Predictive value of SYNTAX score on in-hospital outcomes after Percutaneous Coronary Intervention (PCI)

Abstract

Background: The SYNTAX (Synergy between PCI with Taxus and Cardiac Surgery) score has emerged as an anatomical-based tool that quantitatively determines the coronary vasculature due to 9 criteria such as number, location, complexity, and functional impact of angiographically obstructive lesions. Because of the fact that the same study of this scoring system has not been performed in Iran, we aimed to do this study to predict the value of SYNTAX score on in-hospital outcomes after Percutaneous Coronary Intervention (PCI) in patients referred to Heshmat Hospital, Rasht, Iran. \n \nMethods: The present cross-sectional study conducted at Heshmat Heart Center, Rasht, Iran. Patients admitted for elective or primary PCI of coronary arteries due to the acute coronary syndrome, ST segment elevation myocardial infarction and chest pain. Each patient’s medical data such as clinical characteristics, procedures and adverse events collected from data bank and medical records. The SYNTAX score was calculated using SYNTAX score version 2.58 (SYNTAX SCORE I, www.syntaxscore.com). All in-hospital outcomes were entered in SPSS software version 23 and analysis was done. \n \nResults: In this study 431 patients underwent PCI. The average age of the samples was 57.10 ± 10.67 years (range 23- 85 years). However, the average SYNTAX score in the samples was 15.93 ± 5.53 points. The mean SYNTAX score is significantly higher in positive cases of CVA after PCI (p=0.001), hypotension (p=0.001), arrhythmia (p=0.001), in-hospital death (p=0.002), unsuccessful PCI (p=0.001), CIN (p=0.001) and total adverse event incidence (p=0.001) but not with vascular disorders (p=0.769). ROC curve for predicting outcomes of PCI in the patients based on SYNTAX score, showed the total cutoff point of it was 19.5 and under curve area was calculated 0.79 (CI=0.716-0.865, p<0.0001). \n \nConclusion: It seems that SYNTAX score utilization for samples with anatomic complexity, can predict the in-hospital outcomes in our setting.