An Easy-to-Use Nomogram Based on SII and SIRI to Predict in-Hospital Mortality Risk in Elderly Patients with Acute Myocardial Infarction

AIM: Inflammatory response is closely associated with poor prognosis in elderly patients with acute myocardial infarction (AMI). The aim of this study was to develop an easy-to-use predictive model based on medical history data at admission, systemic immune inflammatory index (SII), and systemic inflammatory response index (SIRI) to predict the risk of in-hospital mortality in elderly patients with AMI. METHODS: We enrolled 1550 elderly AMI patients (aged ≥60 years) with complete medical history data and randomized them 5:5 to the training and validation cohorts. Univariate and multivariate logistic regression analyses were used to screen risk factors associated with outcome events (in-hospital death) and to establish a nomogram. The discrimination, calibration, and clinical application value of nomogram were evaluated based on receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA), respectively. RESULTS: The results of multivariate logistic regression showed that age, body mass index (BMI), previous stroke, diabetes, SII, and SIRI were associated with in-hospital death, and these indicators will be included in the final prediction model, which can be obtained by asking the patient’s medical history and blood routine examination in the early stage of admission and can improve the utilization rate of the prediction model. The areas under the ROC curve for the training and validation cohorts nomogram were 0.824 (95% CI 0.796 to 0.851) and 0.809 (95% CI 0.780 to 0.836), respectively. Calibration curves and DCA showed that nomogram could better predict the risk of in-hospital mortality in elderly patients with AMI. CONCLUSION: The nomogram constructed by combining SII, SIRI, and partial medical history data (age, BMI, previous stroke, and diabetes) at admission has a good predictive effect on the risk of in-hospital death in elderly patients with AMI.