Streamlined machine learning model for early sepsis risk prediction in burn patients.

ABSTRACT: Sepsis is the leading cause of mortality in burn patients, yet early identification remains difficult due to persistent hyperinflammatory responses and altered baseline physiology. We developed a streamlined machine learning model for early sepsis risk prediction in burn patients using data from 6629 patients across 11 centers participating in the German Burn Registry. The model was trained using only six admission-level variables (age, burned body surface area, deep partial-thickness burns, full-thickness burns, inhalation injury, and hypertension), selected through multiple feature selection methods and evaluated using cross-validated machine learning pipelines. The final Random Forest model achieved an AUROC of 0.91, sensitivity of 0.81, specificity of 0.85, and a negative predictive va

SUBMITTER: Drysch M 

PROVIDER: S-EPMC12540978 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature