Machine learning-based precision subtyping and risk prediction in sepsis: a retrospective analysis using MIMIC-IV database

Precise subtyping is crucial for enabling personalized treatments in sepsis patients. This study aims to develop an analytical framework for sepsis intervention, integrating variable selection and phenotype discovery for diverse settings. Using MIMIC-IV database, study included sepsis patients (ICD-9/10 codes), excluding those with 0.1) revealed distinct subtypes (K = 4, 8,14, respectively). Increasing cluster granularity (K from 4 to 14) revealed converging characteristics across subgroups but refined risk stratification. Current ICU admission was identified as a key protective factor, while past ICU history, high age, poor cardiopulmonary function, hypermagnesemia, and PTT/RDW abnormalities were the three core high-risk drivers. The low-risk group (younger age+current ICU admission + no ICU history+better cardiopulmonary function) had a lowest mortality rate of 9.7% in 4-subtype system and 3.4% in the 14-subtype system. Patients without current ICU protection or with the other risk factors showed higher mortality risk than the low-risk group. Notably, extremely high level of RDW (21.6%, subgroup 14, mortality rate 45.42%) and PTT values (130.35s, subgroup 10, mortality rate 33.78%) were independent of age and cardiopulmonary issues, suggesting they define two distinct high-risk subtypes. This study developed a clinically universal and interpretable sepsis subtyping framework using routine clinical variables, identifying subgroups with significant mortality differences, providing a pragmatic tool for risk stratification and prognosis assessment.