Department of Cardiology, University Medical Center Utrecht, Utrecht, The Netherlands.
Netherlands Heart Institute, Utrecht, The Netherlands.
J Cardiovasc Transl Res. 2019 Dec;12(6):517-527. doi: 10.1007/s12265-019-09899-w. Epub 2019 Jul 23.
Many cardiac catheter interventions require accurate discrimination between healthy and infarcted myocardia. The gold standard for infarct imaging is late gadolinium-enhanced MRI (LGE-MRI), but during cardiac procedures electroanatomical or electromechanical mapping (EAM or EMM, respectively) is usually employed. We aimed to improve the ability of EMM to identify myocardial infarction by combining multiple EMM parameters in a statistical model. From a porcine infarction model, 3D electromechanical maps were 3D registered to LGE-MRI. A multivariable mixed-effects logistic regression model was fitted to predict the presence of infarct based on EMM parameters. Furthermore, we correlated feature-tracking strain parameters to EMM measures of local mechanical deformation. We registered 787 EMM points from 13 animals to the corresponding MRI locations. The mean registration error was 2.5 ± 1.16 mm. Our model showed a strong ability to predict the presence of infarction (C-statistic = 0.85). Strain parameters were only weakly correlated to EMM measures. The model is accurate in discriminating infarcted from healthy myocardium. Unipolar and bipolar voltages were the strongest predictors.
许多心脏导管介入治疗都需要准确地区分健康和梗死的心肌。用于梗死成像的金标准是钆延迟增强磁共振成像（LGE-MRI），但在心脏手术中，通常使用电解剖或机电图（EAM 或 EMM）。我们旨在通过在统计模型中结合多个 EMM 参数来提高 EMM 识别心肌梗死的能力。从猪的梗死模型中，将 3D 机电图与 LGE-MRI 进行 3D 配准。根据 EMM 参数拟合多变量混合效应逻辑回归模型来预测梗死的存在。此外，我们还将特征追踪应变参数与局部机械变形的 EMM 测量值相关联。我们将 13 只动物的 787 个 EMM 点与相应的 MRI 位置进行了配准。平均配准误差为 2.5 ± 1.16 毫米。我们的模型在预测梗死的存在方面具有很强的能力（C 统计量 = 0.85）。应变参数与 EMM 测量值仅呈弱相关。该模型在区分梗死和健康心肌方面非常准确。单极和双极电压是最强的预测因子。
