Analysis of risk factors for PCI no-reflow in coronary heart disease and construction of related prediction models.

Objective: To analyze the risk factors of percutaneous coronary intervention (PCI) no-reflow in patients with coronary heart disease (CHD) and construct a predictive nomogram model.

Methods: This retrospective study included 260 patients with CHD who underwent PCI in the Third Affiliated Hospital of Chongqing Medical University from January 2022 to December 2023. The subjects were divided into a PCI no-reflow group (n = 86) and normal reflow group (n = 174) based on thrombolysis in myocardial infarction (TIMI) blood flow grading. General data, PCI related data and laboratory indexes of patients were collected. Logistic regression was used to analyze the risk factors of no-reflow after PCI in CHD patients. Based on the significant variables from regression analysis, a nomogram prediction model was constructed by using R language. The accuracy of the model was evaluated by receiver operating characteristic (ROC) curve and calibration curve, and the decision curve was drawn to clarify the clinical utility of the model. Model performance metrics included area under the curve (AUC), accuracy, sensitivity and specificity.

Results: Multivariate logistic regression analysis showed that hypertension, cystatin C (Cys-C), hypersensitive c-reactive protein (hs-CRP) and platelet-to-lymphocyte ratio (PLR) were risk factors for no-reflow after PCI in CHD patients (OR > 1, P < 0.001), while ADAM metallopeptidase with thrombospondin type 1 motif 13 (ADAMTS-13) and lymphocyte (LYM) were protective factors (OR < 1, P < 0.001). The nomogram prediction model based on the above risk factors showed good predictive value. The AUC of the nomogram prediction model in the training set was 0.967 (95% CI: 0.946-0.989), with a specificity of 0.923 and a sensitivity of 0.908. In the validation set, the AUC was 0.894 (95% CI: 0.817-0.971), with a specificity of 0.807 and a sensitivity of 0.857. The calibration curve indicated good agreement between the predicted and actual probabilities, and the decision curve showed clinical benefit across a range of threshold probabilities in both the training and validation sets (0.0-0.99).

Conclusion: The risk factors affecting the occurrence of no-reflow after PCI in patients with CHD include hypertension, serum Cys-C, hs-CRP, PLR, ADAMTS-13 and LYM levels. The nomogram risk prediction model based on the above factors is valuable for identifying patients with high risk of no-reflow after PCI.