Predictive role of platelet indices on hospital admission and discharge in the long-term prognosis of acute coronary syndrome: Platelets do count

Type of funding sources: None.

Background/Introduction: Previous clinical studies have underlined the prognostic role of platelet indices in acute coronary syndrome (ACS). However, the effect of their dynamic change during hospitalization has not thoroughly been examined. Purpose: We aimed to investigate the association between platelet indices on admission, on discharge and their change during hospitalization and the long-term prognosis of patients with ACS. Methods: Data from a randomized controlled trial recruiting ACS patients were analyzed in a survival analysis. Platelet count (PC), mean platelet volume (MPV), platelet distribution width (PDW) and plateletcrit (PCT) on admission and on discharge dichotomized at the median value, as well as the change between admission and discharge of each variable dichotomized at the zero value. Primary endpoints were major adverse cardiac events (MACE), defined as occurrence of cardiovascular death, non-fatal myocardial infarction, non-fatal stroke or hospitalization for unstable angina, while secondary endpoints were all-cause mortality, all-cause hospitalization and major or minor bleeding events. Results: The study included 252 individuals who were followed-up for a median of 39 months. In the univariate Cox regression analysis, only PC at discharge (HR 2.20, 95% CI 1.10-4.40), MPV at discharge (HR 0.48, 95% CI 0.25-0.94) and PC reduction during the hospitalization (HR 0.25, 95% CI 0.13-0.51) predicted MACE. PC reduction correlated with a lower MACE occurrence (adjusted HR 0.27, 95%CI 0.14-0.54) and lower risk of all-cause hospitalization (adjusted HR 0.36, 95%CI 0.19-0.68) in the multivariable Cox-regression analysis. Conclusion: PC change during hospitalization can be a substantial independent predictor of long-term prognosis of ACS patients.

Baseline and admission characteristics

Abstract Figure. MACE univariate / multivariate analysis