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Application of machine learning to identify risk factors for bloodstream infections caused by different antimicrobial resistance phenotypes in chemotherapy patients

Bloodstream infections (BSIs) remain a major cause of morbidity and mortality in patients undergoing chemotherapy. The rising incidence of multidrug-resistant organisms (MDROs) further challenges the adequacy of empirical antimicrobial therapy. Therefore, this study aimed to identify features associated with different microbiological profiles of BSIs in chemotherapy patients using machine learning models. We retrospectively analyzed 1435 confirmed BSI episodes among 1220 patients treated at a tertiary cancer cen…

By the Truvace desk1 min read1 sourcePeer-reviewedTRV-2026-2123· cite & verify
Application of machine learning to identify risk factors for bloodstream infections caused by different antimicrobial resistance phenotypes in chemotherapy patients
"Work Bench - Microbiology Laboratory, Spotswood Sewerage Pumping Station, Victoria, circa 1939" is licensed under CC BY 4.0. To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/.

In brief

Bloodstream infections (BSIs) remain a major cause of morbidity and mortality in patients undergoing chemotherapy. The rising incidence of multidrug-resistant organisms (MDROs) further challenges the adequacy of empirical antimicrobial therapy.

Therefore, this study aimed to identify features associated with different microbiological profiles of BSIs in chemotherapy patients using machine learning models. We retrospectively analyzed 1435 confirmed BSI episodes among 1220 patients treated at a tertiary cancer center from 2017 to 2022.

Main points

  1. Bloodstream infections (BSIs) remain a major cause of morbidity and mortality in patients undergoing chemotherapy.
  2. The rising incidence of multidrug-resistant organisms (MDROs) further challenges the adequacy of empirical antimicrobial therapy.
  3. Therefore, this study aimed to identify features associated with different microbiological profiles of BSIs in chemotherapy patients using machine learning models.

The problem

Application of machine learning to identify risk factors for bloodstream infections caused by different antimicrobial resistance phenotypes in chemotherapy patients: Model performance varied across pathogen groups, with the highest discrimination observed for CR-GNB (AUC-ROC 83.25%; 95% CI 73.91%-91.64%).

Sources

  1. Peer-reviewedSupportive Care in Cancer2026-10-20

The debate