基于MSCT检测EAT联合LAAEF预测冠心病患者并发房颤的效能探讨

目的 探讨基于多层螺旋CT(MSCT)检测心外膜脂肪（EAT）联合左心耳射血分数（LAAEF）预测冠心病患者并发房颤的效能。方法 选取2019年4月～2020年12月我院收治的189例冠心病患者，根据6个月内是否发生房颤分为两组：房颤组（n=47）和无房颤组（n=142），比较两组基线资料、EAT体积、LAAEF，采用多因素Logistic回归方程分析冠心病并发房颤的相关影响因素，采用受试者工作特征（ROC）曲线及ROC下面积（AUC）分析EAT体积、LAAEF及联合预测房颤的价值。结果 房颤组患者的高血压、Gensini评分、冠心病类型与无房颤组比较，差异有统计学意义（P<0.05或P<0.01）；房颤组患者的EAT体积高于无房颤组，LAAEF低于无房颤组（均P<0.01）；将高血压、Gensini评分、冠心病类型控制后，EAT体积、LAAEF仍是发生房颤的相关影响因素（P<0.01）;EAT体积预测房颤的AUC为0.726,LAAEF预测房颤的AUC为0.777,EAT体积联合LAAEF预测房颤的AUC为0.867；持续性房颤患者EAT体积高于阵发性房颤患者...

Efficacy of epicardial fat combined with left atrial appendage ejection fraction based on multi-slice spiral CT in predicting coronary heart disease patients with atrial fibrillation

AIM To investigate the efficacy of detecting epicardial fat (EAT) combined with left atrial appendage ejection fraction (LAAEF) based on multi-slice spiral CT (MSCT) in predicting coronary heart disease patients with atrial fibrillation. METHODS One hundred and eighty-nine patients with coronary heart disease admitted to our hospital from April 2019 to December 2020 were selected and divided into atrial fibrillation group (n=47) and no atrial fibrillation group (n=142) according to whether atrial fibrillation occurred within 6 months. The baseline data, EAT volume and LAAEF of the two groups were compared. Multivariate logistic regression equation was used to analyze the relevant influencing factors of coronary heart disease complicated by atrial fibrillation and receiver operating characteristic curve (ROC) and the area under the ROC (AUC) were used to analyze the EAT volume, the value of LAAEF and their combined use in prediction of atrial fibrillation. RESULTS There were significant differences in hypertension, Gensini scores and coronary heart disease types between the atrial fibrillation group and the non-atrial fibrillation group (P<0.05 or P<0.01). The atrial fibrillation group had higher EAT volume and lower LAAEF than the non-atrial fibrillation group (all P<0.01). After controlling for hypertension, Gensini score and coronary heart disease type, EAT volume and LAAEF were still related factors influencing the occurrence of atrial fibrillation (P<0.01). EAT volume in predicting the AUC of atrial fibrillation was 0.726, LAAEF was 0.777, but EAT volume combined with LAAEF in predicting the AUC of atrial fibrillation was 0.867. The EAT volume of patients with persistent atrial fibrillation was higher than that of patients with paroxysmal atrial fibrillation, and their LAAEF was lower than that of patients with paroxysmal atrial fibrillation (P<0.01). CONCLUSION The use of MSCT to detect coronary heart disease EAT volume and LAAEF predicts the risk of patients with atrial fibrillation, help distinguish different types of atrial fibrillation and provides an objective basis for clinical diagnosis, treatment and intervention.