AimsIntermittent change in p-wave discernibility during periods of ectopy and sinus arrhythmia is a cause of inappropriate atrial fibrillation (AF) detection in insertable cardiac monitors (ICM). To address this, we developed and validated an enhanced AF detection algorithm.Methods and resultsAtrial fibrillation detection in Reveal LINQ ICM uses patterns of incoherence in RR intervals and absence of P-wave evidence over a 2-min period. The enhanced algorithm includes P-wave evidence during RR irregularity as evidence of sinus arrhythmia or ectopy to adaptively optimize sensitivity for AF detection. The algorithm was developed and validated using Holter data from the XPECT and LINQ Usability studies which collected surface electrocardiogram (ECG) and continuous ICM ECG over a 24–48 h period. The algorithm detections were compared with Holter annotations, performed by multiple reviewers, to compute episode and duration detection performance. The validation dataset comprised of 3187 h of valid Holter and LINQ recordings from 138 patients, with true AF in 37 patients yielding 108 true AF episodes ≥2-min and 449 h of AF. The enhanced algorithm reduced inappropriately detected episodes by 49% and duration by 66% with <1% loss in true episodes or duration. The algorithm correctly identified 98.9% of total AF duration and 99.8% of total sinus or non-AF rhythm duration. The algorithm detected 97.2% (99.7% per-patient average) of all AF episodes ≥2-min, and 84.9% (95.3% per-patient average) of detected episodes involved AF.ConclusionAn enhancement that adapts sensitivity for AF detection reduced inappropriately detected episodes and duration with minimal reduction in sensitivity.
The enhanced dual sense algorithm for bradycardia and pause detection in ICMs substantially reduced inappropriate episode detection with a minimal reduction in true episode detection.
Background: Premature ventricular complexes (PVCs) are an important therapeutic target in symptomatic patients and in the setting of PVC-induced cardiomyopathy; however, measuring burden and therapeutic response is challenging. We developed and validated an algorithm for continuous long-term monitoring of PVC burden in an insertable cardiac monitor (ICM). Methods:A high-specificity PVC detection algorithm was developed using real-world ICM data and validated using simultaneous Holter data and real-world ICM data. The PVC algorithm uses long-short-long RR interval sequence and morphology characteristics for three consecutive beats to detect the occurrence of single PVC beats. Data are expressed as gross incidence, patient average, and generalized estimating equation estimates, which were used to determine sensitivity, specificity, positive and negative predictive value (PPV, NPV). Results:The PVC detection algorithm was developed on eighty-seven 2-min EGM strips recorded by an ICM to obtain a sensitivity and specificity of 75.9% and 98.8%. The ICM validation data cohort consisted of 787 ICM recorded ECG strips 7-16 min in duration from 134 patients, in which the algorithm detected PVC beats with a sensitivity, specificity, PPV, and NPV of 75.2%, 99.6%, 75.9%, and 99.5%, respectively. In the Holter validation dataset with continuous 2-h snippets from 20 patients, the algorithm sensitivity, specificity, PPV, and NPV were 74.4%, 99.6%, 68.8%, and 99.7%, respectively, for detecting PVC beats.Conclusions: The PVC detection algorithm was able to achieve a high specificity with only 0.4% of the normal events being incorrectly identified as PVCs, while detecting around three of four PVCs on a continuous long-term basis in ICMs. K E Y W O R D Sinsertable cardiac monitor, premature ventricular contraction, PVC burden, PVC-induced cardiomyopathy
Background High premature ventricular contraction (PVC) burden may increase the risk of cardiac arrhythmias, PVC-induced cardiomyopathy and heart failure. Purpose We developed and validated an algorithm for continuous long-term monitoring of PVC burden in implantable loop recorders or insertable cardiac monitors (ICM). Methods The PVC algorithm uses long-short-long RR interval sequence and similarity and differences in r-wave morphology for three consecutive beats to detect the occurrence of a single PVC beat. Various threshold combinations were used for long-short-long RR interval sequence and degree of difference and similarity of R-wave morphology to be able to detect various types of PVCs including monomorphic, polymorphic, bigeminal, trigeminal, and interpolated PVCs. For example, a high degree of difference in R-wave morphology only required the short interval to be less than the longer interval by a smaller amount. The algorithm was designed with the intention to achieve minimum over reporting of PVC burden, i.e. maximum specificity. The algorithm was developed and validated using ECG strips stored in an ICM from real world patients. Gross, patient average and generalized estimating equation (GEE) estimates for sensitivity, specificity, positive and negative predictive value are reported. Results The PVC detection algorithm was developed using 87 2-minute ECG strips recorded by an ICM containing 2129 single PVC beats and 12,402 non-PVC beats to obtain a gross sensitivity and specificity of 75.9% and 98.8%. The validation data cohort consisted of 787 ICM recorded ECG strips 7–10 minutes in duration from 134 patients, providing over 460,000 beats of which 439,106 (94%) were normal beats, 8398 (2%) single PVC beats and 16,634 (4%) noisy beats. Couplets and triplets were excluded. Table 1 shows the performance results of the PVC detection algorithm in this validation set. Performance of PVC detector Gross Patient average GEE (95% CI) Sensitivity 75.2% 69.9% 72.5% (65.8–78.3) Specificity 99.6% 99.4% 99.4% (99.2–99.6) Positive Predictive Value (PPV) 75.9% 40.6% 40.6% (33.6–48.0) Negative Predictive Value (NPV) 99.5% 99.6% 99.6% (99.3–99.7) Conclusions The PVC detection algorithm was able to achieve a high specificity, which ensures that 99.6% of the normal events are not incorrectly identified as PVCs, while detecting 75% of PVCs on a continuous long-term basis in insertable cardiac monitors. The accuracy of PVC burden estimates during continuous monitoring using this algorithm needs further validation using Holter studies. Acknowledgement/Funding Medtronic Plc
Background: Frequent premature atrial contractions and sick sinus syndrome are primary causes of inappropriate atrial fibrillation (AF) detection in insertable cardiac monitors (ICM). An algorithm was developed to reduce inappropriate AF detection based on adapting the threshold for detection in the presence of irregular RR intervals and p-wave evidence. Methods: The AF detection algorithm in Reveal LINQ ICM looks for evidence of AF based on differences in the pattern of RR intervals over a 2-minute period. The p-wave evidence based algorithm (P-SENSE) reduces evidence for AF detection if p-waves are detected. The adaptive P-SENSE enhancement uses the presence of p-wave evidence during periods of RR irregularity as evidence of the presence of sick sinus or ectopy to adaptively increase the threshold for AF detection. The algorithm was developed using Holter data from the XPECT study which collected two leads of surface ECG and continuously uplinked ICM ECG over a 46 hour period. ICM detections were compared with Holter annotations to compute episode and duration detection performance. Results: Valid Holter recordings were analyzed from the first 56 patients in the XPECT study with a total followup duration of 2168 hours (39 hours per patient). True AF was observed in 16 patients, yielding 89 true AF episodes !2 minutes and 201 hours of AF. In the nominal (and aggressive) mode of operation, the algorithm correctly identified 97.9% (97.8%) of total AF duration and 99.5% (99.6%) of total sinus or non-AF rhythm duration. The algorithm detected 89% (89%) of all AF episodes !2 minutes, and 60% (74%) of detected episodes had AF in the nominal mode of operation. The adaptive P-SENSE algorithm in nominal (or aggressive) mode was able to reduce false detects by 76% (87%) compared to an algorithm without P-SENSE and 56% (63%) compared to the nominal (aggressive) P-SENSE without any loss in true episode detection (Figure). Conclusions: An ICM algorithm enhancement for AF detection incorporating p-wave and RR variability information to adapt the AF detection threshold substantially reduced inappropriately detected episodes and duration with minimal reduction in sensitivity for detecting AF. The algorithm needs to be validated in an independent dataset.
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