Utilization of machine learning to predict antibiotic resistant event outcomes in acute myeloid leukemia patients undergoing induction chemotherapy.

ABSTRACT:

Introduction

Acute myeloid leukemia (AML) patients are highly susceptible to infection. Moreover, prophylactic and empirical antibiotic treatment during chemotherapy disrupts the gut microbiome, raising the risk for antibiotic-resistant (AR) opportunistic pathogens. There is limited data on risk factors for AR infections or colonization events in treated cancer patients, and no predictive models exist. This study aims to combine metagenomic and antibiotic administration data to develop a model predicting AR event outcomes.

Methods

Baseline stool microbiome, antibiotic administration, resistome, and clinical metadata from 95 patients were utilized to build a Random Forest model to predict AR infection and colonization events by serious AR threats. Additionally, sparse canonic

SUBMITTER: McMahon S 

PROVIDER: S-EPMC12408608 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature