A scoring system combining CatLet score and clinical variables as a predictor of long-term prognosis in patients with chronic coronary syndrome after percutaneous coronary intervention.

Background: The Coronary Artery Tree Description and Lesion Evaluation (CatLet) angiographic scoring system is a newly developed vascular scoring for assessing the degree of coronary artery stenosis. It has unique advantages in reflecting coronary artery variability as compared to Synergy between Percutaneous Coronary Intervention with Taxus and Cardiac Surgery (SYNTAX) score. Preliminary studies support its superiority over SYNTAX in predicting clinical outcomes after percutaneous coronary intervention (PCI) in patients with chronic coronary syndrome (CCS). This study aimed to determine whether the CatLet score incorporating three clinical variables (CVs)-age, ejection fraction, and creatinine-is a better predictor of clinical outcomes in patients treated with PCI for CCS as compared to the CatLet score.

Methods: A total of 222 patients who were diagnosed with CCS, underwent coronary drug-eluting stent (DES) implantation, and had a calculable CatLet score were retrospectively selected from the Second Affiliated Hospital of Wannan Medical College in China between April 2019 and June 2020. The primary endpoint was major adverse cardiac events (MACEs), including myocardial infarction, recurrent angina, cardiac death, heart failure, and ischemia-driven revascularization, and was stratified according to CatLet score tertiles as follows: >0 and ≤23= CatLet low (n=72), 24-43= CatLet mid (n=76), and ≥44= CatLet top (n=74).

Results: The CatLet score predicted long-term prognosis, with a 4.5-year-follow-up and a median of 3.4 years. Of the 222 patients analyzed, the rates of MACEs, cardiac death, and reangina were 27.03%, 3.60%, and 18.02%, respectively. In the Kaplan-Meier analysis, as the tertiles of the CatLet score increased, so did the cumulative incidence event rates for all endpoints (log-rank test for trend P<0.05). The area under the curve (AUC) of the CatLet score was 0.73, 0.76, and 0.73 for MACEs, cardiac death, and reangina, respectively, while the AUCs for CV-adjusted CatLet score models were 0.78, 0.88, and 0.74, respectively. Alone or after adjustments for risk factors, the multivariable-adjusted hazard ratio/unit higher score was 6.22 [95% confidence interval (CI): 2.40-16.13] for MACEs, 4.84 (95% CI: 2.52-9.32) for cardiac death, and 8.59 (95% CI: 2.53-29.10) for heart failure.

Conclusions: As compared with CatLet score alone, the model incorporating the CatLet score and three CVs can provide superior prediction ability.