The temporal stability of recurrence quantification analysis attributes from chronic atrial fibrillation electrograms

Abstract Introduction The temporal behavior of atrial electrograms (AEGs) collected during persistent atrial fibrillation (persAF) directly affects ablative treatment outcomes. We investigated different durations of AEGs collected during persAF using recurrence quantification analysis (RQA). Methods 797 bipolar AEGs with different durations (from 0.5 s to 8 s) from 18 patients were investigated. Four RQA-based attributes were evaluated based on AEG durations: determinism (DET); recurrence rate (RR); laminarity (LAM); and diagonal lines’ entropy (ENTR). The Spearman correlation (ρ) between each duration versus 8 s was calculated. AEG classification was performed following the CARTO criteria (Biosense Webster) and receiving operating characteristic (ROC) curves were created for the RQA variables. Results The RQA variables successfully discriminated the AEGs: the area under the ROC curves were as high as 0.70 for AEGs with 3.5 s or greater. Three types of AEGs were found using these variables: normal, fractionated and temporally unstable. The number of unstable AEGs decreased with longer AEG segments. Different AEG durations significantly affected the RQA variables (P<0.0001), with no statistical difference between the durations 6 s, 7 s and 8 s for DET, LAM and ENTR, and no difference between 7 s and 8 s for RR (P<0.0001). AEGs with 3 s or longer have shown ρ ≥ 80% for all variables. Conclusion The RQA variables have been shown effective in the characterization of AEGs collected during persAF with a shorter duration than current recommendations, which motivates their use for the characterization of atrial substrate during persAF ablation.

【摘要】
引言：持续性心房颤动（persistent atrial fibrillation, persAF）术中采集的心房电图（atrial electrograms, AEGs）的时域特征直接影响消融治疗结局。本研究采用递归定量分析（recurrence quantification analysis, RQA），探究了persAF术中采集的不同时长AEGs的相关特征。
方法：本研究纳入18名患者的797份时长范围为0.5秒至8秒的双极心房电图。基于AEG时长，评估了4项基于RQA的指标：确定性（determinism, DET）、复发率（recurrence rate, RR）、层流性（laminarity, LAM）及对角线熵（diagonal lines’ entropy, ENTR）。计算各时长与8秒时长AEGs的斯皮尔曼相关系数（ρ）。参照Biosense Webster公司的CARTO标测系统标准完成AEG分类，并为各RQA变量绘制受试者工作特征（ROC）曲线。
结果：RQA指标可有效区分AEGs：时长≥3.5秒的AEGs的ROC曲线下面积最高可达0.70。基于上述指标可将AEGs分为3类：正常型、碎裂型及时间不稳定型。不稳定型AEG的数量随AEG片段时长增加而减少。不同AEG时长对RQA指标存在显著影响（P<0.0001）；其中6秒、7秒与8秒时长的AEGs在DET、LAM及ENTR指标上无统计学差异，7秒与8秒时长的AEGs在RR指标上亦无统计学差异。时长≥3秒的AEGs，其各指标与8秒时长AEGs的斯皮尔曼相关系数ρ≥80%。
结论：本研究证实，RQA指标可有效表征时长短于当前临床推荐标准的persAF术中采集的AEGs，这为persAF消融术中心房基质的表征提供了可行的应用依据。