MedicinemedRxiv
Heuristic editor, no API keyVerdict: NotableAI-Based Screening Approach for Primary Aldosteronism Using Longitudinal Electronic Health Records
Context Current guidelines for primary aldosteronism (PA) recommend screening all patients with hypertension.
Key numbers
- 95% CI 0.688-0.730
- 22,264 patients
- 1,279,455 adults
Caveats
- Preprint; not yet peer reviewed.
VerdictWorth a reader's time today.
Abstract
Context Current guidelines for primary aldosteronism (PA) recommend screening all patients with hypertension. Given low screening rates, an automated artificial intelligence (AI)-based risk-stratified approach may facilitate guideline implementation. Objective To develop an AI-based prescreening tool using electronic health record (EHR) data to identify at-risk patients. Design Model development retrospective cohort study. Ensemble architecture using eXtreme Gradient Boosting, random forest, and extremely randomized trees was trained on EHR data (1986-2025) and evaluated on a test set of patients with hypertension. Setting Single institution system spanning community clinics and tertiary/quaternary centers reported via Mayo Clinic Platform. Patients Adults with hypertension, International Classification of Disease (ICD) diagnosis of PA, or had plasma renin/aldosterone testing. Main Outcome Measures Predicting PA diagnosis (disease) or negative workup (control) using age, sex, ICD diagnoses, vitals, labs, and medications by area under receiver operating characteristic (AUROC), estimated calibration error (ECE), and specificity. Results Of 22,264 patients (1,833 disease and 20,431 controls), median time from initial encounter to screening was 5.0 years (IQR 0.5-12.6). The model demonstrated AUROC 0.709 (95% CI 0.688-0.730) with ECE 0.037. At a 0.5 cut-off threshold, the model had high specificity (71.6%) and low false positivity (28.4%). In a test cohort of 1,279,455 adults with hypertension, the model identified 5,295 (0.4%) for first-pass screening. Conclusions A high-specificity model can provide a pre-screening PA pathway using routine EHR data to identify and prioritize patients for PA screening. This AI-based approach can risk-stratify patients, guide sequencing of testing/referrals, and pragmatically facilitate adoption of current guidelines into practice.
The editor's rubric
| Dimension | Level | Weight | What that level means |
|---|---|---|---|
| Leverage | ███░░ 3 | 10% | A method or resource many groups across the field will adopt within a year. |
| Magnitude | ███░░ 3 | 20% | Large gain: roughly 2x, or a clear new state of the art on a hard, unsaturated problem. |
| Evidence | ████░ 4 | 32% | Strong: large scale, preregistered, independently replicated, or a well-powered randomized trial. |
| Novelty | ██░░░ 2 | 8% | A new combination of known ideas. |
| Trajectory | ██░░░ 2 | 5% | Some room to improve with obvious engineering. |
| Stakes | ██░░░ 2 | 25% | Benefits a professional community (practitioners, clinicians, engineers). |
Editor’s rationale
Heuristic triage from title and abstract text only, not a reading of the paper. Cues found: breadth (programmable); gains (relative gain); novelty (open problem); design (randomized); verification (confidence interval, held-out test). Red flags: weak evidence (single site).
How the score was computed
- Merit
- 5.9 / 10
- Adjusted merit
- 4.8 / 10
- Attention
- 0%
- Freshness
- 92%
- Citations0 (reference 20, via semantic-scholar, Oct 9, 2026, 06:17 UTC)