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Record W2520328948 · doi:10.1016/j.joa.2016.08.002

Loss of atrial pacing in a patient with a dual‐chamber permanent pacemaker: What is the mechanism?

2016· article· en· W2520328948 on OpenAlexaff
Enes Elvin Gül, Usama Boles, Fariha Sadiq Ali, Hoshiar Abdollah

Bibliographic record

VenueJournal of Arrhythmia · 2016
Typearticle
Languageen
FieldMedicine
TopicCardiac pacing and defibrillation studies
Canadian institutionsKingston General HospitalQueen's University
Fundersnot available
KeywordsMedicineCardiologyInternal medicineArtificial cardiac pacemakerLead (geology)Heart blockElectrocardiography

Abstract

fetched live from OpenAlex

A 56-year-old woman with prior history of dual-chamber permanent pacemaker (PPM) implantation (ELA Medical Symphony DR 2550, ELA Medical, Montrouge, France) for symptomatic complete heart block presented to the Cardiac Rhythm Device Clinic because of increased shortness of breath. A surface 12-lead electrocardiogram (ECG) showed no pacing activity in the atrium (Fig. 1). What is the mechanism? Twelve-lead ECG showing ventricular pacing and complete heart block. Atrial oversensing due to lead integrity failure (fracture, insulation failure) or connector problems between the header and the lead. Atrial lead displacement: no evidence of micro- or macro-displacement was found on chest radiography. Asynchronous mode of pacing (VOO) due to magnet or programming: device interrogation ruled out this differential diagnosis. The device was fully functional in DDDR mode. Battery end of life triggering VVI mode with underlying complete heart block. Differential diagnoses such as atrial oversensing and lead failures can manifest together, since lead or connector problems can cause oversensing of non-physiologic cardiac signals. In this particular case, all other possible causes were ruled out. Pacemaker interrogation was performed (Fig. 2) and showed atrial mode switch (AMS) due to atrial oversensing. Appropriate AMS refers to the ability of the pacemaker to change automatically from one mode to another in response to atrial tachyarrhythmia. The atrial lead revealed no capture at 5.0 V/1.5 ms, P-wave sensing of 0.7 mV, and an impedance level above 3000 Ω. Intracardiac electrocardiograms obtained from the pacemaker interrogation showing atrial oversensing due to atrial noise and the consequently activated mode switch. The patient had DDD pacing when we performed PM interrogation. Diagnosis obtained from the device revealed atrial arrhythmia and mode switches due to atrial tachyarrhythmia. In addition, the asynchronous pacing mode or magnet mode was adjusted to 80 ppm. However, in our patient, VP rate was 898 ms (66 ppm), and no magnet mode was noted in the device log that was consistent with the AMS rate. This pacemaker is made by Sorin (ELA Medical Symphony DR 2550, ELA Medical, Montrouge, France) and is equipped with a rate response function that is regulated by a minute ventilation sensor. In ELA devices, minute ventilation is measured by using the RA lead with an 8-Hz low-amplitude current. If the RA lead is fractured, the pacemaker automatically increases voltage to maintain the current, and the sensing of this high voltage output prompts an auto-mode switch. In fact, the regular noise in this case was 8 Hz. Therefore, we believe that this was the actual cause of the auto-mode switch. In conclusion, 12-lead ECG interpretation is important in recognizing possible PPM-related problems. All authors declare that the manuscript, as submitted or its content in another version, is not under consideration for publication elsewhere and will not be submitted elsewhere, until a final decision is made by the editors of the Journal of Arrhythmia. All authors declare no conflict of interest related to this study. All authors have made substantive contributions to the study, and all authors endorse the data and conclusions. Nevertheless, confirmation of informed patient consent for publication was obtained.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.474
Threshold uncertainty score0.205

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.257
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2016
Admission routes1
Has abstractyes

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