COVID-19 mortality prediction in Hungarian ICU settings implementing random forest algorithm.

Autor: Hamar Á; Department of Laboratory Medicine, Medical School, University of Pécs, Pécs, Hungary.; Molecular Medicine Research Group, Szentágothai Research Centre, University of Pécs, Pécs, Hungary., Mohammed D; Molecular Medicine Research Group, Szentágothai Research Centre, University of Pécs, Pécs, Hungary., Váradi A; Molecular Medicine Research Group, Szentágothai Research Centre, University of Pécs, Pécs, Hungary.; Institute of Metagenomics, University of Debrecen, Debrecen, Hungary., Herczeg R; Molecular Medicine Research Group, Szentágothai Research Centre, University of Pécs, Pécs, Hungary., Balázsfalvi N; Department of Anaesthesiology and Intensive Care, University of Debrecen, Debrecen, Hungary., Fülesdi B; Department of Anaesthesiology and Intensive Care, University of Debrecen, Debrecen, Hungary., László I; Department of Anaesthesiology and Intensive Care, University of Debrecen, Debrecen, Hungary., Gömöri L; Doctoral School of Neuroscience, University of Debrecen, Debrecen, Hungary., Gergely PA; Institute of Forensic Medicine, University of Debrecen, Debrecen, Hungary., Kovacs GL; Department of Laboratory Medicine, Medical School, University of Pécs, Pécs, Hungary.; Molecular Medicine Research Group, Szentágothai Research Centre, University of Pécs, Pécs, Hungary., Jáksó K; Department of Anaesthesiology and Intensive Care, Clinical Centre, University of Pécs, Pécs, Hungary., Gombos K; Department of Laboratory Medicine, Medical School, University of Pécs, Pécs, Hungary. Gombos.katalin@pte.hu.; Molecular Medicine Research Group, Szentágothai Research Centre, University of Pécs, Pécs, Hungary. Gombos.katalin@pte.hu.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 May 24; Vol. 14 (1), pp. 11941. Date of Electronic Publication: 2024 May 24.
DOI: 10.1038/s41598-024-62791-9
Abstrakt: The emergence of newer SARS-CoV-2 variants of concern (VOCs) profoundly changed the ICU demography; this shift in the virus's genotype and its correlation to lethality in the ICUs is still not fully investigated. We aimed to survey ICU patients' clinical and laboratory parameters in correlation with SARS-CoV-2 variant genotypes to lethality. 503 COVID-19 ICU patients were included in our study beginning in January 2021 through November 2022 in Hungary. Furthermore, we implemented random forest (RF) as a potential predictor regarding SARS-CoV-2 lethality among 649 ICU patients in two ICU centers. Survival analysis and comparison of hypertension (HT), diabetes mellitus (DM), and vaccination effects were conducted. Logistic regression identified DM as a significant mortality risk factor (OR: 1.55, 95% CI 1.06-2.29, p = 0.025), while HT showed marginal significance. Additionally, vaccination demonstrated protection against mortality (p = 0.028). RF detected lethality with 81.42% accuracy (95% CI 73.01-88.11%, [AUC]: 91.6%), key predictors being PaO 2 /FiO 2 ratio, lymphocyte count, and chest Computed Tomography Severity Score (CTSS). Although a smaller number of patients require ICU treatment among Omicron cases, the likelihood of survival has not proportionately increased for those who are admitted to the ICU. In conclusion, our RF model supports more effective clinical decision-making among ICU COVID-19 patients.
(© 2024. The Author(s).)
Databáze: MEDLINE
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