Machine Learning Model to Guide Empirical Antimicrobial Therapy in Febrile Neutropenic Patients With Hematologic Malignancies

Abstract

Background/aim: Optimal antimicrobial selection for patients with febrile neutropenia (FN) may differ depending on the underlying mechanisms. We aimed to develop a model for predicting the severity of bacteremia in patients with FN and hematologic malignancies (HMs) to help clinicians select appropriate antimicrobials using a machine-learning approach.

Patients and methods: In this single-center retrospective study, we analyzed the characteristics and microbial epidemiology of patients with FN and HMs who developed bacteremia. We applied a machine learning approach (least absolute shrinkage selection operator) to select the variables and then created a risk score. Using the risk score, a model was constructed that enabled us to estimate the probability of developing severe complications when a narrow- [cefepime (CEM)] or broad-spectrum [either piperacillin-tazobactam or meropenem (PT+MEM)] antimicrobial agent was administered.

Results: In total, 228 patients were enrolled. Of these, a microbiological cohort (n=126) and an analysis cohort (n=88) were established. In the microbiological cohort, coagulase-negative staphylococci (20.6%) were the most common pathogens, and antimicrobial resistance mechanisms were identified in 53 isolates (42.1%). In the analysis cohort, CEM and PT+MEM were administered to 53 (60.2%) and 35 (39.8%) patients, respectively. The overall incidence of severe complications was 26.1%. The performance of the machine learning model was measured by the area under the receiver operating characteristic curve (AUC) (AUC=0.813; 95% confidence interval=0.691-0.894), which showed good discrimination.

Conclusion: This pilot study introduces a novel method for constructing predictive models tailored to specific patient groups, potentially supporting antimicrobial stewardship.

Keywords: Febrile neutropenia; bacteremia; empirical antimicrobial therapy; hematologic malignancy; machine learning; risk prediction model.