A single-centre analysis of in-stent restenosis after left main stenting

Abstract Funding Acknowledgements Type of funding sources: None. Background Percutaneous coronary interventions (PCI) can be the standard of care for left main (LM) coronary artery disease. In-stent restenosis (ISR) is a rare complication and is associated with patient- and procedure-specific aspects. The aim of our analysis is to evaluate the incidence and the predictors of ISR after left main PCI. Methods From June 2018 till June 2019, 156 LM procedures were performed in our centre with second-generation drug-eluting stents (DES). Our registry includes a wide spectrum of patients, with stable coronary artery disease and with acute coronary syndrome, including STEMI, unprotected and protected LM, high and low SYNTAX scores, with or without true bifurcation disease (engagement). Results In 22.3 percent of the patients, previous percutaneous or surgical revascularization was found. In 78 percent of cases, the recommended care method was provisional stenting, and in 68 percent, IVUS support was used. In hospital discharge the 1st, 6th, 12th month, and each year afterwards, patients were prospectively followed up for MACE (defined as death, stroke, symptom triggered repeat revascularization). According to our data, the only predictor of ISR at multivariate analysis turned out to be type II diabetes mellitus (OR = 3.84; 95% CI: 1.18-13.65, p = 0.007), while sex, age, prior revascularisation and the clinical presentation at admission (stable or acute), stent technique and complex anatomy were not. All patients with significant ISR were retreated successfully; 38 (24%) of them interventionally and in 3 (1.92%) of the cases, surgically. Conclusions According to the results from our registry, composed of all-comer patients, restenosis after PCI of LM with second-generation DES is an infrequent event. The most common location of IRS is at the ostium of the circumflex artery and distal left main. Type II diabetes mellitus is the only predictor of ISR at multivariate analysis.