Prediction of hyperkalemia in ESRD patients by identification of multiple leads and multiple features on ECG
Background 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 testin...
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| Language: | English |
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Taylor & Francis Group
2023-12-01
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| Series: | Renal Failure |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/0886022X.2023.2212800 |
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| author | Daojun Xu Bin Zhou Jiaqi Zhang Chenyu Li Chen Guan Yuxuan Liu Lin Li Haina Li Li Cui Lingyu Xu Hang Liu Li Zhen Yan Xu |
| author_facet | Daojun Xu Bin Zhou Jiaqi Zhang Chenyu Li Chen Guan Yuxuan Liu Lin Li Haina Li Li Cui Lingyu Xu Hang Liu Li Zhen Yan Xu |
| author_sort | Daojun Xu |
| collection | DOAJ |
| description | Background 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.Methods 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.Results 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.Conclusion 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. |
| format | Article |
| id | doaj-art-0d96e8f28d7b4e49a7d517925f43a062 |
| institution | OA Journals |
| issn | 0886-022X 1525-6049 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Renal Failure |
| spelling | doaj-art-0d96e8f28d7b4e49a7d517925f43a0622025-08-20T02:16:11ZengTaylor & Francis GroupRenal Failure0886-022X1525-60492023-12-0145110.1080/0886022X.2023.2212800Prediction of hyperkalemia in ESRD patients by identification of multiple leads and multiple features on ECGDaojun Xu0Bin Zhou1Jiaqi Zhang2Chenyu Li3Chen Guan4Yuxuan Liu5Lin Li6Haina Li7Li Cui8Lingyu Xu9Hang Liu10Li Zhen11Yan Xu12Department of Nephrology, The Affiliated Hospital of Qingdao University, Qingdao, P.R. ChinaDepartment of Nephrology, The Affiliated Hospital of Qingdao University, Qingdao, P.R. ChinaDepartment of Nephrology, The Affiliated Hospital of Qingdao University, Qingdao, P.R. ChinaMedizinische Klinik und Poliklinik IV, Klinikum der Universität, LMU München, München, GermanyDepartment of Nephrology, The Affiliated Hospital of Qingdao University, Qingdao, P.R. ChinaSchool of Artificial Intelligence, Sun Yat-sen University, Guangzhou, P.R. ChinaDepartment of Nephrology, The Affiliated Hospital of Qingdao University, Qingdao, P.R. ChinaDepartment of Nephrology, The Affiliated Hospital of Qingdao University, Qingdao, P.R. ChinaDepartment of Nephrology, The Affiliated Hospital of Qingdao University, Qingdao, P.R. ChinaDepartment of Nephrology, The Affiliated Hospital of Qingdao University, Qingdao, P.R. ChinaDepartment of Nephrology, The Affiliated Hospital of Qingdao University, Qingdao, P.R. ChinaDepartment of Nephrology, The Affiliated Hospital of Qingdao University, Qingdao, P.R. ChinaDepartment of Nephrology, The Affiliated Hospital of Qingdao University, Qingdao, P.R. ChinaBackground 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.Methods 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.Results 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.Conclusion 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.https://www.tandfonline.com/doi/10.1080/0886022X.2023.2212800Electrocardiogramnoninvasive hyperkalemia predictionend-stage renal diseasemachine learninglogistic regression |
| spellingShingle | Daojun Xu Bin Zhou Jiaqi Zhang Chenyu Li Chen Guan Yuxuan Liu Lin Li Haina Li Li Cui Lingyu Xu Hang Liu Li Zhen Yan Xu Prediction of hyperkalemia in ESRD patients by identification of multiple leads and multiple features on ECG Renal Failure Electrocardiogram noninvasive hyperkalemia prediction end-stage renal disease machine learning logistic regression |
| title | Prediction of hyperkalemia in ESRD patients by identification of multiple leads and multiple features on ECG |
| title_full | Prediction of hyperkalemia in ESRD patients by identification of multiple leads and multiple features on ECG |
| title_fullStr | Prediction of hyperkalemia in ESRD patients by identification of multiple leads and multiple features on ECG |
| title_full_unstemmed | Prediction of hyperkalemia in ESRD patients by identification of multiple leads and multiple features on ECG |
| title_short | Prediction of hyperkalemia in ESRD patients by identification of multiple leads and multiple features on ECG |
| title_sort | prediction of hyperkalemia in esrd patients by identification of multiple leads and multiple features on ecg |
| topic | Electrocardiogram noninvasive hyperkalemia prediction end-stage renal disease machine learning logistic regression |
| url | https://www.tandfonline.com/doi/10.1080/0886022X.2023.2212800 |
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