Loading…

Development and Validation of a Clinical Risk Score to Predict Hospitalization Within 30 Days of Coronavirus Disease 2019 Diagnosis

ABSTRACT Introduction Early identification of patients with coronavirus disease 2019 (COVID-19) who are at risk for hospitalization may help to mitigate disease burden by allowing healthcare systems to conduct sufficient resource and logistical planning in the event of case surges. We sought to deve...

Full description

Saved in:
Bibliographic Details
Published in:Military medicine 2023-03, Vol.188 (3-4), p.e833-e840
Main Authors: Aboumrad, Maya, Zwain, Gabrielle, Smith, Jeremy, Neupane, Nabin, Powell, Ethan, Dempsey, Brendan, Reyes, Carolina, Satram, Sacha, Young-Xu, Yinong
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c424t-fa0659bda0abb6391dce13f6356a773f5484a9a896bae12954ad90424aad6d7a3
cites cdi_FETCH-LOGICAL-c424t-fa0659bda0abb6391dce13f6356a773f5484a9a896bae12954ad90424aad6d7a3
container_end_page e840
container_issue 3-4
container_start_page e833
container_title Military medicine
container_volume 188
creator Aboumrad, Maya
Zwain, Gabrielle
Smith, Jeremy
Neupane, Nabin
Powell, Ethan
Dempsey, Brendan
Reyes, Carolina
Satram, Sacha
Young-Xu, Yinong
description ABSTRACT Introduction Early identification of patients with coronavirus disease 2019 (COVID-19) who are at risk for hospitalization may help to mitigate disease burden by allowing healthcare systems to conduct sufficient resource and logistical planning in the event of case surges. We sought to develop and validate a clinical risk score that uses readily accessible information at testing to predict individualized 30-day hospitalization risk following COVID-19 diagnosis. Methods We assembled a retrospective cohort of U.S. Veterans Health Administration patients (age ≥ 18 years) diagnosed with COVID-19 between March 1, 2020, and December 31, 2020. We screened patient characteristics using Least Absolute Shrinkage and Selection Operator logistic regression and constructed the risk score using characteristics identified as most predictive for hospitalization. Patients diagnosed before November 1, 2020, comprised the development cohort, while those diagnosed on or after November 1, 2020, comprised the validation cohort. We assessed risk score discrimination by calculating the area under the receiver operating characteristic (AUROC) curve and calibration using the Hosmer–Lemeshow (HL) goodness-of-fit test. This study was approved by the Veteran’s Institutional Review Board of Northern New England at the White River Junction Veterans Affairs Medical Center (Reference no.:1473972-1). Results The development and validation cohorts comprised 11,473 and 12,970 patients, of whom 4,465 (38.9%) and 3,669 (28.3%) were hospitalized, respectively. The independent predictors for hospitalization included in the risk score were increasing age, male sex, non-white race, Hispanic ethnicity, homelessness, nursing home/long-term care residence, unemployed or retired status, fever, fatigue, diarrhea, nausea, cough, diabetes, chronic kidney disease, hypertension, and chronic obstructive pulmonary disease. Model discrimination and calibration was good for the development (AUROC = 0.80; HL P-value = .05) and validation (AUROC = 0.80; HL P-value = .31) cohorts. Conclusions The prediction tool developed in this study demonstrated that it could identify patients with COVID-19 who are at risk for hospitalization. This could potentially inform clinicians and policymakers of patients who may benefit most from early treatment interventions and help healthcare systems anticipate capacity surges.
doi_str_mv 10.1093/milmed/usab415
format article
fullrecord <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_8522374</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><oup_id>10.1093/milmed/usab415</oup_id><sourcerecordid>2579632068</sourcerecordid><originalsourceid>FETCH-LOGICAL-c424t-fa0659bda0abb6391dce13f6356a773f5484a9a896bae12954ad90424aad6d7a3</originalsourceid><addsrcrecordid>eNqFkcFv1SAcx8mi2d6mV4-Gox66QaG0XEzMe7qZLHFRN72RXwvd2ChUaF-yXf3HZelz0ZMnQvh8P_zgi9ArSo4pkexksG4w-mRO0HJa7aEVlYwUgrIfz9CKkFIUnNTVATpM6ZYQymVD99EB44LSmvAV-rUxW-PCOBg_YfAaX4GzGiYbPA49Brx21tsOHP5i0x3-2oVo8BTwRTTadhM-C2m0U848LJnvdrqxHjOCN3CfHhXrEIOHrY1zwhubDCSDS0Jl3sC1D8mmF-h5Dy6Zl7v1CF1-_PBtfVacfz79tH5_XnS85FPRAxGVbDUQaFvBJNWdoawXrBJQ16yveMNBQiNFC4aWsuKgJclRAC10DewIvVu849zmT-vykyM4NUY7QLxXAaz698TbG3UdtqqpypLVPAve7AQx_JxNmtRgU2ecA2_CnFRZ1VKwkogmo8cL2sWQUjT90zWUqMfm1NKc2jWXA6__Hu4J_1NVBt4uQJjH_8l-AzJ4p2k</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2579632068</pqid></control><display><type>article</type><title>Development and Validation of a Clinical Risk Score to Predict Hospitalization Within 30 Days of Coronavirus Disease 2019 Diagnosis</title><source>Oxford Journals Online</source><creator>Aboumrad, Maya ; Zwain, Gabrielle ; Smith, Jeremy ; Neupane, Nabin ; Powell, Ethan ; Dempsey, Brendan ; Reyes, Carolina ; Satram, Sacha ; Young-Xu, Yinong</creator><creatorcontrib>Aboumrad, Maya ; Zwain, Gabrielle ; Smith, Jeremy ; Neupane, Nabin ; Powell, Ethan ; Dempsey, Brendan ; Reyes, Carolina ; Satram, Sacha ; Young-Xu, Yinong</creatorcontrib><description>ABSTRACT Introduction Early identification of patients with coronavirus disease 2019 (COVID-19) who are at risk for hospitalization may help to mitigate disease burden by allowing healthcare systems to conduct sufficient resource and logistical planning in the event of case surges. We sought to develop and validate a clinical risk score that uses readily accessible information at testing to predict individualized 30-day hospitalization risk following COVID-19 diagnosis. Methods We assembled a retrospective cohort of U.S. Veterans Health Administration patients (age ≥ 18 years) diagnosed with COVID-19 between March 1, 2020, and December 31, 2020. We screened patient characteristics using Least Absolute Shrinkage and Selection Operator logistic regression and constructed the risk score using characteristics identified as most predictive for hospitalization. Patients diagnosed before November 1, 2020, comprised the development cohort, while those diagnosed on or after November 1, 2020, comprised the validation cohort. We assessed risk score discrimination by calculating the area under the receiver operating characteristic (AUROC) curve and calibration using the Hosmer–Lemeshow (HL) goodness-of-fit test. This study was approved by the Veteran’s Institutional Review Board of Northern New England at the White River Junction Veterans Affairs Medical Center (Reference no.:1473972-1). Results The development and validation cohorts comprised 11,473 and 12,970 patients, of whom 4,465 (38.9%) and 3,669 (28.3%) were hospitalized, respectively. The independent predictors for hospitalization included in the risk score were increasing age, male sex, non-white race, Hispanic ethnicity, homelessness, nursing home/long-term care residence, unemployed or retired status, fever, fatigue, diarrhea, nausea, cough, diabetes, chronic kidney disease, hypertension, and chronic obstructive pulmonary disease. Model discrimination and calibration was good for the development (AUROC = 0.80; HL P-value = .05) and validation (AUROC = 0.80; HL P-value = .31) cohorts. Conclusions The prediction tool developed in this study demonstrated that it could identify patients with COVID-19 who are at risk for hospitalization. This could potentially inform clinicians and policymakers of patients who may benefit most from early treatment interventions and help healthcare systems anticipate capacity surges.</description><identifier>ISSN: 0026-4075</identifier><identifier>EISSN: 1930-613X</identifier><identifier>DOI: 10.1093/milmed/usab415</identifier><identifier>PMID: 34611704</identifier><language>eng</language><publisher>US: Oxford University Press</publisher><subject>Adolescent ; COVID-19 - diagnosis ; COVID-19 - epidemiology ; COVID-19 Testing ; Hospitalization ; Humans ; Male ; Retrospective Studies ; Risk Factors</subject><ispartof>Military medicine, 2023-03, Vol.188 (3-4), p.e833-e840</ispartof><rights>Published by Oxford University Press on behalf of the Association of Military Surgeons of the United States 2021. This work is written by (a) US Government employee(s) and is in the public domain in the US. 2021</rights><rights>Published by Oxford University Press on behalf of the Association of Military Surgeons of the United States 2021. This work is written by (a) US Government employee(s) and is in the public domain in the US.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c424t-fa0659bda0abb6391dce13f6356a773f5484a9a896bae12954ad90424aad6d7a3</citedby><cites>FETCH-LOGICAL-c424t-fa0659bda0abb6391dce13f6356a773f5484a9a896bae12954ad90424aad6d7a3</cites><orcidid>0000-0001-6140-4250</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,780,784,885,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34611704$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Aboumrad, Maya</creatorcontrib><creatorcontrib>Zwain, Gabrielle</creatorcontrib><creatorcontrib>Smith, Jeremy</creatorcontrib><creatorcontrib>Neupane, Nabin</creatorcontrib><creatorcontrib>Powell, Ethan</creatorcontrib><creatorcontrib>Dempsey, Brendan</creatorcontrib><creatorcontrib>Reyes, Carolina</creatorcontrib><creatorcontrib>Satram, Sacha</creatorcontrib><creatorcontrib>Young-Xu, Yinong</creatorcontrib><title>Development and Validation of a Clinical Risk Score to Predict Hospitalization Within 30 Days of Coronavirus Disease 2019 Diagnosis</title><title>Military medicine</title><addtitle>Mil Med</addtitle><description>ABSTRACT Introduction Early identification of patients with coronavirus disease 2019 (COVID-19) who are at risk for hospitalization may help to mitigate disease burden by allowing healthcare systems to conduct sufficient resource and logistical planning in the event of case surges. We sought to develop and validate a clinical risk score that uses readily accessible information at testing to predict individualized 30-day hospitalization risk following COVID-19 diagnosis. Methods We assembled a retrospective cohort of U.S. Veterans Health Administration patients (age ≥ 18 years) diagnosed with COVID-19 between March 1, 2020, and December 31, 2020. We screened patient characteristics using Least Absolute Shrinkage and Selection Operator logistic regression and constructed the risk score using characteristics identified as most predictive for hospitalization. Patients diagnosed before November 1, 2020, comprised the development cohort, while those diagnosed on or after November 1, 2020, comprised the validation cohort. We assessed risk score discrimination by calculating the area under the receiver operating characteristic (AUROC) curve and calibration using the Hosmer–Lemeshow (HL) goodness-of-fit test. This study was approved by the Veteran’s Institutional Review Board of Northern New England at the White River Junction Veterans Affairs Medical Center (Reference no.:1473972-1). Results The development and validation cohorts comprised 11,473 and 12,970 patients, of whom 4,465 (38.9%) and 3,669 (28.3%) were hospitalized, respectively. The independent predictors for hospitalization included in the risk score were increasing age, male sex, non-white race, Hispanic ethnicity, homelessness, nursing home/long-term care residence, unemployed or retired status, fever, fatigue, diarrhea, nausea, cough, diabetes, chronic kidney disease, hypertension, and chronic obstructive pulmonary disease. Model discrimination and calibration was good for the development (AUROC = 0.80; HL P-value = .05) and validation (AUROC = 0.80; HL P-value = .31) cohorts. Conclusions The prediction tool developed in this study demonstrated that it could identify patients with COVID-19 who are at risk for hospitalization. This could potentially inform clinicians and policymakers of patients who may benefit most from early treatment interventions and help healthcare systems anticipate capacity surges.</description><subject>Adolescent</subject><subject>COVID-19 - diagnosis</subject><subject>COVID-19 - epidemiology</subject><subject>COVID-19 Testing</subject><subject>Hospitalization</subject><subject>Humans</subject><subject>Male</subject><subject>Retrospective Studies</subject><subject>Risk Factors</subject><issn>0026-4075</issn><issn>1930-613X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNqFkcFv1SAcx8mi2d6mV4-Gox66QaG0XEzMe7qZLHFRN72RXwvd2ChUaF-yXf3HZelz0ZMnQvh8P_zgi9ArSo4pkexksG4w-mRO0HJa7aEVlYwUgrIfz9CKkFIUnNTVATpM6ZYQymVD99EB44LSmvAV-rUxW-PCOBg_YfAaX4GzGiYbPA49Brx21tsOHP5i0x3-2oVo8BTwRTTadhM-C2m0U848LJnvdrqxHjOCN3CfHhXrEIOHrY1zwhubDCSDS0Jl3sC1D8mmF-h5Dy6Zl7v1CF1-_PBtfVacfz79tH5_XnS85FPRAxGVbDUQaFvBJNWdoawXrBJQ16yveMNBQiNFC4aWsuKgJclRAC10DewIvVu849zmT-vykyM4NUY7QLxXAaz698TbG3UdtqqpypLVPAve7AQx_JxNmtRgU2ecA2_CnFRZ1VKwkogmo8cL2sWQUjT90zWUqMfm1NKc2jWXA6__Hu4J_1NVBt4uQJjH_8l-AzJ4p2k</recordid><startdate>20230320</startdate><enddate>20230320</enddate><creator>Aboumrad, Maya</creator><creator>Zwain, Gabrielle</creator><creator>Smith, Jeremy</creator><creator>Neupane, Nabin</creator><creator>Powell, Ethan</creator><creator>Dempsey, Brendan</creator><creator>Reyes, Carolina</creator><creator>Satram, Sacha</creator><creator>Young-Xu, Yinong</creator><general>Oxford University Press</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0001-6140-4250</orcidid></search><sort><creationdate>20230320</creationdate><title>Development and Validation of a Clinical Risk Score to Predict Hospitalization Within 30 Days of Coronavirus Disease 2019 Diagnosis</title><author>Aboumrad, Maya ; Zwain, Gabrielle ; Smith, Jeremy ; Neupane, Nabin ; Powell, Ethan ; Dempsey, Brendan ; Reyes, Carolina ; Satram, Sacha ; Young-Xu, Yinong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c424t-fa0659bda0abb6391dce13f6356a773f5484a9a896bae12954ad90424aad6d7a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Adolescent</topic><topic>COVID-19 - diagnosis</topic><topic>COVID-19 - epidemiology</topic><topic>COVID-19 Testing</topic><topic>Hospitalization</topic><topic>Humans</topic><topic>Male</topic><topic>Retrospective Studies</topic><topic>Risk Factors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Aboumrad, Maya</creatorcontrib><creatorcontrib>Zwain, Gabrielle</creatorcontrib><creatorcontrib>Smith, Jeremy</creatorcontrib><creatorcontrib>Neupane, Nabin</creatorcontrib><creatorcontrib>Powell, Ethan</creatorcontrib><creatorcontrib>Dempsey, Brendan</creatorcontrib><creatorcontrib>Reyes, Carolina</creatorcontrib><creatorcontrib>Satram, Sacha</creatorcontrib><creatorcontrib>Young-Xu, Yinong</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Military medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Aboumrad, Maya</au><au>Zwain, Gabrielle</au><au>Smith, Jeremy</au><au>Neupane, Nabin</au><au>Powell, Ethan</au><au>Dempsey, Brendan</au><au>Reyes, Carolina</au><au>Satram, Sacha</au><au>Young-Xu, Yinong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Development and Validation of a Clinical Risk Score to Predict Hospitalization Within 30 Days of Coronavirus Disease 2019 Diagnosis</atitle><jtitle>Military medicine</jtitle><addtitle>Mil Med</addtitle><date>2023-03-20</date><risdate>2023</risdate><volume>188</volume><issue>3-4</issue><spage>e833</spage><epage>e840</epage><pages>e833-e840</pages><issn>0026-4075</issn><eissn>1930-613X</eissn><abstract>ABSTRACT Introduction Early identification of patients with coronavirus disease 2019 (COVID-19) who are at risk for hospitalization may help to mitigate disease burden by allowing healthcare systems to conduct sufficient resource and logistical planning in the event of case surges. We sought to develop and validate a clinical risk score that uses readily accessible information at testing to predict individualized 30-day hospitalization risk following COVID-19 diagnosis. Methods We assembled a retrospective cohort of U.S. Veterans Health Administration patients (age ≥ 18 years) diagnosed with COVID-19 between March 1, 2020, and December 31, 2020. We screened patient characteristics using Least Absolute Shrinkage and Selection Operator logistic regression and constructed the risk score using characteristics identified as most predictive for hospitalization. Patients diagnosed before November 1, 2020, comprised the development cohort, while those diagnosed on or after November 1, 2020, comprised the validation cohort. We assessed risk score discrimination by calculating the area under the receiver operating characteristic (AUROC) curve and calibration using the Hosmer–Lemeshow (HL) goodness-of-fit test. This study was approved by the Veteran’s Institutional Review Board of Northern New England at the White River Junction Veterans Affairs Medical Center (Reference no.:1473972-1). Results The development and validation cohorts comprised 11,473 and 12,970 patients, of whom 4,465 (38.9%) and 3,669 (28.3%) were hospitalized, respectively. The independent predictors for hospitalization included in the risk score were increasing age, male sex, non-white race, Hispanic ethnicity, homelessness, nursing home/long-term care residence, unemployed or retired status, fever, fatigue, diarrhea, nausea, cough, diabetes, chronic kidney disease, hypertension, and chronic obstructive pulmonary disease. Model discrimination and calibration was good for the development (AUROC = 0.80; HL P-value = .05) and validation (AUROC = 0.80; HL P-value = .31) cohorts. Conclusions The prediction tool developed in this study demonstrated that it could identify patients with COVID-19 who are at risk for hospitalization. This could potentially inform clinicians and policymakers of patients who may benefit most from early treatment interventions and help healthcare systems anticipate capacity surges.</abstract><cop>US</cop><pub>Oxford University Press</pub><pmid>34611704</pmid><doi>10.1093/milmed/usab415</doi><orcidid>https://orcid.org/0000-0001-6140-4250</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 0026-4075
ispartof Military medicine, 2023-03, Vol.188 (3-4), p.e833-e840
issn 0026-4075
1930-613X
language eng
recordid cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_8522374
source Oxford Journals Online
subjects Adolescent
COVID-19 - diagnosis
COVID-19 - epidemiology
COVID-19 Testing
Hospitalization
Humans
Male
Retrospective Studies
Risk Factors
title Development and Validation of a Clinical Risk Score to Predict Hospitalization Within 30 Days of Coronavirus Disease 2019 Diagnosis
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-04T23%3A16%3A35IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Development%20and%20Validation%20of%20a%20Clinical%20Risk%20Score%20to%20Predict%20Hospitalization%20Within%2030%20Days%20of%20Coronavirus%20Disease%202019%20Diagnosis&rft.jtitle=Military%20medicine&rft.au=Aboumrad,%20Maya&rft.date=2023-03-20&rft.volume=188&rft.issue=3-4&rft.spage=e833&rft.epage=e840&rft.pages=e833-e840&rft.issn=0026-4075&rft.eissn=1930-613X&rft_id=info:doi/10.1093/milmed/usab415&rft_dat=%3Cproquest_pubme%3E2579632068%3C/proquest_pubme%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c424t-fa0659bda0abb6391dce13f6356a773f5484a9a896bae12954ad90424aad6d7a3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2579632068&rft_id=info:pmid/34611704&rft_oup_id=10.1093/milmed/usab415&rfr_iscdi=true