---
title: "AI Model Flags Patients at Risk for Underdiagnosed Primary Aldosteronism"
id: "endocrine-news-1-video-ai-model-identifies-patients-at-risk-of-underdiagnosed-cause-of-high"
canonical_url: "https://medichelpline.com/clinical-feed/endocrine-news-1-video-ai-model-identifies-patients-at-risk-of-underdiagnosed-cause-of-high"
content_type: "clinical_feed_article"
specialty: "Endocrinology"
source_name: "Endocrine News"
source_url: "https://endocrinenews.endocrine.org/video-ai-model-identifies-patients-at-risk-of-underdiagnosed-cause-of-high-blood-pressure/"
published_at: "2026-09-09T16:45:00.000Z"
evidence_level: "Verified Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# AI Model Flags Patients at Risk for Underdiagnosed Primary Aldosteronism
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/endocrine-news-1-video-ai-model-identifies-patients-at-risk-of-underdiagnosed-cause-of-high
- **Specialty:** [Endocrinology](https://medichelpline.com/clinical-feed/endocrinology.md)
- **Primary Source:** Endocrine News
- **Source URL:** [Original Journal Publication](https://endocrinenews.endocrine.org/video-ai-model-identifies-patients-at-risk-of-underdiagnosed-cause-of-high-blood-pressure/)
- **Published At:** 2026-09-09T16:45:00.000Z
- **Evidence Rating:** Verified Feed
## Executive GIST (TL;DR)
- A new **AI model** presented at ENDO 2026 analyzed three decades of de-identified electronic health record (EHR) data to identify patients at risk for **primary aldosteronism**, a frequently missed cause of hypertension that raises cardiovascular risk. - Researchers used more than 22,000 patients from the Mayo Clinic Platform to develop the algorithm and then tested it on a cohort of 225,887 adults with hypertension. - The model used an XGBoost machine learning architecture and incorporated variables available in routine records: age, sex, ICD diagnoses related to hypertension and hypokalemia, systolic blood pressure values, serum potassium, and prescriptions for antihypertensives or potassium supplements. - The algorithm predicted risk of primary aldosteronism up to 12 months before clinical diagnosis in the test data set. - At a threshold set to identify low-risk individuals, the model correctly flagged more than 90% of primary aldosteronism cases while missing fewer than 10%, and it recommended selective screening for roughly two-thirds of participants who had not previously been screened. - The study team emphasized that because effective treatments exist for primary aldosteronism, earlier detection could prevent cardiovascular complications and reduce costs; they referenced the Endocrine Society’s 2025 clinical practice guideline calling for broader screening. - Details on external validation beyond the described test cohort, prospective clinical implementation, calibration metrics, or potential biases were not reported in the source article.
## Clinical Analysis & Structured Key Points
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[Login](https://endocrinenews.endocrine.org/auth/login/?redirect=https://endocrinenews.endocrine.org/video-ai-model-identifies-patients-at-risk-of-underdiagnosed-cause-of-high-blood-pressure/) ![](https://endocrinenews.endocrine.org/wp-content/themes/mg-starter/assets/images/Endocrine_News_standard_logo.svg) * * * # VIDEO: AI Model Identifies Patients at Risk of Underdiagnosed Cause of High Blood Pressure An AI model presented at #ENDO2026 helps identify patients at risk of primary aldosteronism, a common cause of undiagnosed high blood pressure. Frank Lee, MD, and Irina Bancos, MD, MS, discuss in our video series. September 9, 2026 Print this article Share this article ![](https://endocrinenews.endocrine.org/wp-content/uploads/Video_Lee-Blancos_Sept_9.jpg)[A new AI model shows promise in identifying patients at risk for primary aldosteronism, a frequently missed cause of high blood pressure. Learn more about the ENDO 2026 findings in our video interview with Frank Lee, MD, and Irina Bancos, MD, MS. ](https://www.youtube.com/shorts/_UAVgTsrZnA) ### Model recommended selective screening of patients with high blood pressure for primary aldosteronism A study of a new AI model examining 30 years of routine electronic health records (EHR) data could improve screening for primary aldosteronism, a leading cause of high blood pressure that is often unrecognized but increases patients’ risk of cardiovascular complications, according to study presented in June at **ENDO 2026** in Chicago, Ill. Primary aldosteronism occurs when the adrenal glands—the small glands located on the top of each kidney—produce too much of the hormone aldosterone. This causes aldosterone, which helps balance levels of sodium and potassium, to build up in the body. People with primary aldosteronism face a higher risk of cardiovascular disease than those with primary hypertension. The true prevalence of primary aldosteronism is unknown, but it is estimated that up to 20 percent of patients with hypertension have primary aldosteronism, according to the study’s lead researcher, Frank Lee, M.D., of Mayo Clinic in Rochester, Minn. Because effective treatments exist for primary aldosteronism, early diagnosis can prevent future complications and reduce healthcare costs, Lee explained. The Endocrine Society’s [“Primary Aldosteronism: An Endocrine Society Clinical Practice Guideline” ](https://www.endocrine.org/clinical-practice-guidelines/primary-aldosteronism-2)released in 2025 calls for more widespread screening for primary aldosteronism. This cause of high blood pressure increases patients’ risk of cardiovascular complications, including stroke, coronary artery disease, atrial fibrillation, heart failure, and renal disease. Using de-identified data from more than 22,000 patients collected between 1986 and 2025 in the [Mayo Clinic Platform](https://www.mayoclinicplatform.org/)—a federated, privacy-preserving infrastructure with multimodal clinical data – researchers developed an AI screening model that analyzed variables including age, gender, hypertension- and hypokalemia-related ICD diagnoses, systolic blood pressure measurements, potassium blood levels, and prescribed antihypertensive or potassium supplement medications. They then tested it on data of 225,887 adults with hypertension. A XGBoost architecture, a type of machine learning library, predicted patients at risk for primary aldosteronism 12 months prior to diagnosis. The model showed an AI-based approach to screen for primary aldosteronism may be feasible, Lee said. When researchers set the threshold to identify people at low risk, the model correctly flagged more than 90% of primary aldosteronism cases while missing fewer than 10%. At this setting, about two-thirds of the study participants were identified as candidates for screening. “During testing on patients with high blood pressure who had never been screened previously for primary aldosteronism, our model identified approximately two out of every three patients for further work-up,” Lee said. “Clinicians have been challenged to screen primary aldosteronism effectively. 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