External Validation of Clinical Prediction Models in Unilateral Primary Aldosteronism

Background: Targeted treatment of primary aldosteronism (PA) is informed by adrenal vein sampling (AVS), which remains limited to specialized centers. Clinical prediction models have been developed to help select patients who would most likely benefit from AVS. Our aim was to assess the performance of these models for PA subtyping.

Methods: This external validation study evaluated consecutive patients referred for PA who underwent AVS at a tertiary care referral center in Alberta, Canada during 2006-2018. In alignment with the original study designs and intended uses of the clinical prediction models, the primary outcome was the presence of lateralization on AVS. Model discrimination was evaluated using the C-statistic. Model calibration was assessed by comparing the observed vs. predicted probability of lateralization in the external validation cohort.

Results: The validation cohort included 342 PA patients who underwent AVS (mean age, 52.1 years [SD, 11.5]; 201 [58.8%] male; 186 [54.4%] with lateralization). Six published models were assessed. All models demonstrated low-to-moderate discrimination in the validation set (C-statistics; range, 0.60-0.72), representing a marked decrease compared with the derivation sets (range, 0.80-0.87). Comparison of observed and predicted probabilities of unilateral PA revealed significant miscalibration. Calibration-in-the-large for every model was >0 (range, 0.35-1.67), signifying systematic underprediction of lateralizing disease. Calibration slopes were consistently <1 (range, 0.35-0.87), indicating poor performance at the extremes of risk.

Conclusions: Overall, clinical prediction models did not accurately predict AVS lateralization in this large cohort. These models cannot be reliably used to inform the decision to pursue AVS for most patients.