Abeyratne, Athula

BackgroundPatients with implantable cardioverter defibrillators (ICDs) often have a history of atrial fibrillation (AF) or will develop AF after device implant. Optimal management of ICD patients includes early diagnosis of AF and monitoring of AF burden. We evaluated the performance of an algorithm for monitoring AF in single‐chamber ICDs.MethodsThe RR interval variability‐based detection algorithm determines RR variability by creating a Lorenz plot of the change in RR intervals for the most recent interval pair versus the previous interval pair. A new plot is created every 2 minutes and the AF evidence score of the plot is computed. Patient RR interval data from several Holter databases were pooled to test the performance of the AF detection algorithm.ResultsIn total, 187 recordings from 186 patients were used to assess the performance of the AF detection algorithm integrated into a single‐chamber ICD by comparing the ICD detection results to the Holter annotated truth. Thirty‐five of 186 patients had a total of 94 AF episodes in their Holter recordings lasting a total of 250.5 hours (mean episode duration 7.2 hours). The generalized estimating equations‐adjusted estimate of episode sensitivity was 94.8% with 95% lower confidence limit of 87.2%. Gross duration sensitivity was 95.0% for AF episodes of at least 6 minutes duration with gross duration specificity of 99.6%.ConclusionThis RR interval‐based AF detection algorithm performs well with high sensitivity and specificity. Integration of this algorithm into single‐chamber ICDs will help monitor and detect AF, thus facilitating optimal therapy to prevent AF‐related sequelae.

Background— Implantable cardioverter-defibrillator system efficacy is tested at implant by induction of ventricular fibrillation (VF). Defibrillation safety margin can be assessed without VF induction using upper limit of vulnerability methods, but these methods have required manual determination of T-wave timing. Methods and Results— To test the feasibility of an inductionless system of implant testing, a multicenter prospective study of an automated vulnerability safety margin system was conducted, which measured T-wave timing using an intracardiac electrogram during a ventricular pacing train. The system delivered up to 4 T-wave shocks of 18 J. Lack of VF induction by all 4 shocks was considered evidence of defibrillation adequacy. Patients subsequently underwent conventional defibrillation testing to meet a standard implant criterion. The 95% lower CI for defibrillation success at 25 J for noninduced patients was found using Bayesian statistics. Sixty patients were enrolled at 6 centers. Vulnerability testing and defibrillation success results were obtained from 54 patients. Vulnerability testing induced VF in 10 (19%) patients, of whom 2 required system revision. All patients not induced by vulnerability testing were successfully defibrillated twice at ≤25 J. The Bayesian credible interval was 97% to 100% for the population success rate of defibrillation at 25 J for automated vulnerability safety margin noninduced patients. Conclusions— An automated system identified all patients who failed conventional safety margin testing, while inducing only 19% of patients. Although limited by sample size, this study suggests the feasibility of automated implant testing that substantially reduces the need for VF induction in patients receiving implantable cardioverter-defibrillators.