2238. Estimating the impact of antibiotic exposure on antibiotic resistance in uncomplicated UTI using machine learning causal inference
Authors: Kanjilal Sanjat , Jeong Hyewon , Ma Yidan , Wei Alex , Yang Kexin , Sontag David 
Published: December 2022

Background

Incorporating an antibiotic’s propensity for engendering resistance to itself and other antibiotics is a potentially useful strategy for preventing antimicrobial resistance (AMR), but prospective studies have been difficult to generalize to outpatients and retrospective studies are prone to design errors and model misspecification. To address this gap, we apply causal inference with targeted maximum likelihood estimation (TMLE) using machine learning, to data from the electronic health record to define the antibiotic use-resistance relationship for common outpatient therapies used to treat urinary tract infection (UTI).

Methods

We estimated the risk of AMR in response to treatment in a cohort of outpatients with uncomplicated UTI in the Mass General Brigham health system between 2016 and 2021. We sought to emulate a randomized controlled trial using the targeted maximum likelihood (TMLE) approach with logistic regression, random forests, multilayer perceptrons, and XGBoost to mitigate confounding by indication and to model the outcome (Figure 1). Potential confounders include demographics, comorbidities and prior microbiology, windowed in time for temporally varying features (Figure 2). We quantified the average treatment effect (ATE) of exposure to nitrofurantoin (NIT, target trial 1) or fluoroquinolones (FQs, target trial 2) to any other antibiotic type on the risk of AMR to NIT, FQs or amoxicillin-clavulanate at 12 months post-exposure. Figure 1:Analytic frameworkFramework for targeted maximum likelihood estimation (TMLE) of causal impacts.Figure 2:Causal diagrama) Conceptual model for the emergence of AMR with observed and unobserved features and b) causal diagram for inference model.

Results

Our final cohort consisted of 4,573 patients with no baseline AMR or antibiotic exposure in the previous 12 months who were treated with NIT, FQs or oral beta-lactams. XGBoost models significantly outperformed other model types. Compared to other antibiotics, the ATE of NIT exposure to NIT resistance at 12 months was 0.05 (0.04 – 0.07) and for FQ resistance was 0.06 (0.05, 0.08). Exposure to NIT had no impact on the risk of resistance to AMC at 12 months. Exposure to FQs had no impact on resistance to FQs, NIT or AMC at 12 months (Figure 3). Figure 3:Average treatment effectsATEs for the impact of NIT or FQs on the risk of AMR to NIT, FQs or AMC at 12 months using a) logistic regression, b) random forest, c) multilayer perceptron and d) XGBoost models

Conclusion

Outpatients treated with NIT had a higher risk of AMR at 12 months than those treated with FQs. Future work will focus on including hospital exposures and immunosuppression into models and infer impact using a wider range of treatments.

Disclosures

Sanjat Kanjilal, MD, MPH, GlaxoSmithKline: Advisor/Consultant|Roche Diagnostics: Honoraria David Sontag, PhD, Adobe: Grant/Research Support|ASAPP: Advisor/Consultant|Cureai Health: Stocks/Bonds|Facebook: Grant/Research Support|Google: Grant/Research Support|IBM: Grant/Research Support|SAP: Grant/Research Support|Takeda: Grant/Research Support.