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首页> 美国卫生研究院文献>Springer Open Choice >Minimizing discordances in automated classification of fractionated electrograms in human persistent atrial fibrillation
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Minimizing discordances in automated classification of fractionated electrograms in human persistent atrial fibrillation

机译：最大限度地减少人类持续性心房颤动的电描记图自动分类中的不一致性

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摘要

Ablation of persistent atrial fibrillation (persAF) targeting complex fractionated atrial electrograms (CFAEs) detected by automated algorithms has produced conflicting outcomes in previous electrophysiological studies. We hypothesize that the differences in these algorithms could lead to discordant CFAE classifications by the available mapping systems, giving rise to potential disparities in CFAE-guided ablation. This study reports the results of a head-to-head comparison of CFAE detection performed by NavX (St. Jude Medical) versus CARTO (Biosense Webster) on the same bipolar electrogram data (797 electrograms) from 18 persAF patients. We propose revised thresholds for both primary and complementary indices to minimize the differences in CFAE classification performed by either system. Using the default thresholds [NavX: CFE-Mean ≤ 120 ms; CARTO: ICL ≥ 7], NavX classified 70 % of the electrograms as CFAEs, while CARTO detected 36 % (Cohen’s kappa κ ≈ 0.3, P < 0.0001). Using revised thresholds found using receiver operating characteristic curves [NavX: CFE-Mean ≤ 84 ms, CFE-SD ≤ 47 ms; CARTO: ICL ≥ 4, ACI ≤ 82 ms, SCI ≤ 58 ms], NavX classified 45 %, while CARTO detected 42 % (κ ≈ 0.5, P < 0.0001). Our results show that CFAE target identification is dependent on the system and thresholds used by the electrophysiological study. The thresholds found in this work counterbalance the differences in automated CFAE classification performed by each system. This could facilitate comparisons of CFAE ablation outcomes guided by either NavX or CARTO in future works.Electronic supplementary materialThe online version of this article (doi:10.1007/s11517-016-1456-2) contains supplementary material, which is available to authorized users.
机译：自动算法检测到的针对复杂的心房电描记图（CFAE）的持续性房颤（persAF）消融在先前的电生理研究中产生了矛盾的结果。我们假设这些算法的差异可能会导致可用的映射系统导致CFAE分类不一致，从而在CFAE引导的消融中引起潜在的差异。这项研究报告了NavX（St. Jude Medical）与CARTO（Biosense Webster）对18位persAF患者的相同双极电描记图数据（797电描记图）进行CFAE检测的头对头比较。我们提出了针对主要指标和补充指标的修订阈值，以最大程度地减少由这两个系统执行的CFAE分类中的差异。使用默认阈值[NavX：CFE-Mean≤120ms; CARTO：ICL≥7]，NavX将70％的电描记图分类为CFAE，而CARTO检测到36％（Cohenκ≈0.3，P <0.0001）。使用通过接收机工作特性曲线发现的修正阈值[NavX：CFE-Mean≤84 ms，CFE-SD≤47 ms； CARTO：ICL≥4，ACI≤82 ms，SCI≤58 ms]，NavX分类为45％，而CARTO检测到42％（κ≈0.5，P <0.0001）。我们的结果表明，CFAE目标识别取决于电生理研究所使用的系统和阈值。在这项工作中发现的阈值抵消了每个系统执行的自动CFAE分类的差异。这可能有助于在以后的工作中比较由NavX或CARTO指导的CFAE消融效果。电子补充材料本文的在线版本（doi：10.1007 / s11517-016-1456-2）包含补充材料，授权用户可以使用。

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