Prediction and Risk Stratification of Cardiovascular Disease in Diabetic Kidney Disease Patients
BackgroundDiabetic kidney disease (DKD) patients are facing an extremely high risk of cardiovascular disease (CVD), which is a major cause of death for DKD patients. We aimed to build a deep learning model to predict CVD risk among DKD patients and perform risk stratifying, which could help them per...
| Những tác giả chính: | , , , , , |
|---|---|
| Định dạng: | Bài viết |
| Ngôn ngữ: | English |
| Được phát hành: |
Frontiers Media S.A.
2022-06-01
|
| Loạt: | Frontiers in Cardiovascular Medicine |
| Những chủ đề: | |
| Truy cập trực tuyến: | https://www.frontiersin.org/articles/10.3389/fcvm.2022.923549/full |
| _version_ | 1828203411073400832 |
|---|---|
| author | Jingjing Ren Jingjing Ren Jingjing Ren Jingjing Ren Jingjing Ren Dongwei Liu Dongwei Liu Dongwei Liu Dongwei Liu Guangpu Li Guangpu Li Guangpu Li Guangpu Li Guangpu Li Jiayu Duan Jiayu Duan Jiayu Duan Jiayu Duan Jiayu Duan Jiancheng Dong Zhangsuo Liu Zhangsuo Liu Zhangsuo Liu Zhangsuo Liu |
| author_facet | Jingjing Ren Jingjing Ren Jingjing Ren Jingjing Ren Jingjing Ren Dongwei Liu Dongwei Liu Dongwei Liu Dongwei Liu Guangpu Li Guangpu Li Guangpu Li Guangpu Li Guangpu Li Jiayu Duan Jiayu Duan Jiayu Duan Jiayu Duan Jiayu Duan Jiancheng Dong Zhangsuo Liu Zhangsuo Liu Zhangsuo Liu Zhangsuo Liu |
| author_sort | Jingjing Ren |
| collection | DOAJ |
| description | BackgroundDiabetic kidney disease (DKD) patients are facing an extremely high risk of cardiovascular disease (CVD), which is a major cause of death for DKD patients. We aimed to build a deep learning model to predict CVD risk among DKD patients and perform risk stratifying, which could help them perform early intervention and improve personal health management.MethodsA retrospective cohort study was conducted to assess the risk of the occurrence of composite cardiovascular disease, which includes coronary heart disease, cerebrovascular diseases, congestive heart failure, and peripheral artery disease, in DKD patients. A least absolute shrinkage and selection operator (LASSO) regression was used to perform the variable selection. A deep learning-based survival model called DeepSurv, based on a feed-forward neural network was developed to predict CVD risk among DKD patients. We compared the model performance with the conventional Cox proportional hazards (CPH) model and the Random survival forest (RSF) model using the concordance index (C-index), the area under the curve (AUC), and integrated Brier scores (IBS).ResultsWe recruited 890 patients diagnosed with DKD in this retrospective study. During a median follow-up of 10.4 months, there are 289 patients who sustained a subsequent CVD. Seven variables, including age, high density lipoprotein (HDL), hemoglobin (Hb), systolic blood pressure (SBP), smoking status, 24 h urinary protein excretion, and total cholesterol (TC), chosen by LASSO regression were used to develop the predictive model. The DeepSurv model showed the best performance, achieved a C-index of 0.767(95% confidence intervals [CI]: 0.717–0.817), AUC of 0.780(95%CI: 0.721–0.839), and IBS of 0.067 in the validation set. Then we used the cut-off value determined by ROC (receiver operating characteristic) curve to divide the patients into different risk groups. Moreover, the DeepSurv model was also applied to develop an online calculation tool for patients to conduct risk monitoring.ConclusionA deep-learning-based predictive model using seven clinical variables can effectively predict CVD risk among DKD patients and perform risk stratification. An online calculator allows its easy implementation. |
| first_indexed | 2024-04-12T12:06:26Z |
| format | Article |
| id | doaj.art-01b51a493ea843e8a3dff2e1b224641d |
| institution | Directory Open Access Journal |
| issn | 2297-055X |
| language | English |
| last_indexed | 2024-04-12T12:06:26Z |
| publishDate | 2022-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Cardiovascular Medicine |
| spelling | doaj.art-01b51a493ea843e8a3dff2e1b224641d2022-12-22T03:33:42ZengFrontiers Media S.A.Frontiers in Cardiovascular Medicine2297-055X2022-06-01910.3389/fcvm.2022.923549923549Prediction and Risk Stratification of Cardiovascular Disease in Diabetic Kidney Disease PatientsJingjing Ren0Jingjing Ren1Jingjing Ren2Jingjing Ren3Jingjing Ren4Dongwei Liu5Dongwei Liu6Dongwei Liu7Dongwei Liu8Guangpu Li9Guangpu Li10Guangpu Li11Guangpu Li12Guangpu Li13Jiayu Duan14Jiayu Duan15Jiayu Duan16Jiayu Duan17Jiayu Duan18Jiancheng Dong19Zhangsuo Liu20Zhangsuo Liu21Zhangsuo Liu22Zhangsuo Liu23Department of Integrated Traditional and Western Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaResearch Institute of Nephrology, Zhengzhou University, Zhengzhou, ChinaHenan Province Research Center for Kidney Disease, Zhengzhou, ChinaKey Laboratory of Precision Diagnosis and Treatment for Chronic Kidney Disease in Henan Province, Zhengzhou, ChinaClinical Research Center of Big-data, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaDepartment of Integrated Traditional and Western Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaResearch Institute of Nephrology, Zhengzhou University, Zhengzhou, ChinaHenan Province Research Center for Kidney Disease, Zhengzhou, ChinaKey Laboratory of Precision Diagnosis and Treatment for Chronic Kidney Disease in Henan Province, Zhengzhou, ChinaDepartment of Integrated Traditional and Western Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaResearch Institute of Nephrology, Zhengzhou University, Zhengzhou, ChinaHenan Province Research Center for Kidney Disease, Zhengzhou, ChinaKey Laboratory of Precision Diagnosis and Treatment for Chronic Kidney Disease in Henan Province, Zhengzhou, ChinaClinical Research Center of Big-data, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaDepartment of Integrated Traditional and Western Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaResearch Institute of Nephrology, Zhengzhou University, Zhengzhou, ChinaHenan Province Research Center for Kidney Disease, Zhengzhou, ChinaKey Laboratory of Precision Diagnosis and Treatment for Chronic Kidney Disease in Henan Province, Zhengzhou, ChinaClinical Research Center of Big-data, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaClinical Research Center of Big-data, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaDepartment of Integrated Traditional and Western Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaResearch Institute of Nephrology, Zhengzhou University, Zhengzhou, ChinaHenan Province Research Center for Kidney Disease, Zhengzhou, ChinaKey Laboratory of Precision Diagnosis and Treatment for Chronic Kidney Disease in Henan Province, Zhengzhou, ChinaBackgroundDiabetic kidney disease (DKD) patients are facing an extremely high risk of cardiovascular disease (CVD), which is a major cause of death for DKD patients. We aimed to build a deep learning model to predict CVD risk among DKD patients and perform risk stratifying, which could help them perform early intervention and improve personal health management.MethodsA retrospective cohort study was conducted to assess the risk of the occurrence of composite cardiovascular disease, which includes coronary heart disease, cerebrovascular diseases, congestive heart failure, and peripheral artery disease, in DKD patients. A least absolute shrinkage and selection operator (LASSO) regression was used to perform the variable selection. A deep learning-based survival model called DeepSurv, based on a feed-forward neural network was developed to predict CVD risk among DKD patients. We compared the model performance with the conventional Cox proportional hazards (CPH) model and the Random survival forest (RSF) model using the concordance index (C-index), the area under the curve (AUC), and integrated Brier scores (IBS).ResultsWe recruited 890 patients diagnosed with DKD in this retrospective study. During a median follow-up of 10.4 months, there are 289 patients who sustained a subsequent CVD. Seven variables, including age, high density lipoprotein (HDL), hemoglobin (Hb), systolic blood pressure (SBP), smoking status, 24 h urinary protein excretion, and total cholesterol (TC), chosen by LASSO regression were used to develop the predictive model. The DeepSurv model showed the best performance, achieved a C-index of 0.767(95% confidence intervals [CI]: 0.717–0.817), AUC of 0.780(95%CI: 0.721–0.839), and IBS of 0.067 in the validation set. Then we used the cut-off value determined by ROC (receiver operating characteristic) curve to divide the patients into different risk groups. Moreover, the DeepSurv model was also applied to develop an online calculation tool for patients to conduct risk monitoring.ConclusionA deep-learning-based predictive model using seven clinical variables can effectively predict CVD risk among DKD patients and perform risk stratification. An online calculator allows its easy implementation.https://www.frontiersin.org/articles/10.3389/fcvm.2022.923549/fullmachine learningdiabetic kidney diseasecardiovascular diseaseprediction modelrisk stratification |
| spellingShingle | Jingjing Ren Jingjing Ren Jingjing Ren Jingjing Ren Jingjing Ren Dongwei Liu Dongwei Liu Dongwei Liu Dongwei Liu Guangpu Li Guangpu Li Guangpu Li Guangpu Li Guangpu Li Jiayu Duan Jiayu Duan Jiayu Duan Jiayu Duan Jiayu Duan Jiancheng Dong Zhangsuo Liu Zhangsuo Liu Zhangsuo Liu Zhangsuo Liu Prediction and Risk Stratification of Cardiovascular Disease in Diabetic Kidney Disease Patients Frontiers in Cardiovascular Medicine machine learning diabetic kidney disease cardiovascular disease prediction model risk stratification |
| title | Prediction and Risk Stratification of Cardiovascular Disease in Diabetic Kidney Disease Patients |
| title_full | Prediction and Risk Stratification of Cardiovascular Disease in Diabetic Kidney Disease Patients |
| title_fullStr | Prediction and Risk Stratification of Cardiovascular Disease in Diabetic Kidney Disease Patients |
| title_full_unstemmed | Prediction and Risk Stratification of Cardiovascular Disease in Diabetic Kidney Disease Patients |
| title_short | Prediction and Risk Stratification of Cardiovascular Disease in Diabetic Kidney Disease Patients |
| title_sort | prediction and risk stratification of cardiovascular disease in diabetic kidney disease patients |
| topic | machine learning diabetic kidney disease cardiovascular disease prediction model risk stratification |
| url | https://www.frontiersin.org/articles/10.3389/fcvm.2022.923549/full |
| work_keys_str_mv | AT jingjingren predictionandriskstratificationofcardiovasculardiseaseindiabetickidneydiseasepatients AT jingjingren predictionandriskstratificationofcardiovasculardiseaseindiabetickidneydiseasepatients AT jingjingren predictionandriskstratificationofcardiovasculardiseaseindiabetickidneydiseasepatients AT jingjingren predictionandriskstratificationofcardiovasculardiseaseindiabetickidneydiseasepatients AT jingjingren predictionandriskstratificationofcardiovasculardiseaseindiabetickidneydiseasepatients AT dongweiliu predictionandriskstratificationofcardiovasculardiseaseindiabetickidneydiseasepatients AT dongweiliu predictionandriskstratificationofcardiovasculardiseaseindiabetickidneydiseasepatients AT dongweiliu predictionandriskstratificationofcardiovasculardiseaseindiabetickidneydiseasepatients AT dongweiliu predictionandriskstratificationofcardiovasculardiseaseindiabetickidneydiseasepatients AT guangpuli predictionandriskstratificationofcardiovasculardiseaseindiabetickidneydiseasepatients AT guangpuli predictionandriskstratificationofcardiovasculardiseaseindiabetickidneydiseasepatients AT guangpuli predictionandriskstratificationofcardiovasculardiseaseindiabetickidneydiseasepatients AT guangpuli predictionandriskstratificationofcardiovasculardiseaseindiabetickidneydiseasepatients AT guangpuli predictionandriskstratificationofcardiovasculardiseaseindiabetickidneydiseasepatients AT jiayuduan predictionandriskstratificationofcardiovasculardiseaseindiabetickidneydiseasepatients AT jiayuduan predictionandriskstratificationofcardiovasculardiseaseindiabetickidneydiseasepatients AT jiayuduan predictionandriskstratificationofcardiovasculardiseaseindiabetickidneydiseasepatients AT jiayuduan predictionandriskstratificationofcardiovasculardiseaseindiabetickidneydiseasepatients AT jiayuduan predictionandriskstratificationofcardiovasculardiseaseindiabetickidneydiseasepatients AT jianchengdong predictionandriskstratificationofcardiovasculardiseaseindiabetickidneydiseasepatients AT zhangsuoliu predictionandriskstratificationofcardiovasculardiseaseindiabetickidneydiseasepatients AT zhangsuoliu predictionandriskstratificationofcardiovasculardiseaseindiabetickidneydiseasepatients AT zhangsuoliu predictionandriskstratificationofcardiovasculardiseaseindiabetickidneydiseasepatients AT zhangsuoliu predictionandriskstratificationofcardiovasculardiseaseindiabetickidneydiseasepatients |