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Updated: Apr 28, 2026

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Predicting primary aldosteronism: risk stratification-based and machine learning-based models.

Jean-Baptiste de Freminville1,2,3, Laurence Amar1,4,5, Jean Feydy2,6

  • 1Hypertension Unit, Vascular Medicine Department, Université Paris-Cité, AP-HP, Hôpital Européen Georges Pompidou, Trousseau Universitary Hospital, Paris, Chambray-lès-Tours 37170, France.

European Journal of Preventive Cardiology
|April 26, 2026
PubMed
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This summary is machine-generated.

New algorithms improve screening for primary aldosteronism (PA) in hypertensive patients. A risk stratification tool, PAstrat, showed good sensitivity and interpretability, outperforming machine learning models in identifying secondary hypertension.

Area of Science:

  • Endocrinology
  • Cardiovascular Medicine
  • Medical Informatics

Background:

  • Screening for primary aldosteronism (PA) in hypertensive patients is challenging.
  • Improved diagnostic algorithms are needed for efficient PA detection.

Purpose of the Study:

  • To develop and compare novel algorithms for primary aldosteronism screening.
  • To evaluate risk stratification and machine learning models for PA detection.

Main Methods:

  • Developed PAstrat, a risk stratification algorithm.
  • Created logistic regression and XGBoost machine learning models.
  • Validated algorithms on derivation and external cohorts (15,507 and 768 patients).

Main Results:

Keywords:
Body mass indexDiabetes mellitusHyperaldosteronismHypertensionHypokalaemiaPheochromocytomaRenovascular hypertension

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  • PAstrat, logistic regression, and XGBoost showed AUCs of 0.80-0.83.
  • PAstrat demonstrated high negative predictive value (0.96).
  • Machine learning models had lower negative predictive values in the validation cohort.

    • Conclusions:

    • PAstrat offers a sensitive and interpretable tool for PA screening.
    • Machine learning models lack explainability and showed lower performance in validation.
    • PAstrat is a promising, user-friendly alternative for PA screening.