Prognostic implication of CT-FFR based functional SYNTAX score in patients with de novo three-vessel disease

• Published in: European heart journal cardiovascular imaging 2020-11, Vol.22 (12), p.1434-1442
• Main Authors: Qiao, Hong Yan, Li, Jian Hua, Schoepf, U Joseph, Bayer, Richard R, Tinnefeld, Fiona C, Di Jiang, Meng, Yang, Fei, Guo, Bang Jun, Zhou, Chang Sheng, Ge, Ying Qian, Lu, Meng Jie, Jiang, Jian Wei, Lu, Guang Ming, Zhang, Long Jiang
• Format: Article
• Language: English
• ISSN: 2047-2404; 2047-2412
• DOI: 10.1093/ehjci/jeaa256

Abstract Aims This study was aimed at investigating whether a machine learning (ML)-based coronary computed tomographic angiography (CCTA) derived fractional flow reserve (CT-FFR) SYNTAX score (SS), ‘Functional SYNTAX score’ (FSSCTA), would predict clinical outcome in patients with three-vessel coronary artery disease (CAD). Methods and results The SS based on CCTA (SSCTA) and ICA (SSICA) were retrospectively collected in 227 consecutive patients with three-vessel CAD. FSSCTA was calculated by combining the anatomical data with functional data derived from a ML-based CT-FFR assessment. The ability of each score system to predict major adverse cardiac events (MACE) was compared. The difference between revascularization strategies directed by the anatomical SS and FSSCTA was also assessed. Two hundred and twenty-seven patients were divided into two groups according to the SSCTA cut-off value of 22. After determining FSSCTA for each patient, 22.9% of patients (52/227) were reclassified to a low-risk group (FSSCTA ≤ 22). In the low- vs. intermediate-to-high (>22) FSSCTA group, MACE occurred in 3.2% (4/125) vs. 34.3% (35/102), respectively (P