Performance of a Hematological Scoring System in Predicting All-Cause Mortality in Patients with Acute Myocardial Infarction

ABSTRACT

Abstract Background: The presence of nucleated red blood cells (NRBCs) and increases in mean platelet volume (MPV) and neutrophil to lymphocyte ratio (NLR) in peripheral circulation are associated with poorer prognosis in patients with acute coronary disease. Objective: We developed a scoring system for in-hospital surveillance of all-cause mortality using hematological laboratory parameters in patients with acute myocardial infarction (AMI). Methods: Patients admitted for AMI were recruited in this prospective study. Exclusion criteria were age younger than 18 years, glucocorticoid therapy, cancer or hematological diseases and readmissions. NRBCs, MPV and NLR were measured during hospitalization. The scoring system was developed in three steps: first, the magnitude of the association of clinical and laboratory parameters with in-hospital mortality was measured by odds ratio (OR), second, a multivariate logistic regression model was conducted with all variables significantly (p < 0.05) associated with the outcome, and third, a β-coefficient was estimated by multivariate logistic regression with hematological parameters with a p < 0.05. Results: A total of 466 patients (mean age were 64.2 ± 12.8 years, 61.6% male) were included in this study. A hematological scoring system ranging from 0 to 49, where higher values were associated with higher risk of in-hospital death. The best performance was registered for a cut-off value of 26 with sensitivity of 89.1% and specificity of 67.2%, positive predictive value of 26.8% (95% CI: 0.204 - 0.332) and negative predictive value of 97.9% (95% CI: 0.962 - 0.996). The area under the curve for the scoring system was 0.868 (95% CI: 0.818 - 0.918). Conclusions: Here we propose a hematological scoring system for surveillance tool during hospitalization of patients with acute myocardial infarction. Based on total blood count parameters, the instrument can evaluate inflammation and hypoxemia due to in-hospital complications and, consequently, predict in-hospital mortality.