Abstract

ObjectivesTo explore the association between left atrial (LA) strain and the GRACE score in patients with acute coronary syndrome (ACS) and to investigate the utility of LA function in predicting short-term adverse cardiovascular events post ACS. MethodsThis retrospective study included ACS patients who underwent coronary angiography (CAG) in two independent cohorts from October 2020 to July 2022. The patients were classified into low-intermediate risk group and high-risk group based on the GRACE score. All participants underwent a transthoracic echocardiogram, with LA strain analysis before CAG. Correlation analysis was used to determine the relationship between LA strain and the GRACE score. The predictive value of LA strain was examined utilizing the area under the curve (AUC). Participants were followed for 10.5 ± 2.9 months for the primary endpoint of major adverse cardiovascular events (MACE). ResultsA total of 229 patients were included in this study, including 196 in the primary group and 33 in the validation group. Spearman’s correlation analysis showed there was a moderate negative correlation between the GRACE and left atrial reservoir strain (LASr) in both the primary (r = −0.63, P < 0.001) and validation (r = −0.73, P < 0.001) cohorts. Receiver operator characteristic (ROC) curve analysis showed that the AUC of LASr for prediction of the high-risk group was 0.86. Taking LASr 19.6% as the cut-off value, the sensitivity and specificity were 0.71 and 0.92, respectively. The cut-off value of 19.6% remains good at identifying high-risk group in the validation group (AUC = 0.87, sensitivity: 77.8%, specificity: 95.8%). Furthermore, 49 patients reached the endpoint in the primary cohort during the follow-up. On multivariable regression analysis, LASr (P = 0.03) was the independent echocardiographic predictor for the primary endpoint, rather than left atrial volume index (LAVI). ConclusionsLASr can identify high-risk patients with ACS as defined by the GRACE score and may be superior to Max LAVI in predicting incidents of MACE in the short-term following ACS.

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