ArticleJournal of clinical hypertension (Greenwich, Conn.)2026
Assessment, External Verification and Modification of Models in Diagnosing Primary Aldosteronism Subtypes.
Article in Journal of clinical hypertension (Greenwich, Conn.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Assessment, External Verification and Modification of Models in Diagnosing Primary Aldosteronism Subtypes.Journal of clinical hypertension (Greenwich, Conn.) · 2026Article
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Authors and funding
14 authors.
Funding
Abstract
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.
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