Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi Higashi-ku, Fukuoka, 812-8582, Japan.
Department of Diabetes and Endocrinology, Sapporo City General Hospital, Sapporo, Japan.
Sci Rep. 2022 Apr 6;12(1):5781. doi: 10.1038/s41598-022-09706-8.
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.
单侧原发性醛固酮增多症（PA）是一种常见的可通过手术治愈的内分泌性高血压。然而，超过一半接受单侧肾上腺切除术的 PA 患者仍持续患有高血压，这可能会使那些适合接受肾上腺切除术的 PA 患者望而却步。本回顾性横断面研究的目的是利用术前在常规临床实践中易于获得的预测因子，建立基于机器学习的预测术后高血压缓解的模型。共纳入 107 例经肾上腺切除术达到完全生化缓解的 PA 患者，并将其随机分配至训练和测试数据集。使用有监督机器学习算法建立完全临床成功的预测模型。在这 107 例患者中，有 40 例在两个数据集的肾上腺切除术后均达到完全临床成功。基于机器学习模型中的预测性能，选择了与完全临床成功相关的 6 个临床特征（高血压持续时间、降压药物的定义日剂量（DDD）、血浆醛固酮浓度（PAC）、性别、体重指数（BMI）和年龄）。该模型在测试数据集的预测准确性和曲线下面积（AUC）分别为 77.3%和 0.884（95%置信区间：0.737-1.000）。在独立的外部队列中，预测模型的性能与准确性为 80.4%和 AUC 为 0.867（95%置信区间：0.763-0.971）相当。高血压持续时间、降压药物的 DDD、PAC 和 BMI 与完全临床成功的预测呈非线性关系。该开发的预测模型在评估单侧肾上腺切除术的获益以及在临床实践中选择 PA 患者的手术治疗和降压药物方面可能具有一定的应用价值。
