{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T04:18:06Z","timestamp":1774671486393,"version":"3.50.1"},"reference-count":85,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,3,22]],"date-time":"2024-03-22T00:00:00Z","timestamp":1711065600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Background: Despite advancements in vaccination, early treatments, and understanding of SARS-CoV-2, its impact remains significant worldwide. Many patients require intensive care due to severe COVID-19. Remdesivir, a key treatment option among viral RNA polymerase inhibitors, lacks comprehensive studies on factors associated with its effectiveness. Methods: We conducted a retrospective study in 2022, analyzing data from 252 hospitalized COVID-19 patients treated with remdesivir. Six machine learning algorithms were compared to predict factors influencing remdesivir\u2019s clinical benefits regarding mortality and hospital stay. Results: The extreme gradient boost (XGB) method showed the highest accuracy for both mortality (95.45%) and hospital stay (94.24%). Factors associated with worse outcomes in terms of mortality included limitations in life support, ventilatory support needs, lymphopenia, low albumin and hemoglobin levels, flu and\/or coinfection, and cough. For hospital stay, factors included vaccine doses, lung density, pulmonary radiological status, comorbidities, oxygen therapy, troponin, lactate dehydrogenase levels, and asthenia. Conclusions: These findings underscore XGB\u2019s effectiveness in accurately categorizing COVID-19 patients undergoing remdesivir treatment.<\/jats:p>","DOI":"10.3390\/jcm13071837","type":"journal-article","created":{"date-parts":[[2024,3,22]],"date-time":"2024-03-22T10:03:59Z","timestamp":1711101839000},"page":"1837","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Personalized Assessment of Mortality Risk and Hospital Stay Duration in Hospitalized Patients with COVID-19 Treated with Remdesivir: A Machine Learning Approach"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4273-1990","authenticated-orcid":false,"given":"Antonio","family":"Ram\u00f3n","sequence":"first","affiliation":[{"name":"Department of Pharmacy, University General Hospital, 46014 Valencia, Spain"},{"name":"Medical Analysis Expert Group, Institute of Technology, University of Castilla-La Mancha, 16002 Cuenca, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andr\u00e9s","family":"Bas","sequence":"additional","affiliation":[{"name":"Department of Pharmacy, University General Hospital, 46014 Valencia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Santiago","family":"Herrero","sequence":"additional","affiliation":[{"name":"Department of Pharmacy, University General Hospital, 46014 Valencia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pilar","family":"Blasco","sequence":"additional","affiliation":[{"name":"Department of Pharmacy, University General Hospital, 46014 Valencia, Spain"},{"name":"Medical Analysis Expert Group, Institute of Technology, University of Castilla-La Mancha, 16002 Cuenca, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Miguel","family":"Su\u00e1rez","sequence":"additional","affiliation":[{"name":"Medical Analysis Expert Group, Institute of Technology, University of Castilla-La Mancha, 16002 Cuenca, Spain"},{"name":"Department of Gastroenterology, Virgen de la Luz Hospital, 16002 Cuenca, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jorge","family":"Mateo","sequence":"additional","affiliation":[{"name":"Medical Analysis Expert Group, Institute of Technology, University of Castilla-La Mancha, 16002 Cuenca, Spain"},{"name":"Medical Analysis Expert Group, Instituto de Investigaci\u00f3n Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1016\/S0140-6736(20)30251-8","article-title":"Genomic characterisation and epidemiology of 2019 novel coronavirus: Implications for virus origins and receptor binding","volume":"395","author":"Lu","year":"2020","journal-title":"Lancet"},{"key":"ref_2","unstructured":"World Health Organization (2024, February 12). 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