{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T18:32:48Z","timestamp":1758652368604,"version":"3.44.0"},"reference-count":14,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T00:00:00Z","timestamp":1758585600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:sec id=\"sec001\">\n<jats:title>Background<\/jats:title>\n<jats:p>Elderly patients with COVID-19 face a heightened risk of severe outcomes and mortality, emphasizing the need for reliable early risk stratification in emergency departments (EDs). Clinical parameters such as respiratory rate, oxygen saturation, and peripheral perfusion index (PPI) have shown promise as simple and effective markers associated with mortality.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec002\">\n<jats:title>Methods<\/jats:title>\n<jats:p>This retrospective, single-center study included 144 COVID-19-positive patients aged 65 years and older admitted to a university hospital in Mugla, Turkey, from February 15, 2021, to April 15, 2023. Vital signs, shock indices, and PPI were collected from medical records. Receiver Operating Characteristic (ROC) curve analysis and binary logistic regression analysis were used to evaluate the associations of these parameters with in-hospital mortality.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec003\">\n<jats:title>Results<\/jats:title>\n<jats:p>Among 144 patients, 37 (26%) died during hospitalization. PPI \u2264 2.20 was the most sensitive parameter (area under the curve [AUC] = 0.684, sensitivity = 75.7%, negative predictive value [NPV] = 87.7%), allowing the early identification of patients at lower mortality risk. Oxygen saturation \u2264 86% demonstrated the highest specificity (79.4%) and positive predictive value [PPV] (46.3%), effectively identifying patients at high risk. Respiratory rate \u2265 25 breaths\/min (AUC = 0.652) also showed significant association with mortality. Binary logistic regression analysis revealed that low PPI levels were independently associated with increased in-hospital mortality (Odds ratio [OR] = 4.067; 95% confidence interval [CI]: 1.668\u20139.913; p = 0.002).<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec004\">\n<jats:title>Conclusions<\/jats:title>\n<jats:p>This study highlights the powerful association of readily available clinical parameters with mortality among elderly COVID-19 patients. PPI, in combination with respiratory rate and oxygen saturation, offers a cost-effective and efficient approach to enhance ED triage protocols. Incorporating these parameters into routine assessment could improve early identification of high-risk patients, optimize resource allocation, and save lives. Further prospective studies are warranted to validate these findings and advance risk stratification frameworks for acute care settings. Our findings should be considered as associations with mortality rather than strong predictive evidence.<\/jats:p>\n<\/jats:sec>","DOI":"10.1371\/journal.pone.0332800","type":"journal-article","created":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T17:28:07Z","timestamp":1758648487000},"page":"e0332800","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Clinical parameters associated with mortality in elderly patients with COVID-19"],"prefix":"10.1371","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9622-5161","authenticated-orcid":true,"given":"Mehmet G\u00f6khan","family":"Kaya","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"K\u0131van\u00e7","family":"Karaman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2025,9,23]]},"reference":[{"issue":"6","key":"pone.0332800.ref001","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1016\/S1473-3099(20)30243-7","article-title":"Estimates of the severity of coronavirus disease 2019: a model-based analysis","volume":"20","author":"R Verity","year":"2020","journal-title":"Lancet Infect Dis"},{"issue":"5","key":"pone.0332800.ref002","doi-asserted-by":"crossref","first-page":"846","DOI":"10.1007\/s00134-020-05991-x","article-title":"Clinical predictors of mortality due to COVID-19 based on an analysis of data of 150 patients from Wuhan, China","volume":"46","author":"Q Ruan","year":"2020","journal-title":"Intensive Care 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