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TRUVACE RECORD VERSION
record: TRV-2026-2123
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-21T22:01:51.540104Z
status: published
lens: p_space
sector: health
headline: Application of machine learning to identify risk factors for bloodstream infections caused by different antimicrobial resistance phenotypes in chemotherapy patients
dek: 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…
gain_title: (none)
problem_title: 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%).
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: 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%).
problem_evidence: (none)
quick_read: 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.
limitation: 
tag: Evidence-backed problem
key_points: 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.
rundown: 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.
sources:
- peer_reviewed | Supportive Care in Cancer | https://doi.org/10.1007/s00520-026-11379-2 | 2026-10-20
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