Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway.
Clinic of Cardiology St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway.
J Clin Monit Comput. 2024 Apr;38(2):281-291. doi: 10.1007/s10877-023-01118-x. Epub 2024 Jan 27.
We have developed a method to automatically assess LV function by measuring mitral annular plane systolic excursion (MAPSE) using artificial intelligence and transesophageal echocardiography (autoMAPSE). Our aim was to evaluate autoMAPSE as an automatic tool for rapid and quantitative assessment of LV function in critical care patients. In this retrospective study, we studied 40 critical care patients immediately after cardiac surgery. First, we recorded a set of echocardiographic data, consisting of three consecutive beats of midesophageal two- and four-chamber views. We then altered the patient's hemodynamics by positioning them in anti-Trendelenburg and repeated the recordings. We measured MAPSE manually and used autoMAPSE in all available heartbeats and in four LV walls. To assess the agreement with manual measurements, we used a modified Bland-Altman analysis. To assess the precision of each method, we calculated the least significant change (LSC). Finally, to assess trending ability, we calculated the concordance rates using a four-quadrant plot. We found that autoMAPSE measured MAPSE in almost every set of two- and four-chamber views (feasibility 95%). It took less than a second to measure and average MAPSE over three heartbeats. AutoMAPSE had a low bias (0.4 mm) and acceptable limits of agreement (- 3.7 to 4.5 mm). AutoMAPSE was more precise than manual measurements if it averaged more heartbeats. AutoMAPSE had acceptable trending ability (concordance rate 81%) during hemodynamic alterations. In conclusion, autoMAPSE is feasible as an automatic tool for rapid and quantitative assessment of LV function, indicating its potential for hemodynamic monitoring.
我们开发了一种使用人工智能和经食管超声心动图（autoMAPSE）自动评估左心室功能的方法，通过测量二尖瓣环平面收缩期位移（MAPSE）。我们的目的是评估 autoMAPSE 作为一种快速、定量评估重症监护患者左心室功能的自动工具。在这项回顾性研究中，我们研究了 40 例心脏手术后的重症监护患者。首先，我们记录了一组超声心动图数据，包括三个连续的中食管两腔和四腔视图的心搏。然后，我们通过将患者置于反特伦德伦伯体位来改变他们的血液动力学，并重复记录。我们手动测量 MAPSE，并在所有可用的心跳和四个左心室壁上使用 autoMAPSE。为了评估与手动测量的一致性，我们使用了改良的 Bland-Altman 分析。为了评估每种方法的精度，我们计算了最小有意义变化（LSC）。最后，为了评估趋势能力，我们使用四象限图计算了一致率。我们发现，autoMAPSE 在几乎每一组两腔和四腔视图中都测量了 MAPSE（可行性 95%）。测量和平均三个心跳的 MAPSE 不到一秒。autoMAPSE 的偏倚低（0.4 毫米），一致性界限可接受（-3.7 至 4.5 毫米）。如果平均更多心跳，autoMAPSE 比手动测量更精确。在血液动力学改变期间，autoMAPSE 具有可接受的趋势能力（一致性率 81%）。总之，autoMAPSE 作为一种快速、定量评估左心室功能的自动工具是可行的，表明其在血液动力学监测方面的潜力。
