Why the study?
To determine whether adding a primary aldosteronism predictive model to a secondary hypertension decision support tool increases primary aldosteronism screening in primary care.
Does the addition of a primary aldosteronism predictive model to a secondary hypertension decision support tool increase screening for primary aldosteronism in primary care patients?
Does the addition of a primary aldosteronism predictive model to a secondary hypertension decision support tool increase screening for primary aldosteronism in primary care patients?
Integrating a predictive model for primary aldosteronism into a secondary hypertension clinical decision support tool significantly increases screening rates for this underdiagnosed condition in primary care.
Supports integration of primary aldosteronism risk models into decision support to increase screening; extends RCT evidence for targeted evaluation in primary care.
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). One hundred twenty-four of 20 493 (0.61%) in model clinics had an ARR ordered compared to 34/17 820 (0.19%) in the 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.
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Mack et al. (2024) studied this question.
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