Development and validation of subtype prediction scores for the workup of primary aldosteronism.

Abstract

A subtype prediction score for primary aldosteronism has not yet been developed and validated using a large dataset. This study aimed to develop and validate a new subtype prediction score and to compare it with existing scores using a large multicenter database. In total, 1936 patients with primary aldosteronism were randomly assigned to the development and validation datasets, constituting 1290 and 646 patients, respectively. Three prediction scores were generated with or without confirmatory tests, using logistic regression analysis. In the validation dataset, new and existing prediction scores were compared using receiver operating characteristic curve, net reclassification improvement, and integrated discrimination improvement analyses. The new prediction score is simply calculated using serum potassium levels [>3.9 mmol/l (four points); 3.5-3.9 mmol/l (three points)], the absence of adrenal nodules during computed tomography (three points), a baseline plasma aldosterone concentration of <210.0 pg/ml (two points), a baseline aldosterone/renin ratio of less than 620 (two points), and female sex (one point). Using the validation dataset, we found that a new subtype prediction score of at least 8 had a positive predictive value of 93.5% for bilateral hyperaldosteronism. The new prediction score for bilateral hyperaldosteronism was better than the existing prediction scores in the receiver operating characteristic curve and net reclassification improvement analyses. The new prediction score has clear advantages over the existing prediction scores in terms of diagnostic accuracy, feasibility, and the potential for generalization in a large population. These data will help healthcare professionals to better select patients who require adrenal venous sampling.