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Data from suspected PCP cases among AIDS patients were gathered prospectively at a reference hospital's ER, with diagnoses later confirmed through sputum PCR analysis. We compared clinical, laboratory, and radiological data between PCP and non-PCP groups, using the Boruta algorithm to confirm significant differences. We evaluated ten PMs tailored for various ERs resource levels to diagnose PCP. Four scenarios were created, two based on X-ray findings (diffuse interstitial infiltrate) and two on CT scans (\u201cground-glass\u201d), incorporating mandatory variables: lactate dehydrogenase, O2<jats:sub>sat<\/jats:sub>, C-reactive protein, respiratory rate (&gt;\u200924\u00a0bpm), and dry cough. We also assessed HIV viral load and CD4 cell count. Among the 86 patients in the study, each model considered either 6 or 8 parameters, depending on the scenario. Many models performed well, with accuracy, precision, recall, and AUC scores\u2009&gt;\u20090.8. Notably, nearest neighbor and na\u00efve Bayes excelled (scores\u2009&gt;\u20090.9) in specific scenarios. Surprisingly, HIV viral load and CD4 cell count did not improve model performance. In conclusion, ER-based PMs using readily available data can significantly aid PCP treatment decisions in AIDS patients.<\/jats:p>","DOI":"10.1038\/s41598-024-61174-4","type":"journal-article","created":{"date-parts":[[2024,5,16]],"date-time":"2024-05-16T19:01:53Z","timestamp":1715886113000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Predictive models-assisted diagnosis of AIDS-associated Pneumocystis jirovecii pneumonia in the emergency room, based on clinical, laboratory, and radiological data"],"prefix":"10.1038","volume":"14","author":[{"given":"Oscar Jos\u00e9","family":"Chagas","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fabio Augusto Rodrigues","family":"Gon\u00e7alves","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Priscila Paiva","family":"Nagatomo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Renata","family":"Buccheri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vera Lucia","family":"Pereira-Chioccola","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gilda Maria Barbaro","family":"Del Negro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gil","family":"Benard","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,5,16]]},"reference":[{"key":"61174_CR1","unstructured":"HIV data and statistics. https:\/\/www.who.int\/teams\/global-hiv-hepatitis-and-stis-programmes\/hiv\/strategic-information\/hiv-data-and-statistics."},{"key":"61174_CR2","unstructured":"Global HIV & AIDS statistics\u2014Fact sheet. https:\/\/www.unaids.org\/en\/resources\/fact-sheet."},{"key":"61174_CR3","unstructured":"HIV Country Profiles. https:\/\/cfs.hivci.org\/index.html."},{"key":"61174_CR4","doi-asserted-by":"publisher","first-page":"564","DOI":"10.1093\/aje\/kwz252","volume":"189","author":"PF Belaunzar\u00e1n-Zamudio","year":"2020","unstructured":"Belaunzar\u00e1n-Zamudio, P. 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