Machine learning model and nomogram to predict the risk of heart failure hospitalization in peritoneal dialysis patients
Introduction The study presented here aimed to establish a predictive model for heart failure (HF) and all-cause mortality in peritoneal dialysis (PD) patients with machine learning (ML) algorithm.Methods We retrospectively included 1006 patients who initiated PD from 2010 to 2016. XGBoost, random f...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Renal Failure |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/0886022X.2024.2324071 |
| _version_ | 1826860063043616768 |
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| author | Liping Xu Fang Cao Lian Wang Weihua Liu Meizhu Gao Li Zhang Fuyuan Hong Miao Lin |
| author_facet | Liping Xu Fang Cao Lian Wang Weihua Liu Meizhu Gao Li Zhang Fuyuan Hong Miao Lin |
| author_sort | Liping Xu |
| collection | DOAJ |
| description | Introduction The study presented here aimed to establish a predictive model for heart failure (HF) and all-cause mortality in peritoneal dialysis (PD) patients with machine learning (ML) algorithm.Methods We retrospectively included 1006 patients who initiated PD from 2010 to 2016. XGBoost, random forest (RF), and AdaBoost were used to train models for assessing risk for 1-year and 5-year HF hospitalization and mortality. The performance was validated using fivefold cross-validation. The optimal ML algorithm was used to construct the models to predictive the risk of the HF and all-cause mortality. The prediction performance of ML methods and Cox regression was compared.Results Over a median follow-up of 49 months. Two hundred and ninety-eight patients developed HF required hospitalization; 199 patients died during the follow-up. The RF model (AUC = 0.853) was the best performing model for predicting HF, and the XGBoost model (AUC = 0.871) was the best model for predicting mortality. Baseline moderate or severe renal disease, systolic blood pressure (SBP), body mass index (BMI), age, Charlson Comorbidity Index (CCI) score were strongly associated with HF hospitalization, whereas age, CCI score, creatinine, age, high-density lipoprotein cholesterol (HDL-C), total cholesterol, baseline estimated glomerular filtration rate (eGFR) were the most significant predictors of mortality. For all the above endpoints, the ML models demonstrated better discrimination than Cox regression.Conclusions We developed and validated a novel method to predict the risk factors of HF and all-cause mortality that integrates readily available clinical, laboratory, and electrocardiographic variables to predict the risk of HF among PD patients. |
| first_indexed | 2024-04-24T11:16:55Z |
| format | Article |
| id | doaj.art-39e1f5bae7924abdbf5c3b2fd4466a0e |
| institution | Directory Open Access Journal |
| issn | 0886-022X 1525-6049 |
| language | English |
| last_indexed | 2025-02-16T19:40:36Z |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Renal Failure |
| spelling | doaj.art-39e1f5bae7924abdbf5c3b2fd4466a0e2025-01-23T04:17:48ZengTaylor & Francis GroupRenal Failure0886-022X1525-60492024-12-0146110.1080/0886022X.2024.2324071Machine learning model and nomogram to predict the risk of heart failure hospitalization in peritoneal dialysis patientsLiping Xu0Fang Cao1Lian Wang2Weihua Liu3Meizhu Gao4Li Zhang5Fuyuan Hong6Miao Lin7Department of Nephrology, The Second Affiliated Hospital of Xiamen Medical College, Xiamen, ChinaDepartment of Nephrology, Provincial Clinical College, Fujian Medical University, Fujian Provincial Hospital, Fuzhou, Fujian, ChinaDepartment of Nephrology, Provincial Clinical College, Fujian Medical University, Fujian Provincial Hospital, Fuzhou, Fujian, ChinaDepartment of Nephrology, Provincial Clinical College, Fujian Medical University, Fujian Provincial Hospital, Fuzhou, Fujian, ChinaDepartment of Nephrology, Provincial Clinical College, Fujian Medical University, Fujian Provincial Hospital, Fuzhou, Fujian, ChinaDepartment of Nephrology, Provincial Clinical College, Fujian Medical University, Fujian Provincial Hospital, Fuzhou, Fujian, ChinaDepartment of Nephrology, Provincial Clinical College, Fujian Medical University, Fujian Provincial Hospital, Fuzhou, Fujian, ChinaDepartment of Nephrology, Provincial Clinical College, Fujian Medical University, Fujian Provincial Hospital, Fuzhou, Fujian, ChinaIntroduction The study presented here aimed to establish a predictive model for heart failure (HF) and all-cause mortality in peritoneal dialysis (PD) patients with machine learning (ML) algorithm.Methods We retrospectively included 1006 patients who initiated PD from 2010 to 2016. XGBoost, random forest (RF), and AdaBoost were used to train models for assessing risk for 1-year and 5-year HF hospitalization and mortality. The performance was validated using fivefold cross-validation. The optimal ML algorithm was used to construct the models to predictive the risk of the HF and all-cause mortality. The prediction performance of ML methods and Cox regression was compared.Results Over a median follow-up of 49 months. Two hundred and ninety-eight patients developed HF required hospitalization; 199 patients died during the follow-up. The RF model (AUC = 0.853) was the best performing model for predicting HF, and the XGBoost model (AUC = 0.871) was the best model for predicting mortality. Baseline moderate or severe renal disease, systolic blood pressure (SBP), body mass index (BMI), age, Charlson Comorbidity Index (CCI) score were strongly associated with HF hospitalization, whereas age, CCI score, creatinine, age, high-density lipoprotein cholesterol (HDL-C), total cholesterol, baseline estimated glomerular filtration rate (eGFR) were the most significant predictors of mortality. For all the above endpoints, the ML models demonstrated better discrimination than Cox regression.Conclusions We developed and validated a novel method to predict the risk factors of HF and all-cause mortality that integrates readily available clinical, laboratory, and electrocardiographic variables to predict the risk of HF among PD patients.https://www.tandfonline.com/doi/10.1080/0886022X.2024.2324071Peritoneal dialysismachine learningall-cause mortalityheart failurecomplications |
| spellingShingle | Liping Xu Fang Cao Lian Wang Weihua Liu Meizhu Gao Li Zhang Fuyuan Hong Miao Lin Machine learning model and nomogram to predict the risk of heart failure hospitalization in peritoneal dialysis patients Renal Failure Peritoneal dialysis machine learning all-cause mortality heart failure complications |
| title | Machine learning model and nomogram to predict the risk of heart failure hospitalization in peritoneal dialysis patients |
| title_full | Machine learning model and nomogram to predict the risk of heart failure hospitalization in peritoneal dialysis patients |
| title_fullStr | Machine learning model and nomogram to predict the risk of heart failure hospitalization in peritoneal dialysis patients |
| title_full_unstemmed | Machine learning model and nomogram to predict the risk of heart failure hospitalization in peritoneal dialysis patients |
| title_short | Machine learning model and nomogram to predict the risk of heart failure hospitalization in peritoneal dialysis patients |
| title_sort | machine learning model and nomogram to predict the risk of heart failure hospitalization in peritoneal dialysis patients |
| topic | Peritoneal dialysis machine learning all-cause mortality heart failure complications |
| url | https://www.tandfonline.com/doi/10.1080/0886022X.2024.2324071 |
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