2838. Clinical Application and Validation of a Predictive Antimicrobial Resistance Risk Categorization Framework for Patients with Uncomplicated Urinary Tract Infection

Abstract Background Empiric antibiotic (ABX) treatment for uncomplicated urinary tract infections (uUTIs) can be ineffective due to antimicrobial resistance (AMR). Understanding the risk of AMR using data-driven approaches can inform appropriate ABX selection. We developed an AMR pathogen risk categorization framework in E. coli caused uUTI using predictive modeling and evaluated its clinical validity. Methods Eligible females with uUTI confirmed by positive E. coli urine culture treated with nitrofurantoin (NTF), trimethoprim/sulfamethoxazole (SXT), fluoroquinolones (FQs), or beta-lactams (BLs) were identified from the Optum de-identified electronic health record data set (Oct 2015–Feb 2020). We developed predictive models using machine learning to quantify AMR probability for each ABX class. A framework with 3 risk categories (low, moderate, high) was constructed using the predicted probability (PP) of non-susceptibility (NS) (Table 1). Six patient profiles from differing risk categorizations were reviewed for clinical validity by 5 clinicians (4 medical doctors, 1 pharmacist). Results Of 87,487 eligible patients, approximately half were classified as low or high risk (44.0–49.1% across ABX classes). The proportion of patients with infections due to NS organisms was 5–12-fold higher among patients classified as high or moderate vs low risk (Figure 1). After review of the patient profiles (Table 2), clinical experts confirmed the consistency of modeled risk classification for all 6 patients with their own assessment of AMR risk across all drug classes. Patient 1 was aged 20 yrs, White, West residence, no UTI or ABX history 1 year prior to her uUTI; PP of NS was low (NTF 1.5%, SXT 16.6%, FQs 4.8%, BLs 8.4%) and was classified as low risk for all ABX classes. In contrast, patient 6 was post-menopausal, Black, Midwest residence, and had UTI episodes, prior AMR, and multiple healthcare visits 1 year prior to uUTI. She had high PP of NS (NTF 10.3%, SXT 97.2%, FQs 96.4%, BLs 48.0%) and was categorized as high risk for all ABX classes. Conclusion AMR risk varied greatly between patients. Our prediction model contextualizes patients’ AMR PP to 4 commonly prescribed ABX classes in the setting of uUTIs. Clinical application of this framework could inform appropriate empiric ABX selection for patients with uUTI. Disclosures Ryan K. Shields, PharmD, MS, Allergan: Advisor/Consultant|Cidara: Advisor/Consultant|Entasis: Advisor/Consultant|GSK: Advisor/Consultant|Melinta