The Impact of a Primary Aldosteronism Predictive Model in Secondary Hypertension Decision Support

Abstract

Objective: To determine whether the addition of a primary aldosteronism (PA) predictive model to a secondary hypertension decision support tool increases screening for PA in a primary care setting. Materials and Methods: 153 primary care clinics were randomized to receive a secondary hypertension decision support tool with or without an integrated predictive model between August 2023 and April 2024. Results: For patients with risk scores in the top 1 percentile, 63/2,896 (2.2%) patients where the alert was displayed in model clinics had the order set launched while 12/1,210 (1.0%) in no model clinics had the order set launched (P = 0.014). 19/2,896 (0.66%) of these highest risk patients in model clinics had an ARR ordered compared to 0/1,210 (0.0%) patients in no model clinics (P = 0.010). For patients with scores not in the top 1 percentile, 438/20,493 (2.1%) patients in model clinics had the order set launched compared to 273/17,820 (1.5%) in no model clinics (P < 0.001). 124/20,493 (0.61%) in model clinics had an ARR ordered compared to 34/17,820 (0.19%) in the no model clinics (P < 0.001). Discussion: The addition of a PA predictive model to secondary hypertension alert displays and triggering criteria along with order set displays and order preselection criteria results in a statistically and clinically significant increase in screening for PA, a condition that clinicians insufficiently screen for currently. Conclusion: Addition of a predictive model for an under-screened condition to traditional clinical decision support may increase screening for these conditions.