Patients undergoing coronary artery bypass grafting (CABG) belong to the very high-risk group of atherosclerotic cardiovascular disease. Although CABG gets advantages in relieving symptoms and improving long-term outcomes, a significant risk of cardiovascular adverse events after surgery still exists and standardized secondary prevention is needed. Lipid management plays a critical role as a secondary preventive strategy in CABG. However, lipid management of CABG patients in real clinical setting is inadequate, including lack of standardized lipid-lowering strategy, low goal attainment rate, as well as poor long-term medication adherence. In recent years, a series of clinical trials have provided a lot of groundbreaking new evidence for lipid management in patients with cardiovascular diseases which offers new strategies together with objectives of lipid-lowering and comprehensive management for patients undergoing CABG. This article reviews the strategy and research progress of lipid management after CABG, aiming to provide objective reference for clinical treatment.
ObjectiveTo systematically review the models for predicting coronary artery disease (CAD) and demonstrate their predictive efficacy. MethodsPubMed, EMbase and China National Knowledge Internet were searched comprehensively by computer. We included studies which were designed to develop and validate predictive models of CAD. The studies published from inception to September 30, 2020 were searched. Two reviewers independently evaluated the studies according to the inclusion and exclusion criteria and extracted the baseline characteristics and metrics of model performance.ResultsA total of 30 studies were identified, and 19 diagnostic predictive models were for CAD. Seventeen models had external validation group with area under curve (AUC)>0.7. The AUC for the external validation of the traditional models, including Diamond-Forrester model, updated Diamond-Forrester model, Duke Clinical Score, CAD consortium clinical score, ranged from 0.49 to 0.87.ConclusionMost models have modest discriminative ability. The predictive efficacy of traditional models varies greatly among different populations.