Assessment, External Verification and Modification of Models in Diagnosing Primary Aldosteronism Subtypes.

Diagnostic models are used to identify primary aldosteronism (PA) subtypes, including unilateral primary aldosteronism (UPA) and bilateral primary aldosteronism (BPA), only UPA could be treated through surgery. PA diagnostic models were screened from the NCBI, Embase, and Web of Science databases and assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). External validation of these models was conducted using our Cohort. Models with high specificity were selected as the foundation for further modification. Based on the data from Cohort A and Cohort B, a new diagnostic model was developed by incorporating plasma aldosterone concentration (PAC) changes during postural stimulation testing (PST) as a variable. Eleven UPA diagnostic models were selected, with the concordance index (C index) varying from 0.745 to 0.938. Among these, three models conducted external validation, and one conducted internal validation. All models were identified as having a high risk of bias by using PROBAST. During external validations, the value of the C-index ranged from 0.600 to 0.708. In the modified diagnostic model, the PAC change was set at ≤ -23.0% during PST. This adjustment increased specificity from 94.5% to 98.7% in cohort A, while specificity from cohort B increased to 96.3%. The performance of all included models needs improvement. Adding a PAC change of ≤ -23.0% during PST as an additional variable to the published UPA diagnostic models improved their specificity and may broaden the models' applicability across different populations.