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

BMC Infectious Diseases, Journal Year: 2025, Volume and Issue: 25(1)

Published: March 26, 2025

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 novel predictive model for 30-day HM BSIs. A retrospective analysis 231 positive blood cultures was conducted. Logistic regression identified risk factors mortality. Th1/Th2 cytokines were collected at BSI onset, LASSO restricted cubic spline used refine predictors. Seven machine learning(ML) algorithm (XGBoost, Regression, LightGBM, RandomForest, AdaBoost, GBDT GNB) trained using 10-fold cross-validation performance evaluated the ROC, calibration plots, decision learning curves Shapley Additive Explanations (SHAP) analysis. The developed by integrating clinical features, aiming enhance accuracy prediction. Among cohort, acute myeloid leukemia (38%) most common HM, while gram negative bacteria (64%) predominant pathogens causing BSI. Age, polymicrobial BSI, IL-4, IL-6 AST levels predictors Regression achieved AUCs 0.802, 0.792, 0.822 training, validation, test cohorts, respectively, strong benefit shown curves. SHAP highlighted IL-4 as key introduces ML-based features predict BSIs, demonstrating applicability.

Language: Английский

From pixels to prognosis: leveraging radiomics and machine learning to predict IDH1 genotype in gliomas

Neurosurgical Review, Journal Year: 2025, Volume and Issue: 48(1)

Published: April 29, 2025

Language: Английский