{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T03:01:13Z","timestamp":1787022073336,"version":"build-2736575974"},"reference-count":20,"publisher":"Public Library of Science (PLoS)","issue":"11","license":[{"start":{"date-parts":[[2020,11,12]],"date-time":"2020-11-12T00:00:00Z","timestamp":1605139200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000057","name":"National Institute of General Medical Sciences","doi-asserted-by":"publisher","award":["GM111913"],"award-info":[{"award-number":["GM111913"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"\n Background<\/jats:title>\n Empirical data on conditions that increase risk of coronavirus disease 2019 (COVID-19) progression are needed to identify high risk individuals. We performed a comprehensive quantitative assessment of pre-existing clinical phenotypes associated with COVID-19-related hospitalization.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n Phenome-wide association study (PheWAS) of SARS-CoV-2-positive patients from an integrated health system (Geisinger) with system-level outpatient\/inpatient COVID-19 testing capacity and retrospective electronic health record (EHR) data to assess pre-COVID-19 pandemic clinical phenotypes associated with hospital admission (hospitalization).<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n Of 12,971 individuals tested for SARS-CoV-2 with sufficient pre-COVID-19 pandemic EHR data at Geisinger, 1604 were SARS-CoV-2 positive and 354 required hospitalization. We identified 21 clinical phenotypes in 5 disease categories meeting phenome-wide significance (P<1.60x10\n -4<\/jats:sup>\n ), including: six kidney phenotypes, e.g. end stage renal disease or stage 5 CKD (OR = 11.07, p = 1.96x10\n -8<\/jats:sup>\n ), six cardiovascular phenotypes, e.g. congestive heart failure (OR = 3.8, p = 3.24x10\n -5<\/jats:sup>\n ), five respiratory phenotypes, e.g. chronic airway obstruction (OR = 2.54, p = 3.71x10\n -5<\/jats:sup>\n ), and three metabolic phenotypes, e.g. type 2 diabetes (OR = 1.80, p = 7.51x10\n -5<\/jats:sup>\n ). Additional analyses defining CKD based on estimated glomerular filtration rate, confirmed high risk of hospitalization associated with pre-existing stage 4 CKD (OR 2.90, 95% CI: 1.47, 5.74), stage 5 CKD\/dialysis (OR 8.83, 95% CI: 2.76, 28.27), and kidney transplant (OR 14.98, 95% CI: 2.77, 80.8) but not stage 3 CKD (OR 1.03, 95% CI: 0.71, 1.48).\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n This study provides quantitative estimates of the contribution of pre-existing clinical phenotypes to COVID-19 hospitalization and highlights kidney disorders as the strongest factors associated with hospitalization in an integrated US healthcare system.<\/jats:p>\n <\/jats:sec>","DOI":"10.1371\/journal.pone.0242182","type":"journal-article","created":{"date-parts":[[2020,11,12]],"date-time":"2020-11-12T13:31:26Z","timestamp":1605187886000},"page":"e0242182","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":40,"title":["Electronic health record analysis identifies kidney disease as the leading risk factor for hospitalization in confirmed COVID-19 patients"],"prefix":"10.1371","volume":"15","author":[{"given":"Matthew T.","family":"Oetjens","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonathan Z.","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander","family":"Chang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joseph B.","family":"Leader","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dustin N.","family":"Hartzel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bryn S.","family":"Moore","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5148-9772","authenticated-orcid":true,"given":"Natasha T.","family":"Strande","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"H. Lester","family":"Kirchner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David H.","family":"Ledbetter","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anne E.","family":"Justice","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David J.","family":"Carey","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0754-3820","authenticated-orcid":true,"given":"Tooraj","family":"Mirshahi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2020,11,12]]},"reference":[{"key":"pone.0242182.ref001","article-title":"Mild or Moderate Covid-19","author":"RT Gandhi","year":"2020","journal-title":"N Engl J Med"},{"key":"pone.0242182.ref002","article-title":"Characteristics and Outcomes of 21 Critically Ill Patients With COVID-19 in Washington State","author":"M Arentz","year":"2020","journal-title":"JAMA"},{"key":"pone.0242182.ref003","article-title":"Risk Factors for Intensive Care Unit Admission and In-hospital Mortality among Hospitalized Adults Identified through the U.S. Coronavirus Disease 2019 (COVID-19)-Associated Hospitalization Surveillance Network (COVID-NET)","author":"L Kim","year":"2020","journal-title":"Clin Infect Dis"},{"key":"pone.0242182.ref004","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1038\/s41586-020-2521-4","article-title":"Factors associated with COVID-19-related death using OpenSAFELY","volume":"584","author":"EJ Williamson","year":"2020","journal-title":"Nature"},{"key":"pone.0242182.ref005","article-title":"Development and Validation of a Clinical Risk Score to Predict the Occurrence of Critical Illness in Hospitalized Patients With COVID-19","author":"W Liang","year":"2020","journal-title":"JAMA Intern Med"},{"key":"pone.0242182.ref006","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1038\/nrg.2015.36","article-title":"Unravelling the human genome-phenome relationship using phenome-wide association studies","volume":"17","author":"WS Bush","year":"2016","journal-title":"Nat Rev Genet"},{"key":"pone.0242182.ref007","doi-asserted-by":"crossref","first-page":"906","DOI":"10.1038\/gim.2015.187","article-title":"The Geisinger MyCode community health initiative: an electronic health record-linked biobank for precision medicine research","volume":"18","author":"DJ Carey","year":"2016","journal-title":"Genet Med"},{"key":"pone.0242182.ref008","doi-asserted-by":"crossref","first-page":"2375","DOI":"10.1093\/bioinformatics\/btu197","article-title":"R PheWAS: data analysis and plotting tools for phenome-wide association studies in the R environment","volume":"30","author":"RJ Carroll","year":"2014","journal-title":"Bioinformatics"},{"key":"pone.0242182.ref009","doi-asserted-by":"crossref","first-page":"A7","DOI":"10.1053\/j.ajkd.2019.01.001","article-title":"US Renal Data System 2018 Annual Data Report: Epidemiology of Kidney Disease in the United States","volume":"73","author":"R Saran","year":"2019","journal-title":"Am J Kidney Dis"},{"key":"pone.0242182.ref010","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.bbi.2020.05.059","article-title":"Lifestyle risk factors, inflammatory mechanisms, and COVID-19 hospitalization: A community-based cohort study of 387,109 adults in UK","volume":"87","author":"M Hamer","year":"2020","journal-title":"Brain Behav Immun"},{"key":"pone.0242182.ref011","doi-asserted-by":"crossref","first-page":"21011","DOI":"10.1073\/pnas.2011086117","article-title":"Overweight, obesity, and risk of hospitalization for COVID-19: A community-based cohort study of adults in the United Kingdom","volume":"117","author":"M Hamer","year":"2020","journal-title":"Proc Natl Acad Sci U S A"},{"key":"pone.0242182.ref012","article-title":"OpenSAFELY: factors associated with COVID-19 death in 17 million patients","author":"EJ Williamson","year":"2020","journal-title":"Nature"},{"key":"pone.0242182.ref013","doi-asserted-by":"crossref","first-page":"2475","DOI":"10.1056\/NEJMc2011117","article-title":"Covid-19 and Kidney Transplantation","volume":"382","author":"E Akalin","year":"2020","journal-title":"N Engl J Med"},{"key":"pone.0242182.ref014","article-title":"Outcomes of patients with end-stage kidney disease hospitalized with COVID-19","author":"JH Ng","year":"2020","journal-title":"Kidney Int"},{"key":"pone.0242182.ref015","article-title":"An initial report from the French SOT COVID Registry suggests high mortality due to Covid-19 in recipients of kidney transplants","author":"S Caillard","year":"2020","journal-title":"Kidney Int"},{"key":"pone.0242182.ref016","doi-asserted-by":"crossref","first-page":"m1966","DOI":"10.1136\/bmj.m1966","article-title":"Factors associated with hospital admission and critical illness among 5279 people with coronavirus disease 2019 in New York City: prospective cohort study","volume":"369","author":"CM Petrilli","year":"2020","journal-title":"BMJ"},{"key":"pone.0242182.ref017","doi-asserted-by":"crossref","first-page":"e46","DOI":"10.1016\/S2213-2600(20)30216-2","article-title":"COVID-19 cytokine storm: the interplay between inflammation and coagulation","volume":"8","author":"RJ Jose","year":"2020","journal-title":"Lancet Respir Med"},{"key":"pone.0242182.ref018","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1186\/s40249-020-00662-x","article-title":"Expression of the SARS-CoV-2 cell receptor gene ACE2 in a wide variety of human tissues","volume":"9","author":"MY Li","year":"2020","journal-title":"Infect Dis Poverty"},{"key":"pone.0242182.ref019","article-title":"Collider bias undermines our understanding of COVID-19 disease risk and severity","author":"G Griffith","year":"2020","journal-title":"medRxiv"},{"key":"pone.0242182.ref020","doi-asserted-by":"crossref","first-page":"2534","DOI":"10.1056\/NEJMsa2011686","article-title":"Hospitalization and Mortality among Black Patients and White Patients with Covid-19","volume":"382","author":"EG Price-Haywood","year":"2020","journal-title":"N Engl J Med"}],"container-title":["PLOS ONE"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0242182","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,11,12]],"date-time":"2020-11-12T13:31:35Z","timestamp":1605187895000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0242182"}},"subtitle":[],"editor":[{"given":"Harald","family":"Mischak","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2020,11,12]]},"references-count":20,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2020,11,12]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0242182","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,12]]}}}