Development of a Prediction Model for Antibiotic-Resistant Urinary Tract Infections Using Integrated Electronic Health Records from Multiple Clinics in North-Central Florida

Abstract

Introduction Urinary tract infections (UTIs) are common infections for which initial antibiotic treatment decisions are empirically based, often without antibiotic susceptibility testing to evaluate resistance, increasing the risk of inappropriate therapy. We hypothesized that models based on electronic health records (EHR) could assist in the identification of patients at higher risk for antibiotic-resistant UTIs and help guide the selection of antimicrobials in hospital and clinic settings. Methods EHR from multiple centers in North-Central Florida, including patient demographics, previous diagnoses, prescriptions, and antibiotic susceptibility tests, were obtained for 9990 patients diagnosed with a UTI during 2011–2019. Decision trees, boosted logistic regression (BLR), and random forest models were developed to predict resistance to common antibiotics used for UTI management [sulfamethoxazole-trimethoprim (SXT), nitrofurantoin (NIT), ciprofloxacin (CIP)] and multidrug resistance (MDR). Results There were 6307 (63.1%) individuals with a UTI caused by a resistant microorganism. Overall, the population was majority female, white, non-Hispanic, and older aged (mean = 60.7 years). The BLR models yielded the highest discriminative ability, as measured by the out-of-bag area under the receiver-operating curve (AUROC), for the resistance outcomes [AUROC = 0.58 (SXT), 0.62 (NIT), 0.64 (CIP), and 0.66 (MDR)]. Variables in the best performing model were sex, history of UTIs, catheterization, renal disease, dementia, hemiplegia/paraplegia, and hypertension. Conclusions The discriminative ability of the prediction models was moderate. Nonetheless, these models based solely on EHR demonstrate utility for the identification of patients at higher risk for resistant infections. These models, in turn, may help guide clinical decision-making on the ordering of urine cultures and decisions regarding empiric therapy for these patients. Supplementary Information The online version contains supplementary material available at 10.1007/s40121-022-00677-x.

Tackling the Antimicrobial Resistance “Pandemic” with Machine Learning Tools: A Summary of Available Evidence

“…Machine learning models based on electronic health records can be used to aid clinicians in predicting antimicrobial-resistant urinary tract infections [214]. Attempts have been made to predict antimicrobial resistance based on the personal clinical history of patients [215].…”

Using Machine Learning to Predict Antimicrobial Resistance―A Literature Review

“…By reducing the unnecessary use of broad-spectrum antibiotics that breed resistant organisms, empiric precision antibiotic prescribing with personalized antibiograms could improve patient safety and antibiotic stewardship [ 43 ]. In the study of Rich et al, the boosted logistic regression (BLR) models yielded the highest discriminative performance as compared to the decision tree (DT) and random forest (RF) models, yet the clinical decision support system developed in this study was moderately predictive of antibiotic-resistant UTIs (AUROC 0.57–0.66) [ 44 ]. Still, when resistance testing is not possible or not rapid enough, these models can inform decision-making.…”