Context: Primary aldosteronism (PA) is the most common endocrine cause of hypertension. The final diagnostic step involves subtyping, using adrenal vein sampling (AVS), to determine if PA is unilateral or bilateral. The complete PA diagnostic process is time and resource intensive, which can impact rates of diagnosis and treatment. Previous studies have developed tools to predict bilateral PA before AVS.
Objective: Evaluate the sensitivity and specificity of published tools that aim to identify bilateral subtypes of PA.
Methods: Medline and Embase databases were searched to identify published models that sought to subtype PA, and algorithms to predict bilateral PA are reported. Meta-analysis and meta-regression were then performed.
Results: There were 35 studies included, evaluating 55 unique algorithms to predict bilateral PA. The algorithms were grouped into 6 categories: those combining biochemical, radiological, and demographic characteristics (A); confirmatory testing alone or combined with biochemical, radiological, and demographic characteristics (B); biochemistry results alone (C); adrenocorticotropic hormone stimulation testing (D); anatomical imaging (E); and functional imaging (F). Across the identified algorithms, sensitivity and specificity ranged from 5% to 100% and 36% to 100%, respectively. Meta-analysis of 30 unique predictive tools from 32 studies showed that the group A algorithms had the highest specificity for predicting bilateral PA, while group F had the highest sensitivity.
Conclusions: Despite the variability in published predictive algorithms, they are likely important for decision-making regarding the value of AVS. Prospective validation may enable medical treatment upfront for people with a high likelihood of bilateral PA without the need for an invasive and resource-intensive test.
Keywords: adrenal vein sampling; meta-analysis; primary aldosteronism; subtyping; systematic review.
© The Author(s) 2023. Published by Oxford University Press on behalf of the Endocrine Society. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.