Prediction and Risk Stratification of Cardiovascular Disease in Diabetic Kidney Disease Patients

Jingjing Ren; Dongwei Liu; Guangpu Li; Jiayu Duan; Jiancheng Dong; Zhangsuo Liu

doi:10.3389/fcvm.2022.923549

Prediction and Risk Stratification of Cardiovascular Disease in Diabetic Kidney Disease Patients

Front Cardiovasc Med. 2022 Jun 24:9:923549. doi: 10.3389/fcvm.2022.923549. eCollection 2022.

Authors

Jingjing Ren^{1

2

3

4

5}, Dongwei Liu^{1

2

3

4}, Guangpu Li^{1

2

3

4

5}, Jiayu Duan^{1

2

3

4

5}, Jiancheng Dong⁵, Zhangsuo Liu^{1

2

3

4}

Affiliations

¹ Department of Integrated Traditional and Western Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
² Research Institute of Nephrology, Zhengzhou University, Zhengzhou, China.
³ Henan Province Research Center for Kidney Disease, Zhengzhou, China.
⁴ Key Laboratory of Precision Diagnosis and Treatment for Chronic Kidney Disease in Henan Province, Zhengzhou, China.
⁵ Clinical Research Center of Big-data, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.

Abstract

Background: Diabetic 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.

Methods: A 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).

Results: We 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.

Conclusion: A 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.

Keywords: cardiovascular disease; diabetic kidney disease; machine learning; prediction model; risk stratification.