Key result
A machine learning-based model using six preoperative clinical features predicted complete clinical success after unilateral adrenalectomy with an accuracy of 77.3% and an AUC of 0.884 in the test dataset.
Why the study?
Over half of patients with primary aldosteronism suffer from persistent hypertension after unilateral adrenalectomy, potentially discouraging surgery even when appropriate.
Can a machine learning-based model using routine preoperative clinical features accurately predict complete clinical success after unilateral adrenalectomy in patients with primary aldosteronism?
Cross-Sectional (n=158)
Yes
Can a machine learning-based model using routine preoperative clinical features accurately predict complete clinical success after unilateral adrenalectomy in patients with primary aldosteronism?
Effect estimate: AUC 0.884 (95% CI 0.737-1.000)
A machine learning model using six routine preoperative clinical features can accurately predict complete clinical success (hypertension remission) after unilateral adrenalectomy in patients with primary aldosteronism.
May support preoperative counseling in unilateral PA; hypothesis-generating and requires prospective validation before use.
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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Kaneko et al. (2022) conducted a cross-sectional in Unilateral primary aldosteronism (n=158). Preoperative clinical features (duration of hypertension, antihypertensive medication dose, plasma aldosterone concentration, sex, BMI, age) was evaluated on Prediction of complete clinical success (normalization of blood pressure without antihypertensive medication) (AUC 0.884, 95% CI 0.737-1.000). A machine learning-based model using six preoperative clinical features predicted complete clinical success after unilateral adrenalectomy with an accuracy of 77.3% and an AUC of 0.884 in the test dataset.
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