Liu Jung-Hua, Huang Wei-Chieh, Hu Jinbo, Hong Namki, Rhee Yumie, Li Qifu, Chen Chung-Ming, Chueh Jeff S, Lin Yen-Hung, Wu Vin-Cent
Department of Communication, National Chung Cheng University, Chiayi, Taiwan.
Division of Cardiology, Department of Internal Medicine, Taipei Veterans General Hospital, Taipei, Taiwan.
JACC Asia. 2024 Nov 12;4(12):972-984. doi: 10.1016/j.jacasi.2024.09.010. eCollection 2024 Dec.
In this study, we developed and validated machine learning models to predict primary aldosteronism (PA) in hypertensive East-Asian patients, comparing their performance against the traditional saline infusion test. The motivation for this development arises from the need to provide a more efficient and standardized diagnostic approach, because the saline infusion test, although considered a gold standard, is often cumbersome, is time-consuming, and lacks uniform protocols. By offering an alternative diagnostic method, this study seeks to enhance patient care through quicker and potentially more reliable PA detection.
This study sought to both develop and evaluate the performance of machine learning models in detecting PA among hypertensive participants, in comparison to the standard saline loading test.
We used patient data from 3 distinct cohorts: TAIPAI (Taiwan Primary Aldosteronism Investigation), CONPASS (Chongqing Primary Aldosteronism Study), and a South Korean cohort. Random Forest's importance scores, XGBoost, and deep learning techniques are adopted to identify the most predictive features of primary aldosteronism.
We present detailed results of the model's performance, including accuracy, sensitivity, and specificity. The Random Forest model achieved an accuracy of 0.673 (95% CI: 0.640-0.707), significantly outperforming the baseline models.
In our discussion, we address both the strengths and limitations of our study. Although the machine learning models demonstrated superior performance in predicting primary aldosteronism, the generalizability of these findings may be limited to East-Asian hypertensive populations. Future studies are needed to validate these models in diverse demographic settings to enhance their applicability.
在本研究中，我们开发并验证了机器学习模型，用于预测东亚高血压患者的原发性醛固酮增多症（PA），并将其性能与传统的生理盐水输注试验进行比较。开展此项研究的动机源于需要提供一种更高效、标准化的诊断方法，因为生理盐水输注试验虽然被视为金标准，但往往操作繁琐、耗时且缺乏统一方案。通过提供一种替代诊断方法，本研究旨在通过更快且可能更可靠地检测PA来改善患者护理。
本研究旨在开发并评估机器学习模型在高血压参与者中检测PA的性能，并与标准生理盐水负荷试验进行比较。
我们使用了来自3个不同队列的数据：台北队列（台湾原发性醛固酮增多症调查）、重庆队列（重庆原发性醛固酮增多症研究）和一个韩国队列。采用随机森林的重要性得分、XGBoost和深度学习技术来识别原发性醛固酮增多症最具预测性的特征。
我们展示了模型性能的详细结果，包括准确性、敏感性和特异性。随机森林模型的准确率达到了0.673（95%CI：0.640 - 0.707），显著优于基线模型。
在我们的讨论中，我们阐述了本研究的优势和局限性。尽管机器学习模型在预测原发性醛固酮增多症方面表现出卓越性能，但这些发现的可推广性可能仅限于东亚高血压人群。未来需要开展研究在不同人口统计学背景下验证这些模型，以提高其适用性。
