ArticleBMC infectious diseases2025
Biomarker states and risk of death among individuals hospitalized with SARS-CoV-2 infection.
Article in BMC infectious diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
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8 authors.
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Abstract
backgroundIdentifying individuals hospitalized for SARS-CoV-2 infection at increased risk of death is crucial for clinical decision making. Analyses must consider simultaneously the multitude of biomarkers across several domains and how these biomarker profiles change over time.
methodsThis electronic health records-based study included individuals hospitalized at a Massachusetts General Brigham hospital for at least 24 h within 5 days prior and 30 days after diagnosis of COVID-19. K-means clustering was used to identify profiles among 20 eligible biomarkers and proportional hazards models were used to model 30-day mortality at hospitalization and 7 days after hospitalization (i.e., landmark models).
resultsTwelve thousand, nine hundred forty-two individuals were included, among whom 1,198 died within 30 days. Six states were identified, characterized by the following abnormalities: (1) normal/reference, (2) hematologic, (3) inflammatory and hematological, (4) metabolic, (5) kidney, hematologic, and metabolic, and (6) cardio-thrombotic, liver, and metabolic. Risk of death within 30 days was higher in States 3, 4, 5, and 6 (adjusted hazard ratios ranging from 3.6 to 7.8) compared to individuals in State 1 at hospitalization. Landmark model findings were similar.
conclusionsDistinct sub-phenotypes based on biomarker profiles were identified among patients hospitalized with SARS-CoV-2 infection, and certain phenotypes are associated with greater risk of 30-day mortality.
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