{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T18:11:18Z","timestamp":1778782278385,"version":"3.51.4"},"reference-count":30,"publisher":"Public Library of Science (PLoS)","issue":"5","license":[{"start":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T00:00:00Z","timestamp":1778716800000},"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>Clinical prediction rules integrate clinical and laboratory variables to estimate outcomes, facilitating decision-making and optimizing resources, especially in high-demand settings. We aimed to validate and compare the performance of four mortality prediction scores -ISARIC-4C, CALL, SEIMC, q-CSI- in a Peruvian cohort of unvaccinated hospitalized COVID-19 pneumonia patients during the initial pandemic wave.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec002\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We performed a retrospective cohort study based on a secondary analysis of data from a previous study (March-December 2020). To ensure a robust and standardized head-to-head comparison, we utilized a complete-case analysis (n\u2009=\u20091,074). Selection bias was rigorously assessed by comparing the analytic sample with excluded patients. Each score\u2019s performance was evaluated using sensitivity, specificity, predictive values, likelihood ratios, area under the receiver operating characteristic curve (AUROC), and robust calibration metrics, including the calibration intercept (\u03b1) and the calibration slope (\u03b2). The ISARIC-4C score was used as an international reference standard for benchmarking.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec003\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Among 3,074 hospitalized patients, 1,074 had complete data for all four scores; no clinically significant differences were found between this group and excluded participants, indicating a representative sample. The cohort was mainly male (67.9%) with a median age of 59 years. The q-CSI score showed the best discrimination (AUROC 0.85, 95% CI: 0.83\u20130.87), significantly better than ISARIC-4C (0.82, 95% CI: 0.80\u20130.85), SEIMC (0.78, 95% CI: 0.75\u20130.81), and CALL (0.69, 95% CI: 0.66\u20130.72) (p\u2009&lt;\u20090.0001). Direct comparison favored q-CSI over ISARIC-4C (p\u2009=\u20090.0016). While the Hosmer-Lemeshow (HL) test indicated a lack of fit for some models, robust calibration analysis confirmed accurate scaling (\u03b2 for all scores was approx. 1.0) and adequate centering (\u03b1 values with p\u2009&gt;\u20090.05), supporting their clinical reliability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec004\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>In this Peruvian cohort, the q-CSI score exhibited the best predictive performance and highest feasibility for in-hospital mortality among patients with COVID-19 pneumonia. While the HL test indicated a lack of fit, the analysis of the calibration \u03b1 and \u03b2 confirmed that the models are globally well-calibrated, supporting their utility for risk stratification. However, local adjustment is still necessary prior to clinical use in our setting. These findings provide a valuable baseline for resource optimization in resource-limited setting during pandemic waves.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1371\/journal.pone.0348683","type":"journal-article","created":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T17:26:01Z","timestamp":1778779561000},"page":"e0348683","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Comparison of the performance of four clinical prediction rules for mortality in patients with COVID-19"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3385-1891","authenticated-orcid":true,"given":"Johan","family":"Aza\u00f1ero-Haro","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8648-8032","authenticated-orcid":true,"given":"Alonso","family":"Soto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2026,5,14]]},"reference":[{"issue":"1","key":"pone.0348683.ref001","doi-asserted-by":"crossref","first-page":"1","DOI":"10.15226\/sojmid\/8\/1\/001108","article-title":"A comprehensive meta-analysis of COVID-19 in Latin America","volume":"8","author":"H Ashktorab","year":"2021","journal-title":"SOJ Microbiol Infect Dis"},{"key":"pone.0348683.ref002","unstructured":"Statista. 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patients with coronavirus disease 2019 in New York: retrospective case series","volume":"369","author":"MG Argenziano","year":"2020","journal-title":"BMJ"},{"issue":"3","key":"pone.0348683.ref023","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0264789","article-title":"Mortality and associated risk factors in patients hospitalized due to COVID-19 in a Peruvian reference hospital","volume":"17","author":"A Soto","year":"2022","journal-title":"PLoS One"},{"key":"pone.0348683.ref024","doi-asserted-by":"crossref","first-page":"101269","DOI":"10.1016\/j.imu.2023.101269","article-title":"Performance of CURB-65 and ISARIC 4C mortality scores for hospitalized patients with confirmed COVID-19 infection in Saudi Arabia","volume":"39","author":"MJ Alwazzeh","year":"2023","journal-title":"Inform Med Unlocked"},{"issue":"6","key":"pone.0348683.ref025","doi-asserted-by":"crossref","first-page":"488","DOI":"10.1001\/jama.1997.03540300056034","article-title":"Clinical prediction rules. A review and suggested modifications of methodological standards","volume":"277","author":"A Laupacis","year":"1997","journal-title":"JAMA"},{"issue":"12","key":"pone.0348683.ref026","article-title":"Towards better mortality prediction in cancer patients in the ICU: a comparative analysis of prognostic scales: systematic literature review","volume":"48","author":"A Cabrera Losada","year":"2024","journal-title":"Med Intensiva (Engl Ed)"},{"key":"pone.0348683.ref027","article-title":"Clinical prediction models for mortality in patients with covid-19: external validation and individual participant data meta-analysis","volume":"378","author":"VMT de Jong","year":"2022","journal-title":"BMJ"},{"key":"pone.0348683.ref028","article-title":"Diagnostic testing accuracy: sensitivity, specificity, predictive values and likelihood ratios","volume-title":"StatPearls","author":"J Shreffler","year":"2025"},{"key":"pone.0348683.ref029","doi-asserted-by":"crossref","first-page":"307","DOI":"10.3389\/fpubh.2017.00307","article-title":"Sensitivity, specificity, and predictive values: foundations, pliabilities, and pitfalls in research and practice","volume":"5","author":"R Trevethan","year":"2017","journal-title":"Front Public Health"},{"issue":"40","key":"pone.0348683.ref030","doi-asserted-by":"crossref","DOI":"10.1097\/MD.0000000000030902","article-title":"Sensitivity and specificity analyses of COVID-19 screening protocol for emergency medical services: A STARD-compliant population-based retrospective study","volume":"101","author":"H Fukushima","year":"2022","journal-title":"Medicine (Baltimore)"}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0348683","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T17:26:09Z","timestamp":1778779569000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0348683"}},"subtitle":[],"editor":[{"given":"Frederick K","family":"Wangai","sequence":"first","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2026,5,14]]},"references-count":30,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,5,14]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0348683","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,14]]}}}