The predictive value of the nomogram model of clinical risk factors for ischemia–reperfusion injury after primary percutaneous coronary intervention

Abstract

AbstractIschemia–reperfusion injury is a risk factor for poor clinical prognosis in patients with ST-segment elevation myocardial infarction (STEMI). However, due to the inability to predict the risk of its occurrence early, the effect of intervention measures is still being determined. This study intends to construct a nomogram prediction model and evaluate its value in predicting the risk of ischemia–reperfusion injury (IRI) after primary percutaneous coronary intervention (PCI). The clinical admission data of 386 STEMI patients who underwent primary PCI were retrospectively analyzed. According to the degree of ST-segment resolution (STR), the patients were divided into the STR < 70% group (n = 197) and the STR > 70 group (n = 187). The least absolute shrinkage and selection operator (LASSO) regression method was used to screen out IRI's admission-related clinical risk factors. The R language software was used to construct and verify the IRI nomogram prediction model based on the above indicators. The peak troponin level and the incidence of in-hospital death in the STR < 70% group were significantly higher than those in the STR > 70% group (p < 0.01), and the left ventricular ejection fraction was significantly lower than that in the STR > 70% group (p < 0.01). Combined with the results of LASSO regression and receiver operating characteristic curve comparison analysis, we constructed a six-dimensional nomogram predictive model: hypertension, anterior myocardial infarction, culprit vessel, proximal occlusion, C-reactive protein (CRP) > 3.85 mg/L, white blood cell count, neutrophil cell count, and lymphocyte count. The area under the nomogram's receiver operating characteristic (ROC) curve was 0.779. The clinical decision curve found that the nomogram had good clinical applicability when the occurrence probability of IRI was between 0.23 and 0.95. The nomogram prediction model constructed based on six clinical factors at admission has good prediction efficiency and clinical applicability regarding the risk of IRI after primary PCI in patients with acute myocardial infarction.