Personalized sepsis mortality prediction: An interpretable machine learning nomogram.

ABSTRACT:

Purpose

Sepsis remains a major cause of mortality in ICU patients, requiring accurate prognostic tools for optimal management. This study aimed to develop and validate an interpretable machine learning-based nomogram for predicting in-hospital mortality in sepsis patients to guide clinical decision-making.

Methods

This retrospective cohort study included 407 adult sepsis patients from ICU admissions between January 2019 and December 2024. Patients were randomly divided into training (n = 284) and validation (n = 123) cohorts. A LASSO-based multivariate logistic regression was applied to construct a predictive nomogram.

Results

Seven independent predictors emerged for in-hospital mortality: age, abdominal infection, vasopressor requirement, WBC, BNP, APACHE II score, a

SUBMITTER: Weng L 

PROVIDER: S-EPMC12924733 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature