Implementation of the COVID-19 vulnerability index across an international network of health care data sets: Collaborative external validation study

Jenna M. Reps*, Chungsoo Kim, Ross D. Williams, Aniek F. Markus, Cynthia Yang, Talita Duarte-Salles, Thomas Falconer, Jitendra Jonnagaddala, Andrew Williams, Sergio Fernández-Bertolín, Scott L. DuVall, Kristin Kostka, Gowtham Rao, Azza Shoaibi, Anna Ostropolets, Matthew E. Spotnitz, Lin Zhang, Paula Casajust, Ewout W. Steyerberg, Fredrik NybergBenjamin Skov Kaas-Hansen, Young Hwa Choi, Daniel Morales, Siaw Teng Liaw, Maria Tereza Fernandes Abrahão, Carlos Areia, Michael E. Matheny, Kristine E. Lynch, María Aragón, Rae Woong Park, George Hripcsak, Christian G. Reich, Marc A. Suchard, Seng Chan You, Patrick B. Ryan, Daniel Prieto-Alhambra, Peter R. Rijnbeek

*Corresponding author for this work

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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. Objective: The aim of this study was to externally validate the C-19 index across a range of health care settings to determine how well it broadly predicts hospitalization due to pneumonia in COVID-19 cases. Methods: We followed the Observational Health Data Sciences and Informatics (OHDSI) framework for external validation to assess the reliability of the C-19 index. We evaluated the model on two different target populations, 41,381 patients who presented with SARS-CoV-2 at an outpatient or emergency department visit and 9,429,285 patients who presented with influenza or related symptoms during an outpatient or emergency department visit, to predict their risk of hospitalization with pneumonia during the following 0-30 days. In total, we validated the model across a network of 14 databases spanning the United States, Europe, Australia, and Asia. Results: The internal validation performance of the C-19 index had a C statistic of 0.73, and the 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 data sets, respectively. The calibration was poor, with the model underestimating risk. When validated on 12 data sets containing influenza patients across the OHDSI network, the C statistics ranged between 0.40 and 0.68. Conclusions: 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.

Original languageEnglish
Article numbere21547
JournalJMIR Medical Informatics
Volume9
Issue number4
Early online date17 Jun 2020
DOIs
Publication statusPublished - Apr 2021

Bibliographical note

Funding Information:
We would like to acknowledge the patients who have contracted or died of this devastating disease, as well as their families and caregivers. We would also like to thank the health care professionals involved in the management of COVID-19 during these challenging times, from primary care to intensive care units. The authors appreciate the health care professionals dedicated to treating patients with COVID-19 in Korea and the Ministry of Health and Welfare and the Health Insurance Review & Assessment Service of Korea for sharing invaluable national health insurance claims data in a prompt manner. This project has received support from the European Health Data and Evidence Network (EHDEN) project. EHDEN received funding from the Innovative Medicines Initiative 2 Joint Undertaking (JU) under grant agreement No 806968. The JU receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA. This work was also supported by the Bio Industrial Strategic Technology Development Program (20001234, 20003883) funded by the Ministry of Trade, Industry & Energy (Korea) and a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea [grant number: HI16C0992]. This project is funded by the Health Department from the Generalitat de Catalunya with a grant for research projects on SARS-CoV-2 and COVID-19 disease organized by the Direcci? General de Recerca i Innovaci? en Salut. The University of Oxford received a grant related to this work from the Bill & Melinda Gates Foundation (Investment ID INV-016201) and partial support from the UK National Institute for Health Research (NIHR) Oxford Biomedical Research Centre. DPA is funded through a NIHR Senior Research Fellowship (Grant number SRF-2018-11-ST2-004). The views expressed in this publication are those of the author(s) and not necessarily those of the National Health Service, the National Institute for Health Research, the Department of Health, the Department of Veterans Affairs, or the United States Government. BSKH is funded through Innovation Fund Denmark (5153-00002B) and the Novo Nordisk Foundation (NNF14CC0001). This project is part funded by the University of New South Wales Research Infrastructure Scheme grant. SLD and MEM report funding from NIH NHBLI R-01, NIH NIDDK R-01 grant, and VA HSR&D. This work was supported using resources and facilities of the Department of Veterans Affairs (VA) Informatics and Computing Infrastructure (VINCI), VA HSR RES 13-457.

Funding Information:
DPA reports grants and other funding from AMGEN, grants, nonfinancial support and other from UCB Biopharma, and grants from Les Laboratoires Servier outside the submitted work; also, Janssen, on behalf of IMI-funded EHDEN and EMIF consortiums and Synapse Management Partners, has supported training programs organized by DPA's department and open for external participants. PRR reports grants from Innovative Medicines Initiative and grants from Janssen Research and Development, during the conduct of the study. CGR and KK report that they are employees of IQVIA. JMR, PBR, AS, and GR are compensated employees of Janssen Research & Development, JNJ. MAS reports receiving grants from US National Institutes of Health, grants from IQVIA, personal fees from Janssen Research and Development, personal fees from Private Health Management, during the conduct of the study. DM is supported by a Wellcome Trust Clinical Research Development Fellowship (Grant 214588/Z/18/Z) and reports grants from the Chief Scientist Office, Health Data Research UK, and NIHR outside the submitted work. GH reports receiving grants from the US National Institutes of Health National Library of Medicine during the conduct of the study and from Janssen Research outside the submitted work. BSKH reports receiving grants from Innovation Fund Denmark and Novo Nordisk Foundation outside the submitted work. SLD reports grants from Anolinx LLC, Astellas Pharma Inc, AstraZeneca Pharmaceuticals LP, Boehringer Ingelheim International GmbH, Celgene Corporation, Eli Lilly and Company, Genentech Inc, Genomic Health Inc, Gilead Sciences Inc, GlaxoSmithKline PLC, Innocrin Pharmaceuticals Inc, Janssen Pharmaceuticals Inc, Kantar Health, Myriad Genetic Laboratories Inc, Novartis International AG, Parexel International Corporation through the University of Utah or Western Institute for Veteran Research outside the submitted work.

Funding Information:
We would like to acknowledge the patients who have contracted or died of this devastating disease, as well as their families and caregivers. We would also like to thank the health care professionals involved in the management of COVID-19 during these challenging times, from primary care to intensive care units. The authors appreciate the health care professionals dedicated to treating patients with COVID-19 in Korea and the Ministry of Health and Welfare and the Health Insurance Review & Assessment Service of Korea for sharing invaluable national health insurance claims data in a prompt manner. This project has received support from the European Health Data and Evidence Network (EHDEN) project. EHDEN received funding from the Innovative Medicines Initiative 2 Joint Undertaking (JU) under grant agreement No 806968. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA. This work was also supported by the Bio Industrial Strategic Technology Development Program (20001234, 20003883) funded by the Ministry of Trade, Industry & Energy (Korea) and a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea [grant number: HI16C0992]. This project is funded by the Health Department from the Generalitat de Catalunya with a grant for research projects on SARS-CoV-2 and COVID-19 disease organized by the Direcció General de Recerca i Innovació en Salut. The University of Oxford received a grant related to this work from the Bill & Melinda Gates Foundation (Investment ID INV-016201) and partial support from the UK National Institute for Health Research (NIHR) Oxford Biomedical Research Centre. DPA is funded through a NIHR Senior Research Fellowship (Grant number SRF-2018-11-ST2-004). The views expressed in this publication are those of the author(s) and not necessarily those of the National Health Service, the National Institute for Health Research, the Department of Health, the Department of Veterans Affairs, or the United States Government. BSKH is funded through Innovation Fund Denmark (5153-00002B) and the Novo Nordisk Foundation (NNF14CC0001). This project is part funded by the University of New South Wales Research Infrastructure Scheme grant. SLD and MEM report funding from NIH NHBLI R-01, NIH NIDDK R-01 grant, and VA HSR&D. This work was supported using resources and facilities of the Department of Veterans Affairs (VA) Informatics and Computing Infrastructure (VINCI), VA HSR RES 13-457.

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