Red cell distribution width as a novel prognosticator for in-hospital mortality in patients with acute myocardial infarction

Aim: To investigate the association between red blood cell distribution width (RDW) and in-hospital mortality; to develop a clinical risk model of in-hospital mortality in patients with acute myocardial infarction (AMI). Materials and methods. The prospective observational study included 577AMI patients undergoing coronary angiography (CAG) < 24 h after symptom onset and was divided according RDW median. The association between RDW, clinical parameters and inhospital mortality was evaluated using logistic regression and receiver operating characteristic (ROC) curve analysis. A prognostic model was developed by using Bayesian approach and logistic regression analysis with identifying predictors for mortality. Results. The median age of patients was 65 (interquartile range [IQR]: 56–74) years. 60.7% were male, 47.1% with STelevation. The in-hospital mortality rate was 5.4% ( n = 31). Median RDW was 14.2% (IQR 13.5–15.0%). In univariate analysis, RDW was a significant risk predictor of in-hospital mortality (odds ratio [OR] 1.27, 95% confidence intervals [CI] 1.07–1.50, p = 0.005). The area under the ROC curve [AUC] was 0.649 (95% CI: 0.540–0.758, cut-off value 15.11%). In Bayesian multivariate logistic model, age (OR 1.10, 95% CI 1.06–1.14, p < 0.001), ST-elevation (OR 3.22, 95% CI 1.41– 7.35, p = 0.006) RDW (OR 1.26, 95% CI 1.04–1.53, p = 0.021), were identified as risk factors for in-hospital mortality. Overall, the model showed excellent discrimination in predicting in-hospital mortality (AUC = 0.832, 95% CI: 0.779– 0.885, p < 0.001, sensitivity: 87.1%, specificity: 72.2%) and with good calibration (Hosmer-Lemeshow test, p = 0.632). Conclusions. Elevated RDW value was independently associated with an increased risk of in-hospital mortality in AMI patients undergoing CAG. The model, including age, ST-elevation and RDW for prediction of in-hospital mortality demonstrated high prognostic potential, enabling the identification of patients at high-risk of adverse outcome.