A nomogram risk prediction model for no-reflow after primary percutaneous coronary intervention based on rapidly accessible patient data among patients with ST-segment elevation myocardial infarction and its relationship with prognosis - Liu, Yehong; Ye, Ting; Chen, Ke; Wu, Gangyong; Xia, Yang; Wang, Xiao; Zong, Gangjun (Frontiers in Cardiovascular Medicine, 2022)
Background: No-reflow occurred after primary percutaneous coronary intervention (PCI) in patients with ST segment elevation myocardial infarction (STEMI) can increase the incidence of major adverse cardiovascular events (MACE). This study is to constructed a nomogram prediction model that can be quickly obtained before operation to predict the risk of no-reflow after PCI in STEMI patients, and further explore its prognostic value in patients with STEMI. Methods: A total of 443 STEMI patients who underwent primary PCI from February 2018 to February 2021 were selected as the research objects. Collection of rapidly available clinical data on emergency admission of patients. The independent risk factors of no-reflow were analyzed by multivariate logistic regression model. Then, a nomogram for no-reflow was constructed and verified by bootstrap resampling. Using receiver operating characteristic (ROC) curve was drawn to evaluate the discrimination of the nomogram model, the calibration curve was employed to detect the concentricity between the model probability curve and ideal curve, and the clinical usefulness of our model was evaluated using the decision curve analysis (DCA). Results: The incidence of no-reflow was 18% in patients with STEMI. Killip class ≥ 2 on admission, Preoperative D-dimer, fibrinogen, and systemic immune–inflammation index (SII) level were independent risk factors for no-reflow. We developed a simple and fast-to-use prediction nomogram of no-reflow after PCI. This nomogram had a good discrimination with an area under the curve (AUC) = 0.716. This nomogram was further validated by bootstrapping for 1000 repetitions. The C-index of the bootstrap model was 0.706. This model showed a good fitting and calibration and positive net benefits in DCA. The Kaplan–Meier survival curve showed that patients with higher model scores had higher incidence of MACE. Multivariate Cox regression analysis revealed that higher model scores was an independent predictor of MACE (HR: 2.062, P=0.004). Conclusions: We have constructed a nomogram prediction model that can be quickly obtained before operation to predict the risk of no-reflow after PCI in STEMI patients. This novel nomogram would be useful for identifying individuals at higher risks of no-reflow in STEMI patients, and can guide the prognosis of STEMI patients.