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Machine Learning Models for Identifying the Risk of Chronic Kidney Disease in Patients with Coronary Heart Disease: A
Ting He1, Jinbo Zhao2, Ling Hou2
1Department of Central Hospital of Tujia and Miao Autonomous Prefecture, Hubei University of Medicine, Shiyan, Hubei, 442000, People's Republic of China.
Insights
Machine learning effectively identifies chronic kidney disease (CKD) in coronary heart disease (CHD) patients. The XGBoost model, using routine data and highlighting uric acid to albumin ratio (UAR), aids early detection and intervention.
Area of Science:
- Cardiology
- Nephrology
- Artificial Intelligence in Medicine
Background:
- Coronary heart disease (CHD) frequently co-exists with chronic kidney disease (CKD).
- Effective tools for identifying undiagnosed CKD in CHD patients are currently lacking.
- This study addresses the need for predictive models in this high-risk population.
Purpose of the Study:
- To develop and validate machine learning (ML) models for detecting undiagnosed CKD in patients with CHD.
- To identify key clinical and laboratory predictors of CKD in this cohort.
- To assess the clinical utility of the developed ML model.
Main Methods:
- Retrospective analysis of 1786 CHD patients undergoing coronary intervention.
- Feature selection using Lasso and multifactor logistic regression.
- Development of five ML models: logistic regression, SVM, random forest, GBM, and XGBoost.
- Model evaluation using AUC, calibration, and decision curves.
Main Results:
- The prevalence of CKD among CHD patients was 21.8%.
- Key predictors identified include male sex, advanced age, hypertension, diabetes, atrial fibrillation, high Gensini score, low hemoglobin, low plateletcrit, high triglycerides, high lipoprotein(a), hyperkalemia, high uric acid to albumin ratio (UAR), high systemic inflammation response index (SIRI), low lymphocyte to monocyte ratio, and high ApoB/ApoA1 ratio.
- The XGBoost model demonstrated optimal performance (AUC=0.909) in the validation set.
- SHapley Additive explanation identified UAR, hypertension, Gensini score, age, and SIRI as the most influential features.
Conclusions:
- The XGBoost model effectively identifies CKD risk in CHD patients using routine clinical data.
- Uric acid to albumin ratio (UAR) emerged as a novel and strong predictor of CKD in this population.
- The model's clinical utility, confirmed by decision curve analysis, supports its use for guiding monitoring and early intervention strategies.
Purpose:
Coronary heart disease (CHD) has a significant co-morbid association with chronic kidney disease (CKD), but identification tools for the risk of concomitant CKD in patients with CHD are still lacking. The purpose of this research was to construct machine learning (ML) models for identifying undetected CKD in CHD patients.
Methods:
1786 CHD patients undergoing coronary intervention were retrospectively included. Lasso regression and multifactor logistic regression were used to screen feature variables. Five ML models, ie, logistic regression (LR), support vector machine (SVM), random forest (RF), gradient boosting machine (GBM), and extreme gradient boosting (XGBoost), were constructed. Participants were divided into the training set and validation set in a 7:3 ratio. The evaluation metrics included the area under the curve, calibration curve, and decision curve.
Results:
Totally, 1786 CHD patients were enrolled and split into training (70%) and validation (30%) sets. The prevalence of CKD was 21.8% (390/1786). Multivariate logistic regression analysis showed that men, advanced age, hypertension, diabetes mellitus, history of atrial fibrillation (AF), high Gensini, low hemoglobin, low plateletcrit (PCT), high triglycerides (TG), high lipoprotein(a) (Lp(a)), hyperkalemia, high uric acid to albumin ratio (UAR), high systemic inflammation response index (SIRI), low lymphocyte to monocyte ratio (LMR), and high apolipoprotein B to apolipoprotein A1 (ApoB/ApoA1) ratio were the key clinical and laboratory test indicators of CKD. The XGBoost model performed optimally in the validation set (AUC=0.909, 95% CI 0.881 -0.937). SHapley Additive explanation analysis identified UAR, hypertension, Gensini score, age, and SIRI as the top 5 key features.
Conclusion:
The XGBoost model constructed on routine clinical data was effective in identifying CKD risk in CHD patients, with UAR as a novel strong predictor. Decision curve analysis confirmed the clinical utility of the model, indicating that it may be used to guide decisions for enhanced monitoring and early intervention over a wide range of risk thresholds.
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