ArticleScientific reports2023
A hyperaldosteronism subtypes predictive model using ensemble learning.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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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
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Tools to Predict Unilateral Primary Aldosteronism and Optimise Patient Selection for Adrenal Vein Sampling: A Systematic Review.Clinical endocrinology · 2025Pooled it
- An online explainable ensemble machine learning model for predicting epidermal growth factor receptor mutation status in lung adenocarcinoma.Translational lung cancer research · 2025Article
- Circulating miRNAs and Machine Learning for Lateralizing Primary Aldosteronism.Hypertension (Dallas, Tex. : 1979) · 2024Article
- Prediction and causal inference of cardiovascular and cerebrovascular diseases based on lifestyle questionnaires.Scientific reports · 2024Article
- A clinical assessment of portable point-of-care testing for quick cortisol assay during adrenal vein sampling.Scientific reports · 2023Observational
- O tempora, o mores: The Age We Live In, Machine Learning, Hypertension, and Primary Aldosteronism.JACC. Asia · 2023Article
- Recent progress in the diagnosis and treatment of primary aldosteronism.Hypertension research : official journal of the Japanese Society of Hypertension · 2023Review
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Authors and funding
26 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aimed to develop a machine-learning algorithm to diagnose aldosterone-producing adenoma (APA) for predicting APA probabilities. A retrospective cross-sectional analysis of the Japan Rare/Intractable Adrenal Diseases Study dataset was performed using the nationwide PA registry in Japan comprised of 41 centers. Patients treated between January 2006 and December 2019 were included. Forty-six features at screening and 13 features at confirmatory test were used for model development to calculate APA probability. Seven machine-learning programs were combined to develop the ensemble-learning model (ELM), which was externally validated. The strongest predictive factors for APA were serum potassium (s-K) at first visit, s-K after medication, plasma aldosterone concentration, aldosterone-to-renin ratio, and potassium supplementation dose. The average performance of the screening model had an AUC of 0.899; the confirmatory test model had an AUC of 0.913. In the external validation, the AUC was 0.964 in the screening model using an APA probability of 0.17. The clinical findings at screening predicted the diagnosis of APA with high accuracy. This novel algorithm can support the PA practice in primary care settings and prevent potentially curable APA patients from falling outside the PA diagnostic flowchart.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.