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Remnant Cholesterol Inflammatory Index for Predicting Heart Failure Risk in Patients with Coronary Artery Disease and
Chaozhong Luo1, Juan Du1, Changjiang Zhang1
1Department of Cardiology, Minda Hospital of Hubei Minzu University, Enshi, Hubei, People's Republic of China.
Insights
The remnant cholesterol inflammatory index (RCII) predicts heart failure (HF) in patients with coronary artery disease (CAD) and type 2 diabetes mellitus (T2DM). Elevated RCII levels are associated with a significantly higher risk of developing HF in this high-risk population.
Area of Science:
- Cardiology
- Endocrinology
- Biomarkers
Background:
- Patients with coronary artery disease (CAD) and type 2 diabetes mellitus (T2DM) face a substantially elevated risk of heart failure (HF).
- Early identification of individuals at high risk for HF within this population remains a significant clinical challenge.
- The remnant cholesterol inflammatory index (RCII) has emerged as a potential predictor of cardiovascular events, but its specific utility in CAD and T2DM patients requires further investigation.
Purpose of the Study:
- To evaluate the efficacy of the remnant cholesterol inflammatory index (RCII) as a predictor of heart failure (HF) in patients with coronary artery disease (CAD) and type 2 diabetes mellitus (T2DM).
- To develop and validate machine learning models for predicting HF risk in this cohort, incorporating RCII and other key clinical factors.
Main Methods:
- Retrospective analysis of clinical data from 1181 patients with CAD and T2DM.
- Application of the Boruta algorithm for feature selection to identify key predictors of HF.
- Development and comparison of five machine learning models: logistic regression, decision tree, elastic net, LASSO, and naïve Bayes.
- Assessment of model performance using receiver operating characteristic (ROC) curves, area under the curve (AUC), calibration plots, Brier scores, and SHAP analysis.
Main Results:
- A total of 73 patients developed HF during the study period, with significantly higher median RCII levels observed in this group.
- Logistic regression model achieved the highest predictive performance, with an AUC of 0.88 in the training set and 0.85 in the testing set.
- SHAP analysis identified elevated RCII, poor nutritional status, and smoking as primary contributors to HF development, confirming RCII's positive association with HF risk.
Conclusions:
- The remnant cholesterol inflammatory index (RCII) serves as a valuable and independent predictor of heart failure (HF) in patients concurrently diagnosed with coronary artery disease (CAD) and type 2 diabetes mellitus (T2DM).
- Elevated RCII levels are strongly correlated with an increased risk of HF incidence in this vulnerable patient population, highlighting its potential clinical utility for risk stratification.
Background:
Patients with coronary artery disease (CAD) and type 2 diabetes mellitus (T2DM) are at markedly increased risk of developing heart failure (HF), yet early identification of high-risk individuals remains challenging. The remnant cholesterol inflammatory index (RCII) has been proposed as a predictor of adverse cardiovascular outcomes, but its role in patients with CAD and T2DM has not been fully elucidated.
Methods:
We retrospectively analyzed clinical data from patients treated at our center. Demographic characteristics, comorbidities, medication use, and laboratory parameters were collected. Key features were selected using the Boruta algorithm, and five machine learning models-logistic regression (Logistic), decision tree (DT), elastic net regression (ENet), LASSO regression, and naïve Bayes (NB)-were constructed. Discrimination was assessed by receiver operating characteristic (ROC) curves and area under the curve (AUC), calibration by calibration plots and Brier scores, and interpretability by SHAP analysis.
Results:
Among 1181 enrolled patients, 73 developed HF. Median RCII levels were significantly higher in the HF group. Boruta feature selection identified 13 key predictors for model development. Logistic regression demonstrated the best performance, achieving AUCs of 0.88 in the training set and 0.85 in the testing set, with overall accuracy of 0.87 and F1-score of 0.79 in the testing cohort. SHAP analysis revealed that elevated RCII, poor nutritional status, and smoking were major contributors to HF occurrence, with RCII showing a positive association with HF risk.
Conclusion:
RCII is a valuable predictor of HF in patients with CAD and T2DM. Higher RCII levels are closely linked to an increased risk of HF.
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