Evaluating clinical heterogeneity and predicting mortality in severely burned patients through unsupervised clustering and latent class analysis.

ABSTRACT: Burn injuries often result in a high level of clinical heterogeneity and poor prognosis in patients with severe burns. Clustering algorithms, which are unsupervised methods that can identify groups with similar trajectories in patients with heterogeneous diseases, can provide insights into the mechanisms of the disease pathogenesis. This study aimed to analyze routinely collected biomarkers to understand their mortality prediction power, identify the clinical meanings or subtypes, and inform treatment decisions to improve the outcomes of patients with burns. This retrospective cohort study included patients aged ≥ 18 years who were admitted between January 2010 and December 2021. The patients were divided into four subgroups based on the time period of their admission: week 1, 2, 3, and 4.

SUBMITTER: Kim S 

PROVIDER: S-EPMC10442401 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature