35 research outputs found

    Effect of Contact Force on Pulsed Field Ablation Lesions in Porcine Cardiac Tissue.

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    BACKGROUND Contact force has been used to titrate lesion formation for radiofrequency ablation. Pulsed Field Ablation (PFA) is a field-based ablation technology for which limited evidence on the impact of contact force on lesion size is available. METHODS Porcine hearts (n=6) were perfused using a modified Langendorff set-up. A prototype focal PFA catheter attached to a force gauge was held perpendicular to the epicardium and lowered until contact was made. Contact force was recorded during each PFA delivery. Matured lesions were cross-sectioned, stained, and the lesion dimensions measured. RESULTS A total of 82 lesions were evaluated with contact forces between 1.3 g and 48.6 g. Mean lesion depth was 4.8 ± 0.9 mm (standard deviation), mean lesion width was 9.1 ± 1.3 mm and mean lesion volume was 217.0. ± 96.6 mm3 . Linear regression curves showed an increase of only 0.01 mm in depth (Depth = 0.01*Contact Force + 4.41, R2 = 0.05), 0.03 mm in width (Width = 0.03*Contact Force + 8.26, R2 = 0.13) for each additional gram of contact force, and 2.20 mm3 in volume (Volume = 2.20*Contact Force + 162, R2 = 0.10). CONCLUSIONS Increasing contact force using a bipolar, biphasic focal PFA system has minimal effects on acute lesion dimensions in an isolated porcine heart model and achieving tissue contact is more important than the force with which that contact is made. This article is protected by copyright. All rights reserved

    Infectious consequences of hematoma from cardiac implantable electronic device procedures and the role of the antibiotic envelope: A WRAP-IT trial analysis.

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    Hematoma is a complication of cardiac implantable electronic device (CIED) procedures and may lead to device infection. The TYRX antibacterial envelope reduced major CIED infection by 40% in the randomized WRAP-IT (World-wide Randomized Antibiotic Envelope Infection Prevention Trial) study, but its effectiveness in the presence of hematoma is not well understood.The purpose of this study was to evaluate the incidence and infectious consequences of hematoma and the association between envelope use, hematomas, and major CIED infection among WRAP-IT patients.All 6800 study patients were included in this analysis (control 3429; envelope 3371). Hematomas occurring within 30 days postprocedure (acute) were characterized and grouped by study treatment and evaluated for subsequent infection risk. Data were analyzed using Cox proportional hazard regression modeling.Acute hematoma incidence was 2.2% at 30 days, with no significant difference between treatment groups (envelope vs control hazard ratio [HR] 1.15; 95% confidence interval [CI] 0.84-1.58; P = .39). Through all follow-up, the risk of major infection was significantly higher among control patients with hematoma vs those without (13.1% vs 1.6%; HR 11.3; 95% CI 5.5-23.2; P.001). The risk of major infection was significantly lower in the envelope vs control patients with hematoma (2.5% vs 13.1%; HR 0.18; 95% CI 0.04-0.85; P = .03).The risk of hematoma was 2.2% among WRAP-IT patients. Among control patients, hematoma carried a11-fold risk of developing a major CIED infection. This risk was significantly mitigated with antibacterial envelope use, with an 82% reduction in major CIED infection among envelope patients who developed hematoma compared to control

    Risk Factors for CIED Infection After Secondary Procedures

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    OBJECTIVES This study aimed to identify risk factors for infection after secondary cardiac implantable electronic device (CIED) procedures. BACKGROUND Risk factors for CIED infection are not well defined and techniques to minimize infection lack supportive evidence. WRAP-IT (World-wide Randomized Antibiotic Envelope Infection Prevention trial), a large study that assessed the safety and efficacy of an antibacterial envelope for CIED infection reduction, offers insight into procedural details and infection prevention strategies. METHODS This analysis included 2,803 control patients from the WRAP-IT trial who received standard preoperative antibiotics but not the envelope (44 patients with major infections through all follow-up). A multivariate least absolute shrinkage and selection operator machine learning model, controlling for patient characteristics and procedural variables, was used for risk factor selection and identification. Risk factors consistently retaining predictive value in the model (appeared >10 times) across 100 iterations of imputed data were deemed significant. RESULTS Of the 81 variables screened, 17 were identified as risk factors with 6 being patient/device-related (nonmodifiable) and 11 begin procedure-related (potentially modifiable). Patient/device-related factors included higher number of previous CIED procedures, history of atrial arrhythmia, geography (outside North America and Europe), device type, and lower body mass index. Procedural factors associated with increased risk included longer procedure time, implant location (non-left pectoral subcutaneous), perioperative glycopeptide antibiotic versus nonglycopeptide, anticoagulant, and/or antiplatelet use, and capsulectomy. Factors associated with decreased risk of infection included chlorhexidine skin preparation and antibiotic pocket wash. CONCLUSIONS In WRAP-IT patients, we observed that several procedural risk factors correlated with infection risk. These results can help guide infection prevention strategies to minimize infections associated with secondary CIED procedures. (J Am Coll Cardiol EP 2022;8:101-111) (c) 2022 The Authors. Published by Elsevier on behalf of the American College of Cardiology Foundation. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

    the world wide randomized antibiotic envelope infection prevention wrap it trial long term follow up

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    Abstract Background The WRAP-IT trial reported a 40% reduction in major CIED infection within 12 months of the procedure with the antibacterial-eluting envelope (TYRX). Objective This report describes the longer-term (>12 months) envelope effects on infection reduction and complications. Methods All trial patients that underwent CIED replacement, upgrade, revision, or initial CRT-D implant received standard-of-care infection prophylaxis and were randomized 1:1 to receive the envelope or not. CIED infection incidence, and procedure and system-related complications were characterized through all follow-up (36 months) using Cox proportional hazard regression modeling. Results In total, 6800 patients received their intended randomized treatment (3371 envelope; 3429 control; mean follow-up 21.0±8.3 months). Major CIED-related infection occurred in 32 envelope patients and 51 control patients (KM estimate, 1.3% vs. 1.9%; HR: 0.64, 95% CI: 0.41-0.99; P=0.046). Any CIED-related infection occurred in 57 envelope patients and 84 control patients (KM estimate, 2.1% vs. 2.8%; HR: 0.69, 95% CI: 0.49-0.97; P=0.030). System- or procedure-related complications occurred in 235 envelope patients and 252 control patients (KM estimate, 8.0% vs. 8.2%; HR, 0.95, 95% CI: 0.79-1.13; P Conclusions The effects of the TYRX envelope in reducing the risk of CIED infection are sustained beyond the first year post-procedure, without increased risk of complication

    Performance of First Pacemaker to Use Smart Device App for Remote Monitoring

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    BACKGROUND: High adherence to remote monitoring (RM) in pacemaker (PM) patients improves outcomes; however, adherence remains suboptimal. Bluetooth low-energy (BLE) technology in newer-generation PMs enables communication directly with patient-owned smart devices using an app without a bedside console. OBJECTIVE: To evaluate the success rate of scheduled RM transmissions using the app compared to other RM methods. METHODS: The BlueSync Field Evaluation was a prospective, international cohort evaluation, measuring the success rate of scheduled RM transmissions using a BLE PM or cardiac resynchronization therapy PM coupled with the MyCareLink Heart app. App transmission success was compared to 3 historical “control” groups from the Medtronic de-identified CareLink database: (1) PM patients with manual communication using a wand with a bedside console (PM manual transmission), (2) PM patients with wireless automatic communication with the bedside console (PM wireless); (3) defibrillator patients with similar automatic communication (defibrillator wireless). RESULTS: Among 245 patients enrolled (age 64.8±15.6 years, 58.4% men), 953 transmissions were scheduled through 12 months, of which 902 (94.6%) were successfully completed. In comparison, transmission success rates were 56.3% for PM manual transmission patients, 77.0% for PM wireless patients, and 87.1% for defibrillator wireless patients. Transmission success with the app was superior across matched cohorts based on age, sex, and device type (single vs dual vs triple chamber). CONCLUSION: The success rate of scheduled RM transmissions was higher among patients using the smart device app compared to patients using traditional RM using bedside consoles. This novel technology may improve patient engagement and adherence to RM

    How to diagnose and manage patients with cardiac implantable electronic device infections

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    Abstract Over the last two decades, there has been a surge in the number of Cardiac Implantable Electronic Device (CIED) implantation. These devices improve the quality of life and survival among certain cardiac patients. However, this benefit might be affected by device complications and one of the most important ones is CIED infection as it carries significant morbidity and mortality. CIED infection can present as a device pocket infection or endovascular infection and its diagnosis could be challenging. In general, management of CIED infection involves device removal and antibiotic therapy and requires collaboration between different clinical teams. Future efforts and research should focus on measures to prevent the occurrence of this outcome

    Atrial Fibrillation Detection With Wearable Devices

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    Applications of machine learning in decision analysis for dose management for dofetilide.

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    BACKGROUND:Initiation of the antiarrhythmic medication dofetilide requires an FDA-mandated 3 days of telemetry monitoring due to heightened risk of toxicity within this time period. Although a recommended dose management algorithm for dofetilide exists, there is a range of real-world approaches to dosing the medication. METHODS AND RESULTS:In this multicenter investigation, clinical data from the Antiarrhythmic Drug Genetic (AADGEN) study was examined for 354 patients undergoing dofetilide initiation. Univariate logistic regression identified a starting dofetilide dose of 500 mcg (OR 5.0, 95%CI 2.5-10.0, p<0.001) and sinus rhythm at the start of dofetilide loading (OR 2.8, 95%CI 1.8-4.2, p<0.001) as strong positive predictors of successful loading. Any dose-adjustment during loading (OR 0.19, 95%CI 0.12-0.31, p<0.001) and a history coronary artery disease (OR 0.33, 95%CI 0.19-0.59, p<0.001) were strong negative predictors of successful dofetilide loading. Based on the observation that any dose adjustment was a significant negative predictor of successful initiation, we applied multiple supervised approaches to attempt to predict the dose adjustment decision, but none of these approaches identified dose adjustments better than a probabilistic guess. Principal component analysis and cluster analysis identified 8 clusters as a reasonable data reduction method. These 8 clusters were then used to define patient states in a tabular reinforcement learning model trained on 80% of dosing decisions. Testing of this model on the remaining 20% of dosing decisions revealed good accuracy of the reinforcement learning model, with only 16/410 (3.9%) instances of disagreement. CONCLUSIONS:Dose adjustments are a strong determinant of whether patients are able to successfully initiate dofetilide. A reinforcement learning algorithm informed by unsupervised learning was able to predict dosing decisions with 96.1% accuracy. Future studies will apply this algorithm prospectively as a data-driven decision aid
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