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Implementation of the COVID-19 Vulnerability Index Across an International Network of Health Care Data Sets: Collaborative External Validation Study

Authors :
Reps, Jenna M
Kim, Chungsoo
Williams, Ross D
Markus, Aniek F
Yang, Cynthia
Salles, Talita Duarte
Falconer, Thomas
Jonnagaddala, Jitendra
Williams, Andrew
Fernández-Bertolín, Sergio
DuVall, Scott L
Kostka, Kristin
Rao, Gowtham
Shoaibi, Azza
Ostropolets, Anna
Spotnitz, Matthew E
Zhang, Lin
Casajust, Paula
Steyerberg, Ewout W
Nyberg, Fredrik
Kaas-Hansen, Benjamin Skov
Choi, Young Hwa
Morales, Daniel
Liaw, Siaw-Teng
Abrahão, Maria Tereza Fernandes
Areia, Carlos
Matheny, Michael E
Aragón, María
Park, Rae Woong
Hripcsak, George
Reich, Christian G
Suchard, Marc A
You, Seng Chan
Ryan, Patrick B
Prieto-Alhambra, Daniel
Rijnbeek, Peter R
Reps, Jenna M
Kim, Chungsoo
Williams, Ross D
Markus, Aniek F
Yang, Cynthia
Salles, Talita Duarte
Falconer, Thomas
Jonnagaddala, Jitendra
Williams, Andrew
Fernández-Bertolín, Sergio
DuVall, Scott L
Kostka, Kristin
Rao, Gowtham
Shoaibi, Azza
Ostropolets, Anna
Spotnitz, Matthew E
Zhang, Lin
Casajust, Paula
Steyerberg, Ewout W
Nyberg, Fredrik
Kaas-Hansen, Benjamin Skov
Choi, Young Hwa
Morales, Daniel
Liaw, Siaw-Teng
Abrahão, Maria Tereza Fernandes
Areia, Carlos
Matheny, Michael E
Aragón, María
Park, Rae Woong
Hripcsak, George
Reich, Christian G
Suchard, Marc A
You, Seng Chan
Ryan, Patrick B
Prieto-Alhambra, Daniel
Rijnbeek, Peter R
Source :
Reps , J M , Kim , C , Williams , R D , Markus , A F , Yang , C , Salles , T D , Falconer , T , Jonnagaddala , J , Williams , A , Fernández-Bertolín , S , DuVall , S L , Kostka , K , Rao , G , Shoaibi , A , Ostropolets , A , Spotnitz , M E , Zhang , L , Casajust , P , Steyerberg , E W , Nyberg , F , Kaas-Hansen , B S , Choi , Y H , Morales , D , Liaw , S-T , Abrahão , M T F , Areia , C , Matheny , M E , Aragón , M , Park , R W , Hripcsak , G , Reich , C G , Suchard , M A , You , S C , Ryan , P B , Prieto-Alhambra , D & Rijnbeek , P R 2021 , ' Implementation of the COVID-19 Vulnerability Index Across an International Network of Health Care Data Sets: Collaborative External Validation Study ' , J M I R Medical Informatics , vol. 9 , no. 4 .
Publication Year :
2021

Abstract

BACKGROUND: SARS-CoV-2 is straining healthcare systems globally. The burden on hospitals during the pandemic could be reduced by implementing prediction models that can discriminate between patients requiring hospitalization and 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 has not been externally validated.OBJECTIVE: Externally validate the C-19 index across a range of healthcare settings to determine how well it broadly predicts hospitalization due to pneumonia in COVID-19 cases.METHODS: We followed the OHDSI framework for external validation to assess the reliability of the C-19 model. We evaluated the model on two different target populations: i) 41,381 patients that have SARS-CoV-2 at an outpatient or emergency room visit and ii) 9,429,285 patients that have influenza or related symptoms during an outpatient or emergency room visit, to predict their risk of hospitalization with pneumonia during the following 0 to 30 days. In total we validated the model across a network of 14 databases spanning the US, Europe, Australia and Asia.RESULTS: The internal validation performance of the C-19 index was a c-statistic of 0.73 and calibration was not reported by the authors. When we externally validated it by transporting it to SARS-CoV-2 data the model obtained c-statistics of 0.36, 0.53 (0.473-0.584) and 0.56 (0.488-0.636) on Spanish, US and South Korean datasets respectively. The calibration was poor with the model under-estimating risk. When validated on 12 datasets containing influenza patients across the OHDSI network the c-statistics ranged between 0.40-0.68.CONCLUSIONS: The results show that the discrimi

Details

Database :
OAIster
Journal :
Reps , J M , Kim , C , Williams , R D , Markus , A F , Yang , C , Salles , T D , Falconer , T , Jonnagaddala , J , Williams , A , Fernández-Bertolín , S , DuVall , S L , Kostka , K , Rao , G , Shoaibi , A , Ostropolets , A , Spotnitz , M E , Zhang , L , Casajust , P , Steyerberg , E W , Nyberg , F , Kaas-Hansen , B S , Choi , Y H , Morales , D , Liaw , S-T , Abrahão , M T F , Areia , C , Matheny , M E , Aragón , M , Park , R W , Hripcsak , G , Reich , C G , Suchard , M A , You , S C , Ryan , P B , Prieto-Alhambra , D & Rijnbeek , P R 2021 , ' Implementation of the COVID-19 Vulnerability Index Across an International Network of Health Care Data Sets: Collaborative External Validation Study ' , J M I R Medical Informatics , vol. 9 , no. 4 .
Notes :
application/pdf, application/pdf, English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1263226309
Document Type :
Electronic Resource