背景:右心室(RV)功能在评估心功能中很重要,但使用标准经胸超声心动图(TTE)进行评估仍然具有挑战性.心脏磁共振成像(CMR)被认为是金标准。美国超声心动图学会建议通过TTE替代测量RV功能和RV射血分数(RVEF),包括分数面积变化(FAC),自由壁应变(FWS),和三尖瓣环平面收缩偏移(TAPSE),但它们需要获取和量化方面的技术专长。
方法:本研究的目的是评估敏感性,特异性,以及FAC的阳性和阴性预测值,FWS,和TAPSE使用快速推导,来自单平面经胸心尖四腔超声心动图的新型人工智能(AI)软件(LVivoRV),与CMR衍生的RVEF相比,无超声增强剂的RV聚焦视图可检测异常RV功能。RV功能障碍定义为在CMR上RVEF<50%和RVEF<40%。
结果:TTE和CMR在中位数10天内进行(四分位间距,2-32天),在没有间隔程序或药物干预的情况下,在225例连续患者中彼此进行。当所有三个AI衍生参数(FAC,FWS,TAPSE)异常分别为91%和96%,而专家医生的阅读量分别为91%和97%。特异性和阳性预测值(50%和32%)低于专家医师阅读的超声心动图(82%和56%)。
结论:AI衍生的FAC测量,FWS,TAPSE对排除显著RV功能障碍(CMRRVEF<40%)有极好的敏感性和阴性预测值,与专家医师读者相比,但特异性较低。因此,AI,使用美国超声心动图学会指南,可以作为快速床边评估的有用筛查工具,以排除严重的RV功能障碍。
Right ventricular (RV) function is important in the evaluation of cardiac function, but its assessment using standard transthoracic echocardiography (TTE) remains challenging. Cardiac magnetic resonance imaging (CMR) is considered the gold standard. The American Society of Echocardiography recommends surrogate measures of RV function and RV ejection fraction (RVEF) by TTE, including fractional area change (FAC), free wall strain (FWS), and tricuspid annular planar systolic excursion (TAPSE), but they require technical expertise in acquisition and quantification.
The aim of this study was to evaluate the sensitivity, specificity, and positive and negative predictive values of FAC, FWS, and TAPSE derived using a rapid, novel artificial intelligence (AI) software (LVivoRV) from a single-plane transthoracic echocardiographic apical four-chamber, RV-focused view without ultrasound-enhancing agents for detecting abnormal RV function compared with CMR-derived RVEF. RV dysfunction was defined as RVEF < 50% and RVEF < 40% on CMR.
TTE and CMR were performed within a median of 10 days (interquartile range, 2-32 days) of each other in 225 consecutive patients without interval procedural or pharmacologic intervention. The sensitivity and negative predictive value to detect CMR-defined RV dysfunction when all three AI-derived parameters (FAC, FWS, and TAPSE) were abnormal were 91% and 96%, while those of expert physician reads were 91% and 97%. Specificity and positive predictive value were lower (50% and 32%) compared with expert physician-read echocardiograms (82% and 56%).
AI-derived measurements of FAC, FWS, and TAPSE had excellent sensitivity and negative predictive value for ruling out significant RV dysfunction (CMR RVEF < 40%), comparable with that of expert physician readers, but lower specificity. Thus AI, using American Society of Echocardiography
guidelines, may serve as a useful screening tool for rapid bedside assessment to exclude significant RV dysfunction.