Department of Cardiovascular Medicine, Cleveland Clinic, Cleveland, OH, USA.
Department of Diagnostic Radiology, Cleveland Clinic, Cleveland, OH, USA; Department of Radiology, Al-Azhar University, Cairo, Egypt; Cardiopulmonary Imaging Division, University of Alabama at Birmingham, Birmingham, AL, USA.
Heart Lung Circ. 2021 Sep;30(9):1348-1355. doi: 10.1016/j.hlc.2021.02.011. Epub 2021 Mar 17.
Cardiac magnetic resonance imaging (CMR) image quality can be degraded by artifact in patients with cardiac implantable electronic devices (CIED). We aimed to establish a clinical risk score, so patient selection for diagnostic CMR could be optimised.
In this retrospective cohort study, CMRs performed for clinical use in subjects with CIED from January 2016 to May 2019 were reviewed. Subject anthropometry, CIED generator/lead specifications and pre-scan chest X-ray (CXR) measurements were collected. Generator-related artifact size was measured on axial steady state free precession images. Interpretability of late gadolinium enhancement (LGE) imaging was performed based on a three-grade visual score attributed to each of 17 myocardial segments.
Fifty-seven (57) patients (59±16 years, 74% male) fitted the inclusion criteria. Artifact precluded left ventricle (LV) evaluation (≥5 segments) in 17 (30%). Artifact was more common with implantable cardioverter-defibrillators, related to generator volume, mass, height, width, thickness, and area, along with right ventricular (RV) lead length and diameter (all p<0.05). Artifact was associated with distance from generator to LV apex, generator to RV lead tip and shortest distance from generator to heart on CXR (all p<0.05). On multivariable regression modelling, RV lead diameter (OR 5.861, 95% CI 1.866-18.407, p=0.002) and distance from generator to LV apex (OR 0.693, 95% CI 0.511-0.940, p=0.019) were independent predictors of artifact. Multivariable predictors were used to develop Device Related CMR Artifact Prediction Score (DR-CAPS), where all patients with DR-CAPS=0 had fully interpretable LGE imaging.
Simple, readily available measures, such as lead characteristics and pre-scan CXR measures, can stratify patients via an artifact prediction score to optimise selection for diagnostic CMR.
心脏植入式电子设备（CIED）患者的心脏磁共振成像（CMR）图像质量可能会因伪影而降低。我们旨在建立一种临床风险评分，以优化诊断性 CMR 的患者选择。
在这项回顾性队列研究中，对 2016 年 1 月至 2019 年 5 月期间因临床需要进行 CMR 的 CIED 患者进行了回顾性分析。收集了受试者的人体测量学、CIED 发生器/导联规格和扫描前胸部 X 线（CXR）测量值。在轴位稳态自由进动图像上测量发生器相关伪影的大小。根据每个 17 个心肌节段的视觉评分（3 级）对晚期钆增强（LGE）成像的可解释性进行评估。
57 例（59±16 岁，74%为男性）患者符合纳入标准。17 例（30%）因伪影而无法评估左心室（LV）（≥5 个节段）。植入式心脏复律除颤器的伪影更常见，与发生器体积、质量、高度、宽度、厚度和面积以及右心室（RV）导联长度和直径有关（均 p<0.05）。发生器与 LV 心尖、发生器与 RV 导联尖端以及发生器与 CXR 上心脏最短距离的距离与伪影有关（均 p<0.05）。多变量回归模型分析显示，RV 导联直径（OR 5.861，95%CI 1.866-18.407，p=0.002）和发生器与 LV 心尖的距离（OR 0.693，95%CI 0.511-0.940，p=0.019）是伪影的独立预测因子。多变量预测因子用于开发设备相关 CMR 伪影预测评分（DR-CAPS），其中所有 DR-CAPS=0 的患者均具有完全可解释的 LGE 成像。
简单、易于获得的指标，如导联特征和扫描前 CXR 指标，可通过伪影预测评分对患者进行分层，以优化诊断性 CMR 的患者选择。
