Thrombosis and major bleeding risk after primary percutaneous coronary intervention among patients with multi-vessels coronary artery disease

Abstract

Abstract Background and aims The present study aimed to develop and validate separate risk prediction models for thrombosis events (TEs) and major bleeding (MB) in patients with multi-vessel coronary artery lesions who had undergone primary percutaneous coronary intervention (PCI). Methods and results TEs were defined as the composite of myocardial infarction recurrence or ischemic cerebrovascular events, whereas MB was defined as the occurrence of bleeding academic research consortium (BARC) 3 or 5 bleeding. The derivation and validation cohorts comprised 2976 patients who underwent primary PCI between January 2010 and June 2017. At a median follow-up of 3.07 years (1122 days), TEs and MB occurred in 167 and 98 patients, respectively. Independent predictors of TEs were older age, prior PCI, non-ST elevated MI (NSTEMI), and stent thrombosis (ST). Independent predictors of MB were triple therapy at discharge, coronary artery bifurcation lesions, lesion restenosis, target lesion of the left main coronary artery, and PTCA. In the derivation and validation cohorts, the areas under the curve were 0.817 and 0.820 for thrombosis and 0.886 and 0.976 for bleeding, respectively. In the derivation cohort, high thrombotic risk (n=755) was associated with a higher 3-year incidence of TEs, major adverse cardiovascular events (MACEs), and all-cause death, compared to low risk (n=1275) (p=0.0022, 0.019, 0.012, respectively). High bleeding risk (n=1675) was associated with a higher incidence of bleeding, MACEs, cardiac death, compared to low risk (n=355) (p<0.0001). Conclusion Simple risk scores can be useful in predicting the risks of ischemic and bleeding events after primary PCI, thereby stratifying thrombotic or MB risks and facilitating clinical decisions. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): This study was supported by the Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences (2016-I2M-1–009), National Natural Science Funds (number: 81970308) and the Fund of “Sanming” Project of Medicine in Shenzhen (number: SZSM201911017). Figure 2Figure 3