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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...
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Published in: | Military medicine 2023-03, Vol.188 (3-4), p.e833-e840 |
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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 |
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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> |
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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 |
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