Abhishek Deshmukh 1 , Mark L Brown 2 , Elise Higgins 2 , Brian Schousek 2 , Athula Abeyratne 2 , Giovanni Rovaris 3 , Paul A Friedman 4 Show Affiliations »
Abstract
BACKGROUND: Patients 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. METHODS: The 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. RESULTS: In 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%. CONCLUSION: This 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. ©2016 The Authors. Pacing and Clinical Electrophysiology published by Wiley Periodicals, Inc.
BACKGROUND: Patients 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. METHODS: The 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. RESULTS: In 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%. CONCLUSION: This 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. ©2016 The Authors. Pacing and Clinical Electrophysiology published by Wiley Periodicals, Inc.
Entities: Disease
Species
Keywords:
algorithm; atrial fibrillation; single-chamber ICD
Mesh： See more »
Year: 2016
PMID： 27433785 DOI： 10.1111/pace.12918
Source DB: PubMed Journal: Pacing Clin Electrophysiol ISSN： 0147-8389 Impact factor: 1.976