4.6 Article

The Usefulness of C-Reactive Protein to Albumin Ratio in the Prediction of Adverse Cardiovascular Events in Coronary Chronic Total Occlusion Undergoing Percutaneous Coronary Intervention

期刊

出版社

FRONTIERS MEDIA SA
DOI: 10.3389/fcvm.2021.731261

关键词

c-reactive protein to albumin ratio; percutaneous coronary intervention; chronic coronary total occlusion; adverse cardiovascular events; prognostic indicator

资金

  1. National Key R&D Program of China [2019YFA0802300]
  2. National Natural Science Foundation of China [81822005, 91639301, 81500219]

向作者/读者索取更多资源

Inflammation and nutrition significantly impact prognosis in patients with CTO undergoing PCI, with CAR values improving predictive value for all-cause death and cardiovascular death. Adding CAR values to traditional prediction models enhances prognosis prediction capacity.
Inflammation and nutrition as main factors can affect the prognosis of patients with chronic total coronary occlusion (CTO) undergoing percutaneous coronary intervention (PCI). The C-reactive protein to albumin ratio (CAR) can clarify the inflammation and nutrition status, which are highly related to clinical outcomes. This study aims to investigate the association between CAR and adverse cardiovascular events in patients with CTO undergoing PCI. For this study, 664 patients were divided into three groups based on the tertiles of CAR. The primary endpoint was all-cause mortality and the secondary endpoint was major adverse cardiovascular events (MACE). Over a median follow-up of 33.7 months, the primary endpoint occurred in 64 patients (9.6%) and the secondary endpoint occurred in 170 patients (25.6%). The patients with higher CAR represented a worse prognosis with all-cause death and cardiovascular death after the adjustment for the baseline risk factors. Adding the CAR values raised the predictive value for the incidence of the all-cause death and cardiovascular death but not MACE. The capacity of prognosis prediction was improved after the addition of the CAR value to the traditional prediction model.

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