, Benjamin Skov
AU - Choi, Young Hwa
AU - Morales, Daniel R.
AU - Liaw, Siaw-Teng
AU - Abrahão, Maria Tereza Fernandes
AU - Areia, Carlos
AU - Matheny, Michael E.
AU - Aragón, María
AU - Park, Rae Woong
AU - Hripcsak, George
AU - Reich, Christian G.
AU - Suchard, Marc A.
AU - You, Seng Chan
AU - Ryan, Patrick B.
AU - Prieto-Alhambra, Daniel
AU - Rijnbeek, Peter R.
PY - 2021
DA - April
TI - Implementation of the COVID-19 vulnerability index across an international network of health care data sets: Collaborative external validation study
JO - JMIR Medical Informatics 
VL - 9
IS - 4
DO - https://doi.org/10.2196/21547
AB - 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.
PB - JMIR Publications
UR - https://discovery.dundee.ac.uk/en/publications/02776465-7980-4170-a923-4874ce4b10ec
KW - Coronavirus (COVID-19)
ER