Abstract MP10: Using Target Trials and Machine Learning to Estimate Heterogeneous Treatment Effects of First-Line Antihypertensive Medications

Introduction: Guidelines for hypertension (HTN) treatment make universal recommendations, ignoring prior research demonstrating differences in BP reduction between HTN medication classes in individuals with different clinical presentations. Here, we leveraged causal inference methods to emulate randomized trials to assess heterogeneity in the effectiveness of first-line HTN treatments. Methods: A hypothetical target trial was designed to assess comparative effectiveness of first-line HTN medications in subgroups defined by clinical presentation. For its emulation, patients with new HTN diagnosis (2014-2022) at Northwestern Medicine (NM) who initiated medication treatment (ACEi, ARB, CCB, Diuretics, CCB or Diuretic combination treatment [with ACEi/ARB]) were selected from the NM electronic health records. Eligibility criteria were: no prior prescription of HTN medication, 1+ outpatient visit in the year before diagnosis, and no pregnancy within a year of HTN diagnosis. The primary outcome was achieving a blood pressure of under 140/90 mm Hg after six months. The analysis involved three steps: using g-computation to estimate individual treatment effects (ITE) of antihypertensive medications, employing Causal Forest to identify key clinical variables affecting ITE, and using targeted maximum likelihood estimation with SuperLearner to estimate treatment effects within patient subgroups. Results: Our study included 22,433 eligible patients from NM. We created 16 patient subgroups by using commonly used clinical thresholds of systolic BP, low-density lipid cholesterol, age, and BMI to partition the overall patient population. Statistically significant differences in treatment effects were found in several subgroups – e.g. in subgroup 1, diuretics were found to perform significantly better than all other HTN medications ( Figure ). Conclusions: Personalized HTN treatment strategies have the potential to improve BP control rates in many patient populations.