Cardiology Department, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom; School of Biomedical Engineering and Imaging Sciences, King's College London, United Kingdom.
Cardiology Department, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom; School of Biomedical Engineering and Imaging Sciences, King's College London, United Kingdom.
Heart Rhythm. 2019 Aug;16(8):1242-1250. doi: 10.1016/j.hrthm.2019.03.001. Epub 2019 Mar 5.
Risk stratification of ventricular arrhythmia remains complex in patients with ischemic and nonischemic cardiomyopathy.
The purpose of this study was to determine whether scar heterogeneity, quantified by mean entropy, predicts appropriate implantable cardioverter-defibrillator (ICD) therapy. We hypothesized that higher mean entropy calculated from cardiac magnetic resonance texture analysis (CMR-TA) will predict appropriate ICD therapy.
Consecutive patients underwent CMR imaging before ICD implantation. Short-axis left ventricular scar was manually segmented. CMR-TA was performed using a Laplacian filter to extract and augment image features to create a scar texture from which histogram analysis of pixel intensity was used to calculate mean entropy. The primary end point was appropriate ICD therapy.
A total of 114 patients underwent CMR-TA (ischemic cardiomyopathy [ICM]: n = 70; nonischemic cardiomyopathy [NICM]: n = 44) with a median follow-up of 955 days (interquartile range 691-1185 days). Mean entropy was significantly higher in the ICM group (5.7 ± 0.7 vs 5.5 ± 0.7; P= .045). Overall, 33 patients received appropriate ICD therapy. Using optimized cutoff values from receiver operating characteristic curves, Kaplan-Meier survival analysis demonstrated time until first appropriate therapy was significantly shorter in the high mean entropy group (P = .003). Multivariable analysis showed that mean entropy was the sole predictor of appropriate ICD therapy (hazard ratio 1.882; 95% confidence interval 1.083-3.271; P = .025). In the ICM group, mean entropy remained an independent predictor of appropriate ICD therapy, whereas in the NICM group, precontrast T1 values were the sole predictor.
Scar heterogeneity, quantified by mean entropy using CMR-TA, was an independent predictor of appropriate ICD therapy in the mixed cardiomyopathy cohort and ICM-only group, suggesting a potential role for CMR-TA in predicting ventricular arrhythmia and risk-stratifying patients for ICD implantation.
在缺血性和非缺血性心肌病患者中，室性心律失常的风险分层仍然复杂。
本研究旨在确定通过平均熵量化的瘢痕异质性是否可以预测合适的植入式心脏复律除颤器（ICD）治疗。我们假设从心脏磁共振纹理分析（CMR-TA）计算出的较高平均熵将预测合适的 ICD 治疗。
连续患者在 ICD 植入前进行 CMR 成像。左心室短轴瘢痕通过手动分割。使用拉普拉斯滤波器进行 CMR-TA，以提取和增强图像特征，从瘢痕纹理中创建像素强度直方图分析，以计算平均熵。主要终点是适当的 ICD 治疗。
共对 114 例患者进行了 CMR-TA（缺血性心肌病 [ICM]：n = 70；非缺血性心肌病 [NICM]：n = 44），中位随访时间为 955 天（四分位距 691-1185 天）。ICM 组的平均熵明显较高（5.7 ± 0.7 比 5.5 ± 0.7；P =.045）。总的来说，33 例患者接受了适当的 ICD 治疗。使用接收器操作特征曲线的优化截断值，Kaplan-Meier 生存分析表明，高平均熵组的首次适当治疗时间明显缩短（P =.003）。多变量分析表明，平均熵是唯一预测适当 ICD 治疗的因素（风险比 1.882；95%置信区间 1.083-3.271；P =.025）。在 ICM 组中，平均熵仍然是适当 ICD 治疗的独立预测因素，而在 NICM 组中，对比前 T1 值是唯一的预测因素。
使用 CMR-TA 通过平均熵量化的瘢痕异质性是混合心肌病队列和仅 ICM 组中适当 ICD 治疗的独立预测因素，这表明 CMR-TA 在预测室性心律失常和为 ICD 植入风险分层患者方面具有潜在作用。
