Clinical subtypes identification and feature recognition of sepsis leukocyte trajectories based on machine learning.

ABSTRACT: Sepsis is a highly variable condition, and tracking leukocyte patterns may offer insights for tailored treatment and prognosis. We used the MIMIC-IV database to analyze patients diagnosed with Sepsis-3 within 24 h of ICU admission. Latent class mixed models (LCMM) were applied to leukocyte trajectories to identify sepsis subtypes. The primary outcome was 28-day all-cause mortality, with secondary outcomes including the need for life-support therapies. Associations between leukocyte trajectories and outcomes were assessed using multivariate regression, and findings were externally validated with the eICU database. Use the XGBoost model to identify baseline characteristics of high-risk mortality sepsis subgroups for predicting subgroup allocation upon patient admission to the ICU, and apply

SUBMITTER: Miao S 

PROVIDER: S-EPMC11986166 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature