Biomedical subjects

Jeffrey M Gillberg

Publications and source records attributed to Jeffrey M Gillberg.

6 recordsLinked to original sources

PubMed2006

Improving SVT discrimination in single-chamber ICDs: a new electrogram morphology-based algorithm.

INTRODUCTION: Wide-spread adoption of ICD therapy has focused efforts on improving the quality of life for patients by reducing "inappropriate" shock therapies. To this end, distinguishing supraventricular tachycardia from ventricular tachycardia remains a major challenge for ICDs. More sophisticated discrimination algorithms based on ventricular electrogram morphology have been made practicable by the increased computational ability of modern ICDs. METHODS AND RESULTS: We report results from a large prospective study (1,122 pts) of a new ventricular electrogram morphology tachycardia discrimination algorithm (Wavelet Dynamic Discrimination, Medtronic, Minneapolis, MN, USA) operating at minimal algorithm setting (RV coil-can electrogram, match threshold of 70%). This is a nonrandomized cohort study of ICD patients using the morphology discrimination of the Wavelet algorithm to distinguish SVT and VT/VF. The Wavelet criterion was required ON in all patients and all other supraventricular tachycardia discriminators were required to be OFF. Spontaneous episodes (N = 2,235) eligible for ICD therapy were adjudicated for detection algorithm performance. The generalized estimating equations method was used to remove bias introduced when an individual patient contributes multiple episodes. Inappropriate therapies for supraventricular tachycardia were reduced by 78% (90% CI: 72.8-82.9%) for episodes within the range of rates where Wavelet was programmed to discriminate. Sensitivity for sustained ventricular tachycardia was 98.6% (90% CI: 97-99.3%) without the use of high-rate time out. CONCLUSIONS: Results from this prospective study of the Wavelet electrogram morphology discrimination algorithm operating as the sole discriminator in the ON mode demonstrate that inappropriate therapy for supraventricular tachycardia in a single-chamber ICD can be dramatically reduced compared to rate detection alone.

Algorithms↗

PubMed2006

Development and testing of an algorithm to detect implantable cardioverter-defibrillator lead failure.

BACKGROUND: Implantable cardioverter-defibrillator (ICD) lead failures often present as inappropriate shock therapy. An algorithm that can reliably discriminate between ventricular tachyarrhythmias and noise due to lead failure may prevent patient discomfort and anxiety and avoid device-induced proarrhythmia by preventing inappropriate ICD shocks. OBJECTIVES: The goal of this analysis was to test an ICD tachycardia detection algorithm that differentiates noise due to lead failure from ventricular tachyarrhythmias. METHODS: We tested an algorithm that uses a measure of the ventricular intracardiac electrogram baseline to discriminate the sinus rhythm isoelectric line from the right ventricular coil-can (i.e., far-field) electrogram during oversensing of noise caused by a lead failure. The baseline measure was defined as the product of the sum (mV) and standard deviation (mV) of the voltage samples for a 188-ms window centered on each sensed electrogram. If the minimum baseline measure of the last 12 beats was <0.35 mV-mV, then the detected rhythm was considered noise due to a lead failure. The first ICD-detected episode of lead failure and inappropriate detection from 24 ICD patients with a pace/sense lead failure and all ventricular arrhythmias from 56 ICD patients without a lead failure were selected. The stored data were analyzed to determine the sensitivity and specificity of the algorithm to detect lead failures. RESULTS: The minimum baseline measure for the 24 lead failure episodes (0.28 +/- 0.34 mV-mV) was smaller than the 135 ventricular tachycardia (40.8 +/- 43.0 mV-mV, P <.0001) and 55 ventricular fibrillation episodes (19.1 +/- 22.8 mV-mV, P <.05). A minimum baseline <0.35 mV-mV threshold had a sensitivity of 83% (20/24) with a 100% (190/190) specificity. CONCLUSION: A baseline measure of the far-field electrogram had a high sensitivity and specificity to detect lead failure noise compared with ventricular tachycardia or fibrillation.

Aged↗

PubMed2006

Continuous template collection and updating for electrogram morphology discrimination in implantable cardioverter defibrillators.

INTRODUCTION: Electrogram morphology analysis improves discrimination of supraventricular tachycardias (SVTs) from ventricular tachycardias (VTs) in implantable cardioverter defibrillators (ICDs), but electrogram morphology may change with lead maturation, drugs, or disease progression. We report the clinical performance of an automatic algorithm that creates and updates templates from non-paced, slow rhythm and continuously checks the quality of the template used for arrhythmia discrimination. METHODS AND RESULTS: We studied this algorithm in 193 patients with single-chamber ICDs (Marquis VR, Medtronic Inc., Minneapolis, MN, USA). Of the 112 patients who completed 6-month follow-up, 99.1% of the patients had > or =1 automatic template created. Match scores between template and ongoing rhythm are computed using Haar Wavelets. Of the 435 automatic templates evaluated at follow-up, 423 (97.2%) had a median match score > or =70%. Intrinsic rhythm at 1 month had significantly higher match scores (P < 0.001) with automatic templates (90.3 +/- 7.0%) than with manual templates (85.7 +/- 10.9%) generated at pre-hospital discharge (PHD). The percentage of appropriately rejected SVTs was slightly higher with the automatic template (280/339 episodes) than with the manual template at PHD (272/339 episodes) while the Wavelet detection of VT was the same (218/220 episodes). CONCLUSIONS: In patients receiving ICDs, the automatic templates were successfully created during a 6-month follow-up period, and consistently matched the patients' intrinsic rhythm at the nominal match threshold. Both early (<1 month postimplant) and late (1- to 3-month follow-up period) changes in electrogram morphology were identified, confirming the need for automatic template updating.

Algorithms↗

PubMed2005

Atrial response to ventricular antitachycardia pacing discriminates mechanism of 1:1 atrioventricular tachycardia.

BACKGROUND: Inappropriate shocks from implantable cardioverter defibrillators (ICD) remain a significant clinical problem despite device discrimination algorithms. The atrial response to antitachycardia pacing (ATP) may determine the mechanism of 1:1 A:V tachycardia. METHODS: For this study we refer to sinus tachycardia, atrial tachycardia (AT), atrial fibrillation, and flutter as atrial tachycardia (AT), and all other tachycardia as "non-AT." Three atrial response patterns during the burst of ATP were determined. The atrial cycle length (ACL) may be unchanged (type 1) indicating AT. The ACL may show variation during ATP (type 2) indicating variable VA block and does not discriminate between an AT and a non-AT mechanism, in which case a default diagnosis of non-AT is made. The ACL may accelerate to the ATP cycle length (type 3) indicating entrainment. A VAAV response at the end of ATP was considered diagnostic of AT (type 3A) whereas a VAV or VVA response was considered a non-AT mechanism (type 3B). This algorithm was applied to ICD tracings from 68 episodes of spontaneous 1:1 A:V tachycardia that had 136 sequences of ATP administered. The rhythm "truth" was determined by consensus of two experienced clinicians. RESULTS: The algorithm correctly identified AT with a sensitivity of 71.9% (95% CI: 67.1-73.6), and specificity of 95% (83.5-99.1). The PPV was 97.2% (90.9-99.5), and NPV 58.5% (51.4-61.0). Kappa was 0.57 (0.43-0.62). If used clinically the algorithm would have aborted 53.3% (8/15) of inappropriate shocks delivered into an AT-mechanism tachycardia and would not have withheld a shock for any episode of VT. CONCLUSION: Analysis of atrial response patterns during and after ventricular ATP can successfully discriminate tachycardia mechanism and may reduce inappropriate ICD shocks.

Algorithms↗

PubMed2003

An adaptive interval-based algorithm for withholding ICD therapy during sinus tachycardia.

Avoiding inappropriate ICD therapy during supraventricular tachycardia (SVT) while assuring 100% sensitivity for VT/VF remains a challenge. Inappropriate VT/VF therapy during sinus tachycardia (ST) is particularly distressing to the patient because the full sequence of ICD therapies is often delivered. ST or 1:1 atrial tachycardia (AT) with long PR intervals and ST or AT with atrial oversensing of far-field R waves cause the majority of inappropriate therapy in the Medtronic GEM DR (Model 7271) ICD. The goals of the present effort were to define an adaptive interval-based algorithm for withholding VT/VF therapy in dual chamber ICDs during ST and to compare performance of the adaptive algorithm with that of the original ST withholding algorithm in the GEM DR. The adaptive algorithm uses a combination of 1:1 atrial to ventricular conduction pattern, changes in RR intervals and changes in intrinsic PR intervals to establish evidence for or against the presence of ST. Performances of the adaptive and original ST withholding algorithms were compared on 3 databases collected by implanted GEM DR devices. The first database included 684 spontaneous VT/VF episodes. The second database included 216 spontaneous SVT episodes that received inappropriate VT/VF therapy. These databases included up to 2,000 atrial or ventricular sensed or paced events preceding the spontaneous tachycardias. The third database included 320 spontaneous ST/AT episodes for which therapy was appropriately withheld by the GEM DR. Performance of the adaptive algorithm on the third database was predicted rather than directly computed because of record length limitations. VT/VF therapy was classified as "withheld" if evidence of ST remained high for one algorithm (i.e., at least 7 more beats to VT/VF detection) at the point of VT/VF detection by the other algorithm. For the 684 true VT/VF episodes, the original algorithm withheld VT/VF therapy in 5 episodes and the adaptive algorithm withheld VT/VF therapy in 3 episodes. The 95% confidence interval for the difference in VT/VF sensitivity between the adaptive and original algorithms was [-0.5 to + 1.1%]. Twelve of the 320 ST/AT episodes (3.8%) that were appropriately classified by the original algorithm were predicted to receive inappropriate therapy by the adaptive algorithm. However, relative to the original algorithm, the adaptive algorithm appropriately withheld VT/VF therapy for 76 of 216 true SVT episodes (i.e., incremental specificity of 35.2%). For the specific SVT episodes that were the targets for improvement by the adaptive ST algorithm (ST/AT with long PR intervals and ST/AT with intermittent atrial oversensing of far-field R waves), the adaptive algorithm reduced inappropriate therapy by 63.2%.

Algorithms↗

PubMed2002

Discrimination of ventricular tachycardia from supraventricular tachycardia by a downloaded wavelet-transform morphology algorithm: a paradigm for development of implantable cardioverter defibrillator detection algorithms.

INTRODUCTION: Present implantable cardioverter defibrillators (ICDs) have algorithms that discriminate supraventricular tachycardia (SVT) from ventricular tachycardia (VT). One type of algorithm is based on differences in morphology of ventricular electrograms during VT and SVT. Prior SVT-VT discrimination algorithms have not undergone real-time evaluation in ambulatory patients until they were incorporated permanently into ICDs. This approach may result in incomplete testing of electrogram morphology algorithms because they are influenced by posture, activity, and electrogram maturation. We downloaded software into implanted ICDs to study a novel algorithm that compares morphologies of baseline and tachycardia electrograms based on differences between corresponding coefficients of their wavelet transforms. This comparison is expressed as a match-percent score. METHODS AND RESULTS: In 23 patients, we downloaded the wavelet algorithm into implanted ICDs to assess the temporal and postural stability of baseline electrograms as measured by this algorithm and its accuracy for SVT-VT discrimination. Median follow-up was 6 months. Software was downloaded into all ICDs without altering other device functions. With few exceptions, percent template match in baseline rhythm was stable with changes in body position, rest versus walking, isometric exercise, and over time (1 and 3 months). Using the nominal match-percent threshold of 70%, sensitivity for detection of 38 VTs was 100%. Specificity for rejection of 65 SVTs was 78%. SVTs were rejected for a total of 2.7 hours. Inappropriate detections of SVT as VT were caused by electrogram truncation, myopotential interference with low-amplitude electrograms, waveform alignment error, and rate-dependent aberrancy. The first three accounted for 69% of inappropriate detections and could have been prevented by optimal programming. The optimal match-percent threshold was 60% to 70% based on a receiver-operator characteristic curve. After shocks, the median time for baseline electrogram morphology to normalize was 85 seconds. CONCLUSION: The wavelet morphology algorithm has high sensitivity for VT detection. Inappropriate detections of SVT as VT may be reduced by optimal programming. Downloadable software permits evaluation of new algorithms in implanted ICDs.

Aged↗