Correlation Model Construction and Validation of Coronary Artery Stenosis Degree Based on TCM Syndromes and Coronary Heart Disease-Related Parameters

Abstract

Objective: To investigate the relationship between coronary heart disease (CHD)-related indicators and the degree of coronary artery stenosis in patients based on traditional Chinese medicine (TCM) syndromes, and to provide a reference for clinicians to predict the severity of coronary artery lesions in CHD patients. Methods: Clinical data of 277 hospitalized patients diagnosed with CHD who underwent coronary angiography (CAG) in the Department of Cardiology, Shaanxi Provincial Hospital of Traditional Chinese Medicine, from June 2023 to June 2025 were collected, including 163 males and 114 females. The patients were divided into the severe CHD group (Gensini score ≥32 points, n=69) and the mild CHD group (Gensini score <32 points, n=208) according to the Gensini score. General information including gender, age, underlying disease history, smoking history, and multiple laboratory indicators of the patients were collected. Univariate analysis was first used to screen the influencing factors of CHD severity; the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm was applied to correct factor collinearity and screen the optimal matching factors. These factors were then included in the multivariate forward stepwise logistic regression analysis to identify independent influencing factors and draw a nomogram. Finally, the receiver operating characteristic (ROC) curve, calibration curve, and clinical decision curve were used to verify the discrimination, accuracy, and clinical application efficacy of the model. Results: 1) Univariate analysis showed that there were statistically significant differences in the levels of lymphocyte (LYM), cardiac troponin (cTn), high-density lipoprotein (HDL), and lipoprotein a (Lp(a)) between the two groups (P < 0.05). 2) LASSO regression and multivariate logistic regression identified 9 independent influencing factors for coronary artery stenosis, including smoking, diabetes mellitus, ln(cTn), HDL, Lp(a), uric acid, cystatin C, blood stasis syndrome, and neutrophil-to-lymphocyte ratio (NLR) (P < 0.05). Among them, HDL was an independent negatively correlated factor, and the others were positively correlated factors. 3) The ROC curve showed that the area under the curve (AUC) of the model group was 0.760 (95% CI: 0.713, 0.806) and that of the validation group was 0.745 (95% CI: 0.714, 0.776), indicating good discrimination of the model. 4) The clinical decision curve and clinical impact curve showed that the model achieved the maximum clinical net benefit when the threshold probability was 0.12-0.76 in the model group and 0.18-0.57 in the validation group. Conclusion: 1) Smoking, diabetes mellitus, ln(cTn), HDL, Lp(a), uric acid, cystatin C, blood stasis syndrome, and NLR are independent risk factors for the degree of coronary artery stenosis in CHD patients. 2) Combined detection of serum levels of ln(cTn), HDL, Lp(a), uric acid, and cystatin C has reference value for predicting the degree of coronary artery stenosis in CHD patients.