Prediction of ciprofloxacin resistance in hospitalized patients using machine learning

Mintz et al. develop machine learning models to predict the probability of ciprofloxacin resistance in hospitalized patients. Resistance of previous infections, prior location of patients, sex and recent resistance frequencies in the hospital impact the probability of ciprofloxacin resistance in each patient. Plain language summaryCiprofloxacin is an antibiotic commonly used to treat various infections. Due to the frequent use of ciprofloxacin, bacteria have developed high rates of resistance to it, which means they continue to grow, reducing the effectiveness of treatment. The aim of this study was to develop computer code to predict ciprofloxacin resistance in hospitalized patients. We used data from medical records and tests of whether particular bacteria could be killed by antibiotics from a large hospital in Israel to develop the computer code. The computational model accurately predicted resistance. This model could enable antibiotic treatment to be more appropriately targeted to patients that would benefit from it and reduce the amount of bacteria resistant to ciprofloxacin. BackgroundCiprofloxacin is a widely used antibiotic that has lost efficiency due to extensive resistance. We developed machine learning (ML) models that predict the probability of ciprofloxacin resistance in hospitalized patients.MethodsData were collected from electronic records of hospitalized patients with positive bacterial cultures, during 2016-2019. Susceptibility results to ciprofloxacin (n = 10,053 cultures) were obtained for Escherichia coli, Klebsiella pneumoniae, Morganella morganii, Pseudomonas aeruginosa, Proteus mirabilis and Staphylococcus aureus. An ensemble model, combining several base models, was developed to predict ciprofloxacin resistant cultures, either with (gnostic) or without (agnostic) information on the infecting bacterial species.ResultsThe ensemble models' predictions are well-calibrated, and yield ROC-AUCs (area under the receiver operating characteristic curve) of 0.737 (95%CI 0.715-0.758) and 0.837 (95%CI 0.821-0.854) on independent test-sets for the agnostic and gnostic datasets, respectively. Shapley additive explanations analysis identifies that influential variables are related to resistance of previous infections, where patients arrived from (hospital, nursing home, etc.), and recent resistance frequencies in the hospital. A decision curve analysis reveals that implementing our models can be beneficial in a wide range of cost-benefits considerations of ciprofloxacin administration.ConclusionsThis study develops ML models to predict ciprofloxacin resistance in hospitalized patients. The models achieve high predictive ability, are well calibrated, have substantial net-benefit across a wide range of conditions, and rely on predictors consistent with the literature. This is a further step on the way to inclusion of ML decision support systems into clinical practice.