Circulation. Arrhythmia and electrophysiology最新文献

Background: Macroreentry stands as the predominant mechanism of typical and atypical flutter. Despite advances in mapping, many aspects of macroreentrant atrial tachycardia remain unsolved. In this translational study, we applied principles of topology to understand the activation patterns, entrainment characteristics, and ablation responses in a large clinical macroreentrant atrial tachycardia database.

Methods: Because the atrium can be topologically seen as a closed sphere with holes, we used a computational fixed spherical mesh model with a finite number of holes to induce and analyze macroreentrant atrial tachycardia. The ensuing insights were used to interpret high-density activation maps, postpacing interval-tachycardia cycle length values (difference between postpacing interval and tachycardia cycle length), and ablation response in 131 cases of typical and atypical flutter (n=106 left atrium, n=25 right atrium).

Results: Modeling of macroreentrant atrial tachycardia revealed that reentry on closed surfaces consistently manifests itself as paired rotation and that an odd number of critical boundaries is mathematically impossible. Together with mathematical confirmation by the index theorem, this led to a unifying construct that could explain the number of loops, difference between postpacing interval and tachycardia cycle length values, and ablation outcomes (termination, no change, or prolongation in tachycardia cycle length) in all 131 cases.

Conclusions: Combining topology with the index theorem offers a novel and cohesive framework for understanding and managing typical and atypical flutter.

Background: Atrial fibrillation (AF) is associated with an increased risk of stroke, yet the limitations of conventional monitoring have restricted our understanding of AF burden risk thresholds. Predictive algorithms incorporating continuous AF burden measures may be useful for predicting stroke. This study evaluated the performance of temporal AF burden trends as predictors of stroke from a large cohort with insertable cardiac monitors.

Methods: Using deidentified data from Optum Clinformatics Data Mart (2007-2019) linked with the Medtronic CareLink insertable cardiac monitor database, we identified patients with an insertable cardiac monitor for AF management (n=1197), suspected AF (n=1611), and cryptogenic stroke (n=2205). Daily AF burden was transformed into simple moving averages, and temporal AF burden trends were defined as the comparison of unique simple moving average pairs. Classification trees were used to predict ischemic stroke, and AF burden significance was quantified using bootstrapped mean variable importance.

Results: Of 5013 patients (age, 69.2±11.7 years; 50% male; CHA₂DS₂-VASc, 3.7±1.9) who met inclusion criteria, 869 had an ischemic stroke over 2 409 437 days total follow-up. Prior stroke or transient ischemic attack (variable importance, 13.13) was the number 1 predictor of future stroke followed by no prior diagnosis of AF (7.35) and AF burden trends in follow-up (2.59). Temporal proximity of AF and risk of stroke differed by device indication (simple moving averages: AF management, <8 days and suspected AF and cryptogenic stroke, 8-21 days). Together, baseline characteristics and AF burden trends performed optimally for the area under the receiver operating characteristic curve (0.73), specificity (0.70), and relative risk (5.00).

Conclusions: AF burden trends may provide incremental prognostic value as leading indicators of stroke risk compared with conventional schemes.

Background: Atrial fibrillation is associated with an increased risk of cardiovascular hospitalization (CVH), which may be triggered by changes in daily burden. Machine learning of dynamic trends in atrial fibrillation burden, as measured by insertable cardiac monitors (ICMs), may be useful in predicting near-term CVH.

Methods: Using Optum's deidentified Clinformatics Data Mart Database (2007-2019), linked with the Medtronic CareLink ICM database, we identified patients with >1 days of ICM-detected atrial fibrillation. ICM-detected diagnostic parameters were transformed into simple moving averages over different periods for daily follow-up. A diagnostic trend was defined as the comparison of 2 simple moving averages of different periods for each diagnostic parameter. CVH was defined as any hospital, emergency department, or ambulatory surgical center encounter with a cardiovascular diagnosis-related group or diagnosis code. Machine learning was used to determine which diagnostic trends could best predict patient risk 5 days before CVH.

Results: A total of 2616 patients with ICMs met the inclusion criteria (71±11 years; 55% male). Among them, 1998 (76%) had a planned or unplanned CVH over 605 363 days. Machine learning revealed distinct groups: (A) sinus rhythm (reference), (B) below-average burden, (C) above-average burden, and (D) above-average burden with decreasing patient activity. The relative risk was increased in all groups versus the reference (B, 4.49 [95% CI, 3.74-5.40]; C, 8.41 [95% CI, 7.00-10.11]; D, 11.15 [95% CI, 9.10-13.65]), including a 21% increase in CVH detection over prespecified burden thresholds of duration (≥1 hour) and quantity (≥5%). The area under the receiver operating characteristic curve increased from 0.55 when using hourly burden amounts to 0.66 when using burden trends and decreasing patient activity (P<0.001), a 20% increase in predictive power.

Conclusions: Trends in atrial fibrillation were strongly associated with near-term CVH, especially above-average burden coupled with low patient activity. This approach could provide actionable information to guide treatment and reduce CVH.

Background: Left bundle branch area pacing (LBBAP) may be an alternative to biventricular pacing (BVP) for cardiac resynchronization therapy (CRT). We sought to compare the acute hemodynamic and ECG effects of LBBAP, BVP, and left bundle-optimized therapy CRT (LOT-CRT) in CRT candidates with advanced conduction disease.

Methods: In this multicenter study, 48 patients with either nonspecific interventricular conduction delay (n=29) or left bundle branch block (n=19) underwent acute hemodynamic testing to determine the change in left ventricular pressure maximal first derivative (LV dP/dt_max) from baseline atrial pacing to BVP, LBBAP, or LOT-CRT.

Results: Atrioventricular-optimized increases in LV dP/dt_max for LOT-CRT (mean, 25.8% [95% CI, 20.9%-30.7%]) and BVP (26.4% [95% CI, 20.2%-32.6%]) were greater than unipolar LBBAP (19.3% [95% CI, 15.0%-23.7%]) or bipolar LBBAP (16.4% [95% CI, 12.7%-20.0%]; P≤0.005). QRS shortening was greater in LOT-CRT (29.5 [95% CI, 23.4-35.6] ms) than unipolar LBBAP (11.9 [95% CI, 6.1-17.7] ms), bipolar LBBAP (11.7 ms [95% CI, 6.4-17.0]), or BVP (18.5 [95% CI, 11.0-25.9] ms), all P≤0.005. Compared with patients with left bundle branch block, patients with interventricular conduction delay experienced less QRS reduction (P=0.026) but similar improvements in LV dP/dt_max (P=0.29). Bipolar LBBAP caused anodal capture in 54% of patients and resulted in less LV dP/dt_max improvement than unipolar LBBAP (18.6% versus 23.7%; P<0.001). Subclassification of LBBAP capture (EHRA criteria) indicated LBBAP or LV septal pacing in 27 patients (56%) and deep septal pacing in 21 patients (44%). The hemodynamic benefit of adding left ventricular coronary vein pacing to LBBAP depended on baseline QRS duration (P=0.031) and success of LBBAP (P<0.004): LOT-CRT provided 14.5% (5.0%-24.1%) greater LV dP/dt_max improvement and 20.8 (12.8-28.8) ms greater QRS shortening than LBBAP in subjects with QRS ≥171 ms and deep septal pacing capture type.

Conclusions: In a CRT cohort with advanced conduction disease, LOT-CRT and BVP provided greater acute hemodynamic benefit than LBBAP. Subjects with wider QRS or deep septal pacing are more likely to benefit from the addition of a left ventricular coronary vein lead to implement LOT-CRT.

Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT04905290.