Machine learning approach to predict subtypes of primary aldosteronism is helpful to estimate indication of adrenal vein sampling
Machine learning approach to predict subtypes of primary aldosteronism is helpful to estimate indication of adrenal vein sampling
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预测原发性醛固酮增多症亚型的机器学习方法有助于估计肾上腺静脉采样的指征
DOI：
10.1007/s40292-022-00523-8
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Suzuki Ryo
中科院分区：
文献类型：
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作者：
Tamaru Shinichi;Suwanai Hirotsugu;Abe Hironori;Sasaki Junko;Ishii Keitaro;Iwasaki Hajime;Shikuma Jumpei;Ito Rokuro;Miwa Takashi;Sasaki Toru;Takamiya Tomoko;Inoue Shigeru;Saito Kazuhiro;Odawara Masato;Suzuki Ryo
前言原发性醛固酮增多症(PA)是一种常见病。尤其是单侧PA(UPA)，心血管疾病的风险很高，适当的定位很重要。肾上腺静脉采样(AVS)是一种常用的PA定位方法，但其实用性有限。本研究的目的是建立一个基于机器学习的双侧或单侧PA预测模型，以提取单侧或双侧PA或密切观察的病例。方法对2010年1月至2021年6月在本院就诊的154例PA患者进行回顾性分析。结果机器学习的准确率为88%，预测uPA的因素依次为生理盐水输注试验后的血浆醛固酮浓度、卡托普利激发试验后的醛固酮/肾素比值、血钾和醛固酮/肾素比值。综合这些因素，其准确性、敏感性、特异性和曲线下面积(AUC)分别为91%、70%、99%和0.91。此外，我们还检查了uPA的手术结果，发现被预测者诊断为单侧的患者的临床表现有所改善，而被预测者诊断为双侧的患者的临床表现没有改善。结论基于机器学习的预测模型可以支持选择肾上腺静脉采样或观察的性能。
IntroductionPrimary aldosteronism (PA) is a common disease. Especially in unilateral PA (UPA), the risk of cardiovascular disease is high and proper localization is important. Adrenal vein sampling (AVS) is commonly used to localize PA, but its availability is limited. Therefore, it is important to predict the unilateral or bilateral PA and to choose the appropriate cases for AVS or watchful observation.AimThe purpose of this study is to develop a model using machine learning to predict bilateral or unilateral PA to extract cases for AVS or watchful observation.MethodsWe retrospectively analyzed 154 patients diagnosed with PA and who underwent AVS at our hospital between January 2010 and June 2021. Based on machine learning, we determined predictors of PA subtypes diagnosis from the results of blood and loading tests.ResultsThe accuracy of the machine learning was 88% and the top predictors of the UPA were plasma aldosterone concentration after the saline infusion test, aldosterone to renin ratio after the captopril challenge test, serum potassium and aldosterone-to-renin ratio. By using these factors, the accuracy, sensitivity, specificity and the area under the curve (AUC) were 91%, 70%, 99% and 0.91, respectively. Furthermore, we examined the surgical outcomes of UPA and found that the group diagnosed as unilateral by the predictors showed improvement in clinical findings, while the group diagnosed as bilateral by the predictors showed no improvement.ConclusionOur predictive model based on machine learning can support to choose the performance of adrenal vein sampling or watchful observation.