Department of Nephrology, The Affiliated Hospital of Qingdao University, Qingdao, P.R. China.
Medizinische Klinik und Poliklinik IV, Klinikum der Universität, LMU München, München, Germany.
Ren Fail. 2023 Dec;45(1):2212800. doi: 10.1080/0886022X.2023.2212800.
Patients with end-stage renal disease (ESRD) especially those undergoing dialysis have a high prevalence of hyperkalemia, which must be detected and treated immediately. But the initial symptoms of hyperkalemia are insidious, and traditional laboratory serum potassium concentration testing takes time. Therefore, rapid and real-time measurement of serum potassium is urgently needed. In this study, different machine learning methods were used to make rapid predictions of different degrees of hyperkalemia by analyzing the ECG.
A total of 1024 datasets of ECG and serum potassium concentrations were analyzed from December 2020 to December 2021. The data were scaled into training and test sets. Different machine learning models (LR, SVM, CNN, XGB, Adaboost) were built for dichotomous prediction of hyperkalemia by analyzing 48 features of chest leads V2-V5. The performance of the models was also evaluated and compared using sensitivity, specificity, accuracy, accuracy, F1 score and AUC.
We constructed different machine models to predict hyperkalemia using LR and four other common machine-learning methods. The AUCs of the different models ranged from 0.740 (0.661, 0.810) to 0.931 (0.912,0.953) when different serum potassium concentrations were used as the diagnostic threshold for hyperkalemia, respectively. As the diagnostic threshold of hyperkalemia was raised, the sensitivity, specificity, accuracy and precision of the model decreased to various degrees. And AUC also performed less well than when predicting mild hyperkalemia.
Noninvasive and rapid prediction of hyperkalemia can be achieved by analyzing specific waveforms on the ECG by machine learning methods. But overall, XGB had a higher AUC in mild hyperkalemia, but SVM performed better in predicting more severe hyperkalemia.
终末期肾病（ESRD）患者，尤其是接受透析治疗的患者，高钾血症的患病率很高，必须立即发现和治疗。但高钾血症的初始症状隐匿，传统的血清钾浓度检测实验室检测需要时间。因此，迫切需要快速实时测量血清钾。在这项研究中，通过分析心电图，使用不同的机器学习方法来快速预测不同程度的高钾血症。
分析了 2020 年 12 月至 2021 年 12 月期间共 1024 组心电图和血清钾浓度数据集。数据被缩放到训练集和测试集。通过分析 V2-V5 导联的 48 个特征，使用不同的机器学习模型（LR、SVM、CNN、XGB、Adaboost）对高钾血症进行二分类预测。使用灵敏度、特异性、准确性、准确度、F1 分数和 AUC 来评估和比较模型的性能。
我们使用 LR 和其他四种常见的机器学习方法构建了不同的机器模型来预测高钾血症。当不同的血清钾浓度作为高钾血症的诊断阈值时，不同模型的 AUC 范围分别为 0.740（0.661，0.810）至 0.931（0.912，0.953）。随着高钾血症诊断阈值的升高，模型的灵敏度、特异性、准确性和精密度均不同程度下降。AUC 也不如预测轻度高钾血症时表现良好。
通过机器学习方法分析心电图上的特定波形，可以实现非侵入性和快速预测高钾血症。但总体而言，XGB 在轻度高钾血症时具有较高的 AUC，而 SVM 在预测更严重的高钾血症时表现更好。
