Clinical Prediction of Heart Failure in Hemodialysis Patients: Based on the Extreme Gradient Boosting Method
Background: Heart failure (HF) is the main cause of mortality in hemodialysis (HD) patients. However, it is still a challenge for the prediction of HF in HD patients. Therefore, we aimed to establish and validate a prediction model to predict HF events in HD patients.Methods: A total of 355 maintena...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-04-01
|
| Series: | Frontiers in Genetics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2022.889378/full |
| _version_ | 1828347377213243392 |
|---|---|
| author | Yanfeng Wang Xisha Miao Gang Xiao Chun Huang Junwei Sun Ying Wang Panlong Li Xu You |
| author_facet | Yanfeng Wang Xisha Miao Gang Xiao Chun Huang Junwei Sun Ying Wang Panlong Li Xu You |
| author_sort | Yanfeng Wang |
| collection | DOAJ |
| description | Background: Heart failure (HF) is the main cause of mortality in hemodialysis (HD) patients. However, it is still a challenge for the prediction of HF in HD patients. Therefore, we aimed to establish and validate a prediction model to predict HF events in HD patients.Methods: A total of 355 maintenance HD patients from two hospitals were included in this retrospective study. A total of 21 variables, including traditional demographic characteristics, medical history, and blood biochemical indicators, were used. Two classification models were established based on the extreme gradient boosting (XGBoost) algorithm and traditional linear logistic regression. The performance of the two models was evaluated based on calibration curves and area under the receiver operating characteristic curves (AUCs). Feature importance and SHapley Additive exPlanation (SHAP) were used to recognize risk factors from the variables. The Kaplan–Meier curve of each risk factor was constructed and compared with the log-rank test.Results: Compared with the traditional linear logistic regression, the XGBoost model had better performance in accuracy (78.5 vs. 74.8%), sensitivity (79.6 vs. 75.6%), specificity (78.1 vs. 74.4%), and AUC (0.814 vs. 0.722). The feature importance and SHAP value of XGBoost indicated that age, hypertension, platelet count (PLT), C-reactive protein (CRP), and white blood cell count (WBC) were risk factors of HF. These results were further confirmed by Kaplan–Meier curves.Conclusions: The HF prediction model based on XGBoost had a satisfactory performance in predicting HF events, which could prove to be a useful tool for the early prediction of HF in HD. |
| first_indexed | 2024-04-14T00:39:29Z |
| format | Article |
| id | doaj.art-569dd609bb784fe98cfcb4fcee4e8191 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-04-14T00:39:29Z |
| publishDate | 2022-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-569dd609bb784fe98cfcb4fcee4e81912022-12-22T02:22:14ZengFrontiers Media S.A.Frontiers in Genetics1664-80212022-04-011310.3389/fgene.2022.889378889378Clinical Prediction of Heart Failure in Hemodialysis Patients: Based on the Extreme Gradient Boosting MethodYanfeng Wang0Xisha Miao1Gang Xiao2Chun Huang3Junwei Sun4Ying Wang5Panlong Li6Xu You7The School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, ChinaThe School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, ChinaDepartment of Clinical Laboratory, The Third Affiliated Hospital, Southern Medical University, Guangzhou, ChinaThe School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, ChinaThe School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, ChinaDepartment of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, ChinaThe School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, ChinaDepartment of Clinical Laboratory, The Third Affiliated Hospital, Southern Medical University, Guangzhou, ChinaBackground: Heart failure (HF) is the main cause of mortality in hemodialysis (HD) patients. However, it is still a challenge for the prediction of HF in HD patients. Therefore, we aimed to establish and validate a prediction model to predict HF events in HD patients.Methods: A total of 355 maintenance HD patients from two hospitals were included in this retrospective study. A total of 21 variables, including traditional demographic characteristics, medical history, and blood biochemical indicators, were used. Two classification models were established based on the extreme gradient boosting (XGBoost) algorithm and traditional linear logistic regression. The performance of the two models was evaluated based on calibration curves and area under the receiver operating characteristic curves (AUCs). Feature importance and SHapley Additive exPlanation (SHAP) were used to recognize risk factors from the variables. The Kaplan–Meier curve of each risk factor was constructed and compared with the log-rank test.Results: Compared with the traditional linear logistic regression, the XGBoost model had better performance in accuracy (78.5 vs. 74.8%), sensitivity (79.6 vs. 75.6%), specificity (78.1 vs. 74.4%), and AUC (0.814 vs. 0.722). The feature importance and SHAP value of XGBoost indicated that age, hypertension, platelet count (PLT), C-reactive protein (CRP), and white blood cell count (WBC) were risk factors of HF. These results were further confirmed by Kaplan–Meier curves.Conclusions: The HF prediction model based on XGBoost had a satisfactory performance in predicting HF events, which could prove to be a useful tool for the early prediction of HF in HD.https://www.frontiersin.org/articles/10.3389/fgene.2022.889378/fullmachine learningextreme gradient boostingheart failure predictionhemodialysisrisk factors |
| spellingShingle | Yanfeng Wang Xisha Miao Gang Xiao Chun Huang Junwei Sun Ying Wang Panlong Li Xu You Clinical Prediction of Heart Failure in Hemodialysis Patients: Based on the Extreme Gradient Boosting Method Frontiers in Genetics machine learning extreme gradient boosting heart failure prediction hemodialysis risk factors |
| title | Clinical Prediction of Heart Failure in Hemodialysis Patients: Based on the Extreme Gradient Boosting Method |
| title_full | Clinical Prediction of Heart Failure in Hemodialysis Patients: Based on the Extreme Gradient Boosting Method |
| title_fullStr | Clinical Prediction of Heart Failure in Hemodialysis Patients: Based on the Extreme Gradient Boosting Method |
| title_full_unstemmed | Clinical Prediction of Heart Failure in Hemodialysis Patients: Based on the Extreme Gradient Boosting Method |
| title_short | Clinical Prediction of Heart Failure in Hemodialysis Patients: Based on the Extreme Gradient Boosting Method |
| title_sort | clinical prediction of heart failure in hemodialysis patients based on the extreme gradient boosting method |
| topic | machine learning extreme gradient boosting heart failure prediction hemodialysis risk factors |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2022.889378/full |
| work_keys_str_mv | AT yanfengwang clinicalpredictionofheartfailureinhemodialysispatientsbasedontheextremegradientboostingmethod AT xishamiao clinicalpredictionofheartfailureinhemodialysispatientsbasedontheextremegradientboostingmethod AT gangxiao clinicalpredictionofheartfailureinhemodialysispatientsbasedontheextremegradientboostingmethod AT chunhuang clinicalpredictionofheartfailureinhemodialysispatientsbasedontheextremegradientboostingmethod AT junweisun clinicalpredictionofheartfailureinhemodialysispatientsbasedontheextremegradientboostingmethod AT yingwang clinicalpredictionofheartfailureinhemodialysispatientsbasedontheextremegradientboostingmethod AT panlongli clinicalpredictionofheartfailureinhemodialysispatientsbasedontheextremegradientboostingmethod AT xuyou clinicalpredictionofheartfailureinhemodialysispatientsbasedontheextremegradientboostingmethod |