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首页> 外文期刊>PACE: Pacing and clinical electrophysiology >Clinical evaluation of morphology discrimination: an algorithm for rhythm discrimination in cardioverter defibrillators.
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Clinical evaluation of morphology discrimination: an algorithm for rhythm discrimination in cardioverter defibrillators.

机译：形态学辨别的临床评估：一种用于心脏复律除颤器的节奏辨别的算法。

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The aim of this study was to test the new morphology discrimination diagnostic algorithm for ICDs that differentiates supraventricular tachycardias (SVTs) from VTs by analysis of ventricular depolarization complexes morphology. Twenty-five patients implanted with a St. Jude Ventritex single chamber ICD were studied during electrophysiological evaluation at predischarge and were followed for 7 +/- 4 months. Sensitivity and specificity for VT detection and overall diagnostic accuracy of the morphology discrimination algorithm were calculated on 326 detected events. At electrophysiological evaluation, the algorithm was tested during 67 episodes of right atrial pacing, during 119 episodes of RV pacing (at basal interventricular septum and RV apex) and during 27 episodes of sustained AF: specificity was 98%, sensitivity was 66%, and diagnostic accuracy was 80%. All episodes of AF were correctly diagnosed as SVT. Exclusion of detections related to pacing at the basal interventricular septum, resulted in a specificity of 98%, a sensitivity of 85%, and a diagnostic accuracy of 93%. During follow-up, evaluation of the morphology discrimination algorithm on 113 spontaneous episodes (31 VTs, 31 AF, 7 SVTs, and 44 sinus tachycardias) exhibited a specificity of 89%, a sensitivity of 100%, and a diagnostic accuracy of 92%. In conclusion, the morphology discrimination algorithm exhibits a high specificity in discriminating VTs from SVTs, although with a corresponding reduction in sensitivity. The preliminary experience on spontaneous episodes is promising. To correct for the reduction in sensitivity, it is advisable to use this algorithm in parallel with other algorithms for rhythm discrimination (sudden onset, stability) coupled with extended high rate.
机译：这项研究的目的是通过分析心室去极化复合物的形态来测试用于ICD的新的形态学鉴别诊断算法，该算法可区分室上性心动过速（SVT）和室速。在放电前的电生理评估过程中研究了25名植入了St. Jude Ventritex单室ICD的患者，并随访7 +/- 4个月。对326个检测到的事件计算出VT检测的敏感性和特异性以及形态学判别算法的总体诊断准确性。在电生理评估中，对算法进行了测试，包括67次右心房起搏，119次RV起搏（在基础室间隔和RV顶点）和27次持续性AF：特异性为98％，敏感性为66％，并且诊断准确性为80％。所有房颤均被正确诊断为SVT。排除与室间隔底部起搏有关的检测结果，可得出特异性为98％，灵敏度为85％和诊断准确度为93％。在随访期间，对113种自发发作（31 VT，31 AF，7 SVT和44窦性心动过速）进行形态学判别算法的评估显示出89％的特异性，100％的敏感性和92％的诊断准确性。总之，形态学识别算法在区分VT和SVT方面表现出很高的特异性，尽管灵敏度相应降低。自发发作的初步经验是有希望的。为了纠正灵敏度的降低，建议与其他算法并行使用此算法来进行节奏识别（突然发作，稳定性）并伴有高速率。

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