Evidence map›Paper›PMID 38560375›Full record

ArticleHeart rhythm O22024

Predictive utility of the impedance drop on AF recurrence using digital intraprocedural data linked to electronic health record data.

Paul Coplan, Amit Doshi, Mingkai Peng, Yariv Amos, Mati Amit, Don Yungher, Rahul Khanna, Liat Tsoref

Open access · goldAbstract read
In one paragraph

Article in Heart rhythm O2, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors at 2 institutions in 1 country.

Paul CoplanMedTech Epidemiology and Real-World Data Sciences, Office of the Chief Medical Officer, Johnson & Johnson, New Brunswick, New Jersey.
Amit DoshiMercy Hospital, St. Louis, Missouri.
Mingkai PengMedTech Epidemiology and Real-World Data Sciences, Office of the Chief Medical Officer, Johnson & Johnson, New Brunswick, New Jersey.
Yariv AmosBiosense Webster LTD, Haifa Technology Center, Israel.
Mati AmitBiosense Webster LTD, Haifa Technology Center, Israel.
Don YungherBiosense Webster LTD, Haifa Technology Center, Israel.
Rahul KhannaMedTech Epidemiology and Real-World Data Sciences, Office of the Chief Medical Officer, Johnson & Johnson, New Brunswick, New Jersey.
Liat TsorefBiosense Webster LTD, Haifa Technology Center, Israel.
Johnson & Johnson (United States) · USMercy Hospital · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Local impedance drop in cardiac tissue during catheter ablation may be a valuable measure to guide atrial fibrillation (AF) ablation procedures for greater effectiveness. Objective: The study sought to assess whether local impedance drop during catheter ablation to treat AF predicts 1-year AF recurrence and what threshold of impedance drop is most predictive. Methods: We identified patients with AF undergoing catheter ablation in the Mercy healthcare system. We downloaded AF ablation procedural data recorded by the CARTO system from a cloud-based analytical tool (CARTONET) and linked them to individual patient electronic health records. Average impedance drops in anatomical region of right and left pulmonary veins were calculated. Effectiveness was measured by a composite outcome of repeat ablation, AF rehospitalization, direct current cardioversion, or initialization of a new antiarrhythmic drug post-blanking period. The association between impedance drop and 1-year AF recurrence was assessed by logistic regression adjusting for demographics, clinical, and ablation characteristics. Bootstrapping was used to determine the most predictive threshold for impedance drop based on the Youden index. Results: Among 242 patients, 23.6% (n = 57) experienced 1-year AF recurrence. Patients in the lower third vs upper third of average impedance drop had a 5.9-fold (95% confidence interval [CI] 1.81-21.8) higher risk of recurrence (37.0% vs 12.5%). The threshold of 7.2 Ω (95% CI 5.75-7.7 Ω) impedance drop best predicted AF recurrence, with sensitivity of 0.73 and positive predictive value of 0.33. Patients with impedance drop ≤7.2 Ω had 3.5-fold (95% CI 1.39-9.50) higher risk of recurrence than patients with impedance drop >7.2 Ω, and there was no statistical difference in adverse events between the 2 groups of patients. Sensitivity analysis on right and left wide antral circumferential ablation impedance drop was consistent. Conclusion: Average impedance drop is a strong predictor of clinical success in reducing AF recurrence but as a single criterion for predicting recurrence only reached 73% sensitivity and 33% positive predictive value.

Indexed as

Atrial fibrillationCARTONETImpedance dropLinkageRadiofrequency catheter ablation

Identifiers

PMID38560375
PMCPMC10980921
OpenAlexW4391483492

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.