CatLet score and Clinical CatLet score as predictors of long-term outcomes for patients with acute myocardial infarction undergoing delayed percutaneous coronary intervention. (14th October 2021)

• Record Type: Journal Article
• Authors: Xu, J P; Sun, B C; He, Y M
• Is Part Of: European heart journal. Volume 42(2021)Supplement 1
• Publication Date: 2021-10-14
• Subjects: Outcome; Cardiology -- Periodicals; Heart -- Diseases -- Periodicals; 616.12005
• DOI: 10.1093/eurheartj/ehab724.2120
• Languages: English
• ISSNs: 0195-668X

Abstract

Abstract: Background: We have recently developed a Coronary Artery Tree description and Lesion EvaluaTion (CatLet) angiographic scoring system based on the 17 myocardial segment model, law of competitive blood supply, and law of flow conservation. This novel angiographic scoring system can be utilized to account for the variable coronary anatomy, the severity and complexity of coronary artery lesions, and to standardize the collection of angiographic data. Our preliminary study demonstrated that the CatLet score better predicted clinical outcomes than the SYNTAX score for patients with acute myocardial infarction (AMI) undergoing primary percutaneous coronary intervention (PCI). The current study aimed at assessing whether the CatLet score (CS) and the Clinical CatLet score (CCS) predicted clinical outcomes for AMI patients undergoing delayed PCI. Methods: The CS was calculated in 1018 consecutively prospectively enrolled AMI patients. Primary end point was major adverse cardiac events (MACEs), a composite of myocardial infarction, cardiac death, and ischemia-driven revascularization. CCS was calculated multiplying the CS by the ACEF score constructed using age, creatinine, and LVEF. Tertile partitioning of CS and CCS were as follows: CSlow ≤12, CSmid 13–18, and CStop ≥19; CCSlow ≤13, CCSmid 14–24, and CCStop ≥25, respectively. Cox regression analysis was employed to identify the associations between predictors and clinical outcomes. Results: Four-year MACE rates were significantly higher with CStop compared with CSlow [4.82 (3.39–6.85), P<0.001], which was also the case for all-cause death, cardiac death, myocardial infarction, and ischemia-driven revascularization; for all these endpoints, there were a significant trend (P<0.001) for higher event rates with the increasing CS tertiles as shown in Figure 1. Stratifying outcomes across CCS tertiles resulted in similar results for the comparisons between CCStop and CCSlow. For MACEs, CCS had a significantly increased C-index [(C-index (95% CI), 0.725 (0.692–0.759) vs. 0.693 (0.659–0.727), P=0.001] compared with CS, which was also the case for all-cause death, cardiac death, and myocardial infarction. However, for ischemia-driven revascularization, CCS had a slightly decreased C-index [0.655 (0.609–0.701) vs. 0.670 (0.625–0.716), P=0.235] compared with CS, without a significant difference. In terms of calibration, CCS was comparable to CS for MACEs, all-cause death, cardiac death, and myocardial infarction. However, calibration for ischemia-driven revascularization worsened for CCS compared with CS (χ 2 =22.07, P=0.004 vs. χ 2 =7.79, P=0.454). Both scores remained to be independent predictors of clinical outcomes (in separate models) after adjustment for a broad spectrum of risk factors. Conclusions: CS and to a greater extent CCS were able to risk-stratify long-term outcomes in AMI patients undergoing delayed PCI. FUNDunding Acknowledgement: Type of funding sources: Foundation. Main funding source(s): 2020YFC2004705 & SYS2019040