Outcome-Based Decision-Making Algorithm for Treating Patients with Primary Aldosteronism

Optimal management of primary aldosteronism (PA) is crucial due to the increased risk of cardiovascular and cerebrovascular diseases. Adrenal venous sampling (AVS) is the gold standard method for determining subtype but is technically challenging and invasive. Some PA patients do not benefit clinically from surgery. We sought to develop an algorithm to improve decision- making before engaging in AVS and surgery in clinical practice.

We conducted the ongoing Korean Primary Aldosteronism Study at two tertiary centers. Study A involved PA patients with successful catheterization and a unilateral nodule on computed tomography and aimed to predict unilateral aldosterone-producing adenoma (n=367). Study B involved similar patients who underwent adrenalectomy and aimed to predict postoperative outcome (n=330). In study A, we implemented important feature selection using the least absolute shrinkage and selection operator regression.

We developed a unilateral PA prediction model using logistic regression analysis: lowest serum potassium level ≤3.4 mEq/L, aldosterone-to-renin ratio ≥150, plasma aldosterone concentration ≥30 ng/mL, and body mass index <25 kg/m2 (area under the curve, 0.819; 95% confidence interval, 0.774 to 0.865; sensitivity, 97.6%; specificity, 25.5%). In study B, we identified female, hypertension duration <5 years, anti-hypertension medication <2.5 daily defined dose, and the absence of coronary artery disease as predictors of clinical success, using stepwise logistic regression models (sensitivity, 94.2%; specificity, 49.3%). We validated our algorithm in the independent validation dataset (n=53).

We propose this new outcome-driven diagnostic algorithm, simultaneously considering unilateral aldosterone excess and clinical surgical benefits in PA patients.