Advanced machine learning-based screening for primary aldosteronism with plasma steroids, potassium, and renin

Commonly used screening tests for primary aldosteronism (PA) provide suboptimal diagnostic accuracy, particularly with antihypertensive medication use. This study utilized three datasets totaling 1380 patients with and without PA to develop machine learning models for screening based on plasma steroids, potassium, and renin. A feedforward neural network (FNN) model with steroids and potassium improved diagnostic accuracy compared to models without potassium. Inclusion of renin negligibly improved accuracy. The FNN and other renin-independent models showed similar accuracy before and after antihypertensive medication washout, whereas renin-dependent models exhibited poorer accuracy without medication washout. Three further optimized renin-independent models outperformed the aldosterone-to-renin ratio (ARR) for screening according to areas under receiver-operating-characteristic curves of 0.948-0.954 versus 0.839 for the ARR. Those models minimize need for medication washout and, at cut-offs for optimal 90-95% diagnostic sensitivity, reduce false positives by 53-72% to more effectively screen for PA than with the ARR.

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Publication Details

Journal
npj Digital Medicine
Published
2026-06-24
DOI
https://doi.org/10.1038/s41746-026-02906-w
Primary Topic
Hormonal Regulation and Hypertension
Type
article
Field-Weighted Citation Impact
0.00

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article

Advanced machine learning-based screening for primary aldosteronism with plasma steroids, potassium, and renin

René Jäkel, Manuel Schulze, Mirko Peitzsch, Andrea R. Horvath et al.
npj Digital Medicine
Hormonal Regulation and Hypertension
article

Advanced machine learning-based screening for primary aldosteronism with plasma steroids, potassium, and renin

René Jäkel, Manuel Schulze, Mirko Peitzsch, Andrea R. Horvath, Tracy Ann Williams, Stefanie Hahner, Carmina Teresa Fuß, R. Müller-Pfefferkorn, Wenyu Zhang, Jun Yang, Graeme Eisenhofer, Georgiana Constantinescu, Christina Pamporaki, Felix Beuschlein, Martin Reincke
article en

Abstract

Commonly used screening tests for primary aldosteronism (PA) provide suboptimal diagnostic accuracy, particularly with antihypertensive medication use. This study utilized three datasets totaling 1380 patients with and without PA to develop machine learning models for screening based on plasma steroids, potassium, and renin. A feedforward neural network (FNN) model with steroids and potassium improved diagnostic accuracy compared to models without potassium. Inclusion of renin negligibly improved accuracy. The FNN and other renin-independent models showed similar accuracy before and after antihypertensive medication washout, whereas renin-dependent models exhibited poorer accuracy without medication washout. Three further optimized renin-independent models outperformed the aldosterone-to-renin ratio (ARR) for screening according to areas under receiver-operating-characteristic curves of 0.948-0.954 versus 0.839 for the ARR. Those models minimize need for medication washout and, at cut-offs for optimal 90-95% diagnostic sensitivity, reduce false positives by 53-72% to more effectively screen for PA than with the ARR.

npj Digital MedicineVol. 9(1)
New South Wales Department of Health (AU), University of Würzburg (DE), Hudson Institute of Medical Research (AU), University Hospital of Zurich (CH), Prince of Wales Hospital (AU), Universitätsklinikum Würzburg (DE), University Hospital Carl Gustav Carus (DE), Technische Universität Dresden (DE), Ludwig-Maximilians-Universität München (DE)
Deutsche Forschungsgemeinschaft
No poverty
Openalex Percentile: Top 8%
Hormonal Regulation and Hypertension
DOIOpen Access PDFOpenAlex
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