Key result
Across 55 algorithms predicting bilateral primary aldosteronism, sensitivity ranged from 5% to 100% and specificity from 36% to 100%, with combined clinical models showing the highest specificity.
Why the study?
The diagnostic process for subtyping primary aldosteronism via adrenal vein sampling is time and resource intensive, prompting evaluation of predictive tools for bilateral disease.
Can predictive algorithms accurately identify bilateral subtypes of primary aldosteronism without adrenal vein sampling?
Meta-Analysis
Can predictive algorithms accurately identify bilateral subtypes of primary aldosteronism without adrenal vein sampling?
Predictive algorithms show highly variable sensitivity and specificity for identifying bilateral primary aldosteronism, but specific combinations of clinical and imaging features may help select patients for upfront medical therapy without invasive adrenal vein sampling.
Wide performance variation precludes routine clinical reliance on these algorithms; leaves open the potential of combined models requiring prospective confirmation.
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.
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Ng et al. (2023) conducted a meta-analysis in Primary aldosteronism. Predictive algorithms for bilateral primary aldosteronism was evaluated on Sensitivity and specificity for predicting bilateral PA. Across 55 algorithms predicting bilateral primary aldosteronism, sensitivity ranged from 5% to 100% and specificity from 36% to 100%, with combined clinical models showing the highest specificity.