Machine learning-based prognostic model for bloodstream infections in hematological malignancies using Th1/Th2 cytokines.

ABSTRACT:

Objective

Bloodstream infection (BSI) is a significant cause of mortality in patients with hematologic malignancies(HMs), particularly amid rising antibiotic resistance. This study aimed to analyze pathogen distribution, drug-resistance patterns and develop a novel predictive model for 30-day mortality in HM patients with BSIs.

Methods

A retrospective analysis of 231 HM patients with positive blood cultures was conducted. Logistic regression identified risk factors for 30-day mortality. Th1/Th2 cytokines were collected at BSI onset, with LASSO regression and restricted cubic spline analysis used to refine predictors. Seven machine learning(ML) algorithm (XGBoost, Logistic Regression, LightGBM, RandomForest, AdaBoost, GBDT and GNB) were trained using 10-fold cross-validation

SUBMITTER: Li Q 

PROVIDER: S-EPMC11948653 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature