Enhanced prediction of coronary heart disease risk in diabetic patients via Machine learning incorporating multiple inflammatory and metabolic indices: A study with Dual-Cohort validation.
Coronary heart disease (CHD) remains a leading cause of mortality worldwide, with individuals with diabetes mellitus (DM) facing markedly elevated risk due to complex inflammatory and metabolic disturbances. Emerging composite inflammatory and metabolic indices have demonstrated promise in enhancing cardiovascular risk stratification, yet research quantifying and comparing their respective predictive performance and assessing their relative contributions remains limited. This study aimed to develop a clinically applicable model for early CHD risk prediction in diabetic patients using novel composite inflammatory and metabolic indices.
This study utilized data from 3379 diabetic participants in the NHANES 1999-2018 survey cycles. Additionally, an independent external set of 902 patients from Qilu Hospital of Shandong University served as the validation cohort. Novel inflammatory and metabolic indices were calculated. Feature selection was performed via LASSO regression, the univariate logistic regression, and the Boruta algorithm. Nine machine learning (ML) models were developed using selected predictors. Model performance was evaluated using the receiver operating characteristic (ROC) curve, calibration curves, and decision curve analysis. SHapley Additive exPlanations (SHAP) values were used to interpret model predictions and identify key contributing features.
Five composite indices UHR (uric acid-to-HDL ratio), MHR (monocyte-to-HDL ratio), NPAR (neutrophil-to-albumin ratio), NLR (neutrophil-to-lymphocyte ratio), and AIP (atherogenic index of plasma) were identified as robust predictors of CHD. The random forest (RF) algorithm achieved the highest performance, with an AUC of 0.852 in the internal validation set and 0.713 in the external cohort. Calibration plots, Brier scores, and decision curve analysis further confirmed the RF model's predictive reliability and clinical utility. SHapley Additive exPlanations (SHAP) value analysis revealed that UHR, MHR, NLR, age, and hypertension were the key features driving CHD prediction.
We developed and externally validated an ML model incorporating five composite inflammatory and metabolic indices (UHR, MHR, NLR, NPAR, AIP), which demonstrated promising performance in predicting CHD risk in diabetic patients.
This study utilized data from 3379 diabetic participants in the NHANES 1999-2018 survey cycles. Additionally, an independent external set of 902 patients from Qilu Hospital of Shandong University served as the validation cohort. Novel inflammatory and metabolic indices were calculated. Feature selection was performed via LASSO regression, the univariate logistic regression, and the Boruta algorithm. Nine machine learning (ML) models were developed using selected predictors. Model performance was evaluated using the receiver operating characteristic (ROC) curve, calibration curves, and decision curve analysis. SHapley Additive exPlanations (SHAP) values were used to interpret model predictions and identify key contributing features.
Five composite indices UHR (uric acid-to-HDL ratio), MHR (monocyte-to-HDL ratio), NPAR (neutrophil-to-albumin ratio), NLR (neutrophil-to-lymphocyte ratio), and AIP (atherogenic index of plasma) were identified as robust predictors of CHD. The random forest (RF) algorithm achieved the highest performance, with an AUC of 0.852 in the internal validation set and 0.713 in the external cohort. Calibration plots, Brier scores, and decision curve analysis further confirmed the RF model's predictive reliability and clinical utility. SHapley Additive exPlanations (SHAP) value analysis revealed that UHR, MHR, NLR, age, and hypertension were the key features driving CHD prediction.
We developed and externally validated an ML model incorporating five composite inflammatory and metabolic indices (UHR, MHR, NLR, NPAR, AIP), which demonstrated promising performance in predicting CHD risk in diabetic patients.
Authors
Xie Xie, Ran Ran, Zhu Zhu, Xie Xie, Dong Dong, Wang Wang, Zhai Zhai, Qiao Qiao, Yang Yang, Chen Chen
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