Predicting Risk of Multidrug-Resistant Enterobacterales Infections Among People With HIV

Abstract

Background: Medically vulnerable individuals are at increased risk of acquiring multidrug-resistant Enterobacterales (MDR-E) infections. People with HIV (PWH) experience a greater burden of comorbidities and may be more susceptible to MDR-E due to HIV-specific factors.

Methods: We performed an observational study of PWH participating in an HIV clinical cohort and engaged in care at a tertiary care center in the Southeastern United States from 2000 to 2018. We evaluated demographic and clinical predictors of MDR-E by estimating prevalence ratios (PRs) and employing machine learning classification algorithms. In addition, we created a predictive model to estimate risk of MDR-E among PWH using a machine learning approach.

Results: Among 4734 study participants, MDR-E was isolated from 1.6% (95% CI, 1.2%-2.1%). In unadjusted analyses, MDR-E was strongly associated with nadir CD4 cell count ≤200 cells/mm³ (PR, 4.0; 95% CI, 2.3-7.4), history of an AIDS-defining clinical condition (PR, 3.7; 95% CI, 2.3-6.2), and hospital admission in the prior 12 months (PR, 5.0; 95% CI, 3.2-7.9). With all variables included in machine learning algorithms, the most important clinical predictors of MDR-E were hospitalization, history of renal disease, history of an AIDS-defining clinical condition, CD4 cell count nadir ≤200 cells/mm³, and current CD4 cell count 201-500 cells/mm³. Female gender was the most important demographic predictor.

Conclusions: PWH are at risk for MDR-E infection due to HIV-specific factors, in addition to established risk factors. Early HIV diagnosis, linkage to care, and antiretroviral therapy to prevent immunosuppression, comorbidities, and coinfections protect against antimicrobial-resistant bacterial infections.

Keywords: Enterobacterales; HIV; gram-negative; machine learning; multidrug resistance.

Figure 1.

Diagram of patient flow, with inclusion and exclusion criteria. Abbreviation: UCHCC, University of North Carolina Center for AIDS Research HIV Clinical Cohort.

Figure 2.

Predicted percentage of patients with multidrug-resistant Enterobacterales infections by CD4 count. A, Nadir CD4 cell count. B, Current CD4 cell count. Percentages were estimated using restricted cubic spline models. Bands represent 95% confidence intervals (bands do not extend the full range of estimates due to lack of precision at the extremes).

Figure 3.

Relative influence of predictors on machine learning model output. A, Coefficients from penalized logistic regression (elastic-net) model. The model includes all demographic and clinical predictors of interest, with variables specified as binary input features. B, Shapley additive explanations for super learner model. The model includes all demographic and clinical predictors of interest, with variables specified as either continuous or binary input features. Continuous features vary from low to high values, whereas binary features are either present or absent. Each dot represents the impact of a feature on the prediction of a multidrug-resistant Enterobacterales isolate for 1 patient. Abbreviation: SHAP, Shapley additive explanations.