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Towards clinical data-driven eligibility criteria optimization for interventional COVID-19 clinical trials
OBJECTIVE: This research aims to evaluate the impact of eligibility criteria on recruitment and observable clinical outcomes of COVID-19 clinical trials using electronic health record (EHR) data. MATERIALS AND METHODS: On June 18, 2020, we identified frequently used eligibility criteria from all the...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7798960/ https://www.ncbi.nlm.nih.gov/pubmed/33260201 http://dx.doi.org/10.1093/jamia/ocaa276 |
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author | Kim, Jae Hyun Ta, Casey N Liu, Cong Sung, Cynthia Butler, Alex M Stewart, Latoya A Ena, Lyudmila Rogers, James R Lee, Junghwan Ostropolets, Anna Ryan, Patrick B Liu, Hao Lee, Shing M Elkind, Mitchell S V Weng, Chunhua |
author_facet | Kim, Jae Hyun Ta, Casey N Liu, Cong Sung, Cynthia Butler, Alex M Stewart, Latoya A Ena, Lyudmila Rogers, James R Lee, Junghwan Ostropolets, Anna Ryan, Patrick B Liu, Hao Lee, Shing M Elkind, Mitchell S V Weng, Chunhua |
author_sort | Kim, Jae Hyun |
collection | PubMed |
description | OBJECTIVE: This research aims to evaluate the impact of eligibility criteria on recruitment and observable clinical outcomes of COVID-19 clinical trials using electronic health record (EHR) data. MATERIALS AND METHODS: On June 18, 2020, we identified frequently used eligibility criteria from all the interventional COVID-19 trials in ClinicalTrials.gov (n = 288), including age, pregnancy, oxygen saturation, alanine/aspartate aminotransferase, platelets, and estimated glomerular filtration rate. We applied the frequently used criteria to the EHR data of COVID-19 patients in Columbia University Irving Medical Center (CUIMC) (March 2020–June 2020) and evaluated their impact on patient accrual and the occurrence of a composite endpoint of mechanical ventilation, tracheostomy, and in-hospital death. RESULTS: There were 3251 patients diagnosed with COVID-19 from the CUIMC EHR included in the analysis. The median follow-up period was 10 days (interquartile range 4–28 days). The composite events occurred in 18.1% (n = 587) of the COVID-19 cohort during the follow-up. In a hypothetical trial with common eligibility criteria, 33.6% (690/2051) were eligible among patients with evaluable data and 22.2% (153/690) had the composite event. DISCUSSION: By adjusting the thresholds of common eligibility criteria based on the characteristics of COVID-19 patients, we could observe more composite events from fewer patients. CONCLUSIONS: This research demonstrated the potential of using the EHR data of COVID-19 patients to inform the selection of eligibility criteria and their thresholds, supporting data-driven optimization of participant selection towards improved statistical power of COVID-19 trials. |
format | Online Article Text |
id | pubmed-7798960 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-77989602021-01-25 Towards clinical data-driven eligibility criteria optimization for interventional COVID-19 clinical trials Kim, Jae Hyun Ta, Casey N Liu, Cong Sung, Cynthia Butler, Alex M Stewart, Latoya A Ena, Lyudmila Rogers, James R Lee, Junghwan Ostropolets, Anna Ryan, Patrick B Liu, Hao Lee, Shing M Elkind, Mitchell S V Weng, Chunhua J Am Med Inform Assoc Research and Applications OBJECTIVE: This research aims to evaluate the impact of eligibility criteria on recruitment and observable clinical outcomes of COVID-19 clinical trials using electronic health record (EHR) data. MATERIALS AND METHODS: On June 18, 2020, we identified frequently used eligibility criteria from all the interventional COVID-19 trials in ClinicalTrials.gov (n = 288), including age, pregnancy, oxygen saturation, alanine/aspartate aminotransferase, platelets, and estimated glomerular filtration rate. We applied the frequently used criteria to the EHR data of COVID-19 patients in Columbia University Irving Medical Center (CUIMC) (March 2020–June 2020) and evaluated their impact on patient accrual and the occurrence of a composite endpoint of mechanical ventilation, tracheostomy, and in-hospital death. RESULTS: There were 3251 patients diagnosed with COVID-19 from the CUIMC EHR included in the analysis. The median follow-up period was 10 days (interquartile range 4–28 days). The composite events occurred in 18.1% (n = 587) of the COVID-19 cohort during the follow-up. In a hypothetical trial with common eligibility criteria, 33.6% (690/2051) were eligible among patients with evaluable data and 22.2% (153/690) had the composite event. DISCUSSION: By adjusting the thresholds of common eligibility criteria based on the characteristics of COVID-19 patients, we could observe more composite events from fewer patients. CONCLUSIONS: This research demonstrated the potential of using the EHR data of COVID-19 patients to inform the selection of eligibility criteria and their thresholds, supporting data-driven optimization of participant selection towards improved statistical power of COVID-19 trials. Oxford University Press 2020-12-01 /pmc/articles/PMC7798960/ /pubmed/33260201 http://dx.doi.org/10.1093/jamia/ocaa276 Text en © The Author(s) 2020. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) |
spellingShingle | Research and Applications Kim, Jae Hyun Ta, Casey N Liu, Cong Sung, Cynthia Butler, Alex M Stewart, Latoya A Ena, Lyudmila Rogers, James R Lee, Junghwan Ostropolets, Anna Ryan, Patrick B Liu, Hao Lee, Shing M Elkind, Mitchell S V Weng, Chunhua Towards clinical data-driven eligibility criteria optimization for interventional COVID-19 clinical trials |
title | Towards clinical data-driven eligibility criteria optimization for interventional COVID-19 clinical trials |
title_full | Towards clinical data-driven eligibility criteria optimization for interventional COVID-19 clinical trials |
title_fullStr | Towards clinical data-driven eligibility criteria optimization for interventional COVID-19 clinical trials |
title_full_unstemmed | Towards clinical data-driven eligibility criteria optimization for interventional COVID-19 clinical trials |
title_short | Towards clinical data-driven eligibility criteria optimization for interventional COVID-19 clinical trials |
title_sort | towards clinical data-driven eligibility criteria optimization for interventional covid-19 clinical trials |
topic | Research and Applications |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7798960/ https://www.ncbi.nlm.nih.gov/pubmed/33260201 http://dx.doi.org/10.1093/jamia/ocaa276 |
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