Translation of Machine Learning-Based Prediction Algorithms to Personalised Empiric Antibiotic Selection: A Population-Based Cohort Study

Chungsoo Kim, Young Hwa Choi, Jung Yoon Choi, Hee Jung Choi, Rae Woong Park, Sandy Jeong Rhie

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Background: Prediction of antibiotic non-susceptibility based on patient characteristics and clinical status may support selection of empiric antibiotics for suspected hospital-acquired urinary tract infections (HA-UTIs). Methods: Prediction models were developed to predict non-susceptible results of eight antibiotic susceptibility tests ordered for suspected HA-UTI. Eligible patients were those with urine culture and susceptibility test results after 48 hours of admission between 2010–2021. Patient demographics, diagnosis, prescriptions, exposure to multidrug-resistant organisms, transfer history, and a daily calculated antibiogram were used as predictors. Lasso logistic regression (LLR), extreme gradient boosting (XGB), random forest, and stacked ensemble methods were used for development. Parsimonious models were also developed for clinical utility. Discrimination was assessed using the area under the receiver operating characteristic curve (AUROC). Results: In 10 474 suspected HA-UTI cases, the mean age was 62.1 ± 16.2 years and 48.1% were male. Non-susceptibility prediction for ampicillin/sulbactam, cefepime, ciprofloxacin, imipenem, piperacillin/tazobactam, and trimethoprim/sulfamethoxazole performed best using the stacked ensemble (AUROC 76.9, 76.1, 77.0, 80.6, 76.1, and 76.5, respectively). The model for ampicillin performed best with LLR (AUROC 73.4). Extreme gradient boosting only performed best for gentamicin (AUROC 66.9). In the parsimonious models, the LLR yielded the highest AUROC for ampicillin, ampicillin/sulbactam, cefepime, gentamicin, and trimethoprim/sulfamethoxazole (AUROC 70.6, 71.8, 73.0, 65.9, and 73.0, respectively). The model for ciprofloxacin performed best with XGB (AUROC 70.3), while the model for imipenem performed best in the stacked ensemble (AUROC 71.3). A personalised application using the parsimonious models was publicly released. Conclusions: Prediction models for antibiotic non-susceptibility were developed to support empiric antibiotic selection for HA-UTI.

Original languageEnglish
Article number106966
JournalInternational Journal of Antimicrobial Agents
Volume62
Issue number5
DOIs
StatePublished - Nov 2023

Bibliographical note

Publisher Copyright:
© 2023 Elsevier Ltd and International Society of Antimicrobial Chemotherapy

Keywords

  • Empiric antibiotics
  • Machine-learning
  • Non-susceptibility
  • Prediction models
  • Urinary tract infection

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