Analysis of Metabolic Risk Factors for Microcirculation Disorders Post-Percutaneous Coronary Intervention and Predictive Model Construction: A Study on Patients with Unstable Angina - Kangming Li; Shuang Liu; Jing Wang; Zhen Liu; Chunmei Qi (Reviews in Cardiovascular Medicine)
Background: This study aimed to analyze the metabolic risk factors for microcirculation disorders in patients with unstable angina (UA) after percutaneous coronary intervention (PCI), evaluating their predictive value for developing microcirculation disorders. Methods: A single-center retrospective study design was used, which included 553 patients with UA who underwent PCI. The angiographic microcirculatory resistance (AMR) index was calculated based on coronary angiography data. Patients were divided into two groups according to their post-PCI AMR values: a post-PCI AMR ≤2.50 group and a post-PCI AMR >2.50 group. Variables were included in the multivariate regression model through univariate regression and variance inflation factor (VIF) screening. Subgroup analyses were conducted by sex to further evaluate the predictive value of selected variables in the overall sample. The total sample was randomly split into a 7:3 ratio for the training and validation sets. A nomogram based on the training sets was then constructed to visualize these predictions. The discrimination and calibration of the prediction model were evaluated using the receiver operating characteristic (ROC) curve and calibration curve. Results: The post-PCI AMR >2.50 group had a higher percentage of females, increased incidence of diabetes, and elevated fasting blood glucose (FBG), glycated hemoglobin (HbA1c), triglyceride (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), very low-density lipoprotein cholesterol (VLDL-C), and lipoprotein(a) (Lp(a)) levels (p < 0.05). Logistic regression analysis identified HbA1c, TG, LDL-C, and Lp(a) as independent predictors of elevated AMR post-PCI after adjusting for confounders. Subgroup analysis confirmed no significant interaction between the model and sex (p > 0.05). A nomogram was constructed based on the training set, with the area under the curve (AUC) for the ROC of 0.824 in the training set and 0.746 in the validation set. The calibration curves showed a good fit (training set: p = 0.219; validation set: p = 0.258). Conclusions: HbA1c, TG, LDL-C, and Lp(a) levels are independent risk factors for microcirculation disorders in patients with UA post-PCI. The constructed nomogram provides good predictive accuracy.