ArticleScientific reports2022
Machine learning-based models for predicting clinical outcomes after surgery in unilateral primary aldosteronism.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Clinical and biochemical outcomes after adrenalectomy for primary aldosteronism in tertiary and quaternary referral centers: data from SOPRANO study.Hypertension research : official journal of the Japanese Society of Hypertension · 2024Pooled it
- Article
- CT-based body composition enhances prognostic performance of PASO score for surgical outcome in primary aldosteronism.Hypertension research : official journal of the Japanese Society of Hypertension · 2026Article
- Molecular pathology of primary aldosteronism and hypercortisolism: Impact on adrenal surgery.iScience · 2026Review
- Treatment for patients with unilateral aldosteronism: surgical or medical treatment.Hypertension research : official journal of the Japanese Society of Hypertension · 2025Article
- Factors affecting clinical outcomes of minimally invasive adrenalectomy for unilateral primary aldosteronism: a single-center retrospective cohort study.Gland surgery · 2025Article
- Random forest algorithm for predicting postoperative hypotension in oral cancer resection and free flap reconstruction surgery.Scientific reports · 2025Article
- Artificial intelligence in primary aldosteronism: current achievements and future challenges.Frontiers in molecular biosciences · 2025Review
- Predicting the resolution of hypertension following adrenalectomy in primary aldosteronism: Controversies and unresolved issues a narrative review.Langenbeck's archives of surgery · 2024Review
- Machine learning-based model for prediction and feature analysis of recurrence in pancreatic neuroendocrine tumors G1/G2.Journal of gastroenterology · 2023Article
- A new machine learning-based prediction model for subtype diagnosis in primary aldosteronism.Frontiers in endocrinology · 2022Article
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17 authors.
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Abstract
Unilateral subtype of primary aldosteronism (PA) is a common surgically curable form of endocrine hypertension. However, more than half of the patients with PA who undergo unilateral adrenalectomy suffer from persistent hypertension, which may discourage those with PA from undergoing adrenalectomy even when appropriate. The aim of this retrospective cross-sectional study was to develop machine learning-based models for predicting postoperative hypertensive remission using preoperative predictors that are readily available in routine clinical practice. A total of 107 patients with PA who achieved complete biochemical success after adrenalectomy were included and randomly assigned to the training and test datasets. Predictive models of complete clinical success were developed using supervised machine learning algorithms. Of 107 patients, 40 achieved complete clinical success after adrenalectomy in both datasets. Six clinical features associated with complete clinical success (duration of hypertension, defined daily dose (DDD) of antihypertensive medication, plasma aldosterone concentration (PAC), sex, body mass index (BMI), and age) were selected based on predictive performance in the machine learning-based model. The predictive accuracy and area under the curve (AUC) for the developed model in the test dataset were 77.3% and 0.884 (95% confidence interval: 0.737-1.000), respectively. In an independent external cohort, the performance of the predictive model was found to be comparable with an accuracy of 80.4% and AUC of 0.867 (95% confidence interval: 0.763-0.971). The duration of hypertension, DDD of antihypertensive medication, PAC, and BMI were non-linearly related to the prediction of complete clinical success. The developed predictive model may be useful in assessing the benefit of unilateral adrenalectomy and in selecting surgical treatment and antihypertensive medication for patients with PA in clinical practice.
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