The impact of a primary aldosteronism predictive model in secondary hypertension decision support

Abstract

Objectives: 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: One hundred fifty-three 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, 63/2896 (2.2%) patients where the alert was displayed in model clinics had the order set launched, while 12/1210 (1.0%) in no-model clinics had the order set launched (P ͇ .014). Nineteen of 2896 (0.66%) of these highest risk patients in model clinics had an aldosterone-to-renin ratio (ARR) ordered compared to 0/1210 (0.0%) patients in no-model clinics (P ͇ .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 < .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.