Use of an Intracardiac Electrogram Eliminates the Need for a Surface ECG during Implantable Cardioverter‐Defibrillator Follow‐Up
Bibliographic record
Abstract
BACKGROUND: A surface electrocardiogram (SECG) for pacing threshold measurements during routine implantable cardioverter-defibrillator (ICD) follow-up can be cumbersome. This study evaluated the use of an intrathoracic far-field electrogram (EGM) derived between the Can and superior vena cava (SVC) electrode -- the Leadless electrocardiogram (LLECG), in dual chamber ICDs in performing pacing threshold tests. METHODS: The LLECG was evaluated prospectively during atrial and ventricular pacing threshold testing as a substudy of the Comparison of Empiric to Physician-Tailored Programming of Implantable Cardioverter-Defibrillators trial (EMPIRIC) in which dual chamber ICDs were implanted in 888 patients. Threshold tests were conducted at 1 volt by decrementing the pulse width. Follow-up at three months compared pacing thresholds measured using LLECG with those using Lead I of the surface ECG (SECG). The timesaving afforded by LLECG was assessed by a questionnaire. RESULTS: The median threshold difference between LLECG and SECG measurements for both atrial (0.00 ms, P = 0.90) and ventricular (0.00 ms, P = 0.34) threshold tests were not significant. Ninety percent of atrial and ventricular threshold differences were bounded by +/- 0.10 ms and -0.10 to +0.04 ms, respectively. We found that 99% of atrial and ventricular thresholds tests at six and 12 months attempted using LLECG were successfully completed. The questionnaire indicated that 65% of healthcare professionals found LLECG to afford at least some timesaving during device follow-ups. CONCLUSION: Routine follow-up can be performed reliably and expeditiously in dual chamber Medtronic (Minneapolis, MN, USA) ICDs using LLECG alone, resulting in overall timesaving.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".