Deceleration Capacity Improves Prognostic Accuracy of Relative Increase and Final Coronary Physiology in Patients With Non-ST-Elevation Acute Coronary Syndrome

BackgroundBoth coronary physiology and deceleration capacity (DC) showed prognostic efficacy for patients with acute coronary syndrome (ACS). This retrospective cohort study was performed to evaluate the prognostic implication of DC combined with the relative increase and final coronary physiology as detected by quantitative flow ratio (QFR) for patients with non-ST-elevation ACS (NSTE-ACS) who underwent complete and successful percutaneous coronary intervention (PCI). MethodsPatients with NSTE-ACS who underwent PCI with pre- and post-procedural QFR in our department between January 2018 and November 2019 were included. The 24-hour deceleration capacity (DC 24h) was obtained via Holter monitoring. The incidence of major adverse cardiac and cerebrovascular events (MACCEs) during follow up was defined as the primary outcome. The optimal cutoffs of the relative increase, final QFR, and DC 24h for prediction of MACCEs were determined via receiver operating characteristic (ROC) analysis and the predictive efficacies were evaluated with multivariate Cox regression analysis. ResultsOverall, 240 patients were included. During a mean follow up of 21.3 months, 31 patients had MACCEs. Results of multivariate Cox regression analyses showed that a higher post-PCI QFR [adjusted hazard ratio (HR): 0.318; 95% confidence interval (CI): 0.129-0.780], a higher relative QFR increase (HR: 0.161; 95% CI: 0.066-0.391], and a higher DC (HR: 0.306; 95% CI: 0.134-0.701) were all independent predictors of lower risk of MACCEs. Subsequently, incorporating low DC (<= 2.42) into the risk predicting model with clinical variables, the predictive efficacies of low relative QRS increase (<= 23%) and low post-PCI QFR (<= 0.88) for MACCEs were both significantly improved. ConclusionsThe DC combined with relative increase and final coronary physiology may improve the predictive efficacy of existing models based on clinical variables for MACCEs in NSTE-ACS patients who underwent complete and successful PCI.