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Updated: May 21, 2025
A Novel Method: Super-selective Adrenal Venous Sampling
Published on: September 15, 2017
Tools to Predict Unilateral Primary Aldosteronism and Optimise Patient Selection for Adrenal Vein Sampling: A
Elisabeth Ng1,2,3, Stella May Gwini1,4, Winston Zheng2
1Centre for Endocrinology & Metabolism, Hudson Institute of Medical Research, Clayton, Australia.
Objective:
Primary aldosteronism (PA), the most common endocrine cause of hypertension, is evaluated using adrenal vein sampling (AVS), to determine if aldosterone excess is bilateral or unilateral. AVS is invasive and technically challenging; it would ideally be used only in those with unilateral PA who are candidates for surgical cure. Those with bilateral PA would benefit from a direct path to medical management before AVS. Strategic patient selection for AVS would enable judicious and cost-efficient use of this procedure. This review evaluates the diagnostic accuracy of published algorithms that aim to predict unilateral PA and therefore facilitate informed selection for AVS.
Design:
This systematic review was performed by searching Medline and EMBASE databases to identify published models that sought to subtype PA (PROSPERO registration CRD42021277841). Algorithms reported to predict unilateral PA and therefore select patients for AVS, using AVS and/or surgical outcomes as the gold standard, were systematically evaluated.
Results:
There were 28 studies evaluating 63 unique predictive algorithms, of which 14 were tested in multiple cohorts. These were grouped into 5 categories; those combining biochemical, radiological and demographic characteristics, those involving confirmatory testing those using biochemical results only, those involving dynamic testing, and anatomical imaging. The algorithm with the highest sensitivity for unilateral PA which has been validated in at least two cohorts, involved serum potassium, CT imaging, PAC, ARR and female sex (sensitivity 78-96%). In a hypothetical scenario of 1000 people with PA where 30% have unilateral PA, this top performing algorithm would appropriately select 234-289 people for AVS and allow 143-324 to correctly bypass AVS.
Conclusions:
Accurate algorithms to inform selection for AVS will ensure that AVS is only performed in patients with a high probability of unilateral PA without clear evidence of the side of lateralisation. This will lower the demand for this invasive procedure, avoid unnecessary procedural complications, and reduce associated health care costs. Further validation of the top-performing algorithms in larger and diverse cohorts will support their use in routine practice.
