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Prediction and Risk Stratification of Cardiovascular Disease in Diabetic Kidney Disease Patients
Jingjing Ren1,2,3,4,5, Dongwei Liu1,2,3,4, Guangpu Li1,2,3,4,5
1Department of Integrated Traditional and Western Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Insights
Diabetic kidney disease patients face high cardiovascular disease risk. A deep learning model accurately predicts this risk, enabling early intervention and better health management for DKD patients.
Area of Science:
- Nephrology
- Cardiology
- Artificial Intelligence in Medicine
Background:
- Diabetic kidney disease (DKD) significantly elevates cardiovascular disease (CVD) risk in patients.
- CVD is a leading cause of mortality among individuals with DKD.
- Early intervention and improved personal health management are crucial for DKD patients at high CVD risk.
Purpose of the Study:
- To develop a deep learning model for predicting CVD risk in DKD patients.
- To implement risk stratification for timely intervention and enhanced patient health management.
- To compare the predictive performance of the deep learning model against conventional methods.
Main Methods:
- A retrospective cohort study involving 890 DKD patients.
- Variable selection using Least Absolute Shrinkage and Selection Operator (LASSO) regression.
- Development of a deep learning survival model (DeepSurv) and comparison with Cox proportional hazards (CPH) and Random Survival Forest (RSF) models.
Main Results:
- Seven key variables (age, HDL, Hb, SBP, smoking, 24h urinary protein, TC) were identified for prediction.
- The DeepSurv model demonstrated superior performance with a C-index of 0.767 and AUC of 0.780.
- Risk stratification was achieved using ROC curve cut-off values, and an online risk monitoring tool was developed.
Conclusions:
- A deep learning model effectively predicts CVD risk in DKD patients using seven clinical variables.
- The model facilitates risk stratification, aiding in early intervention strategies.
- An accessible online calculator enhances the practical application of the predictive model for patient monitoring.
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.
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