Unbiased identification of clinical characteristics predictive of COVID-19 severity.

Autor: Akama-Garren EH; Harvard Medical School, Boston, MA, 02115, USA. elliot_akama-garren@hms.harvard.edu., Li JX; Harvard Medical School, Boston, MA, 02115, USA.; Division of General Medicine, Beth Israel Deaconess Medical Center, Boston, MA, 02115, USA.
Jazyk: angličtina
Zdroj: Clinical and experimental medicine [Clin Exp Med] 2022 Feb; Vol. 22 (1), pp. 137-149. Date of Electronic Publication: 2021 Jun 05.
DOI: 10.1007/s10238-021-00730-y
Abstrakt: There is currently limited clinical ability to identify COVID-19 patients at risk for severe outcomes. To unbiasedly identify metrics associated with severe outcomes in COVID-19 patients, we conducted a retrospective study of 835 COVID-19 positive patients at a single academic medical center between March 10, 2020 and October 13, 2020. As of December 1, 2020, 656 (79%) patients required hospitalization and 149 (18%) died. Unbiased comparisons of all clinical characteristics and mortality revealed that abnormal pH (OR 8.54, 95% CI 5.34-13.6), abnormal creatinine (OR 6.94, 95% CI 4.22-11.4), and abnormal PTT (OR 4.78, 95% CI 3.11-7.33) were most significantly associated with mortality. Correlation with ordinal severity scores confirmed these associations, in addition to associations between respiratory rate (Spearman's rho  = -0.56), absolute neutrophil count (Spearman's rho  = -0.5), and C-reactive protein (Spearman's rho  =  0.59) with disease severity. Unsupervised principal component analysis and machine learning model classification of patient demographics, laboratory results, medications, comorbidities, signs and symptoms, and vitals are capable of separating patients on the basis of COVID-19 mortality (AUC 0.82). This retrospective analysis identifies laboratory and clinical metrics most relevant to predict COVID-19 severity.
(© 2021. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)
Databáze: MEDLINE
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