Should they stay, or should they go: Do we need to remove the old cardiac implantable electronic device if a new system is required on the contralateral side?
Bibliographic record
Abstract
Background: Ipsilateral approach in patients requiring cardiac implantable electronic device (CIED) revision or upgrade may not be feasible, primarily due to vascular occlusion. If a new CIED is implanted on the contralateral side, a common practice is to explant the old CIED to avoid device interaction. Objective: The purpose of this study was to assess a conservative approach of abandoning the old CIED after implanting a new contralateral device. Methods: We used an artificial intelligence algorithm to analyze postimplant chest radiographs to identify those with multiple CIEDs. Outcomes of interest included device interaction, abandoned CIED elective replacement indicator (ERI) behavior, subsequent programming changes, and explant of abandoned CIED. Theoretical risk of infection with removal of abandoned CIED was estimated using a validated scoring system. Results: Among 12,045 patients, we identified 40 patients with multiple CIEDs. Occluded veins were the most common indication for contralateral implantation (n = 27 [67.5%]). Fifteen abandoned CIEDs reached ERI, with 4 reverting to VVI 65. One patient underwent explant due to device interaction, and 2 required device reprogramming. Of 32 patients with an implantable cardioverter-defibrillator, 8 (25%) had treated ventricular arrhythmia. There were no failed or inappropriate therapies due to interaction. Eighteen patients (45%) had hypothetical >1% annual risk of hospitalization for device infection if the abandoned CIED had been explanted. Conclusion: In patients requiring new CIED implant on the contralateral side, abandoning the old device is feasible. This approach may reduce the risk of infection and concerns regarding abandoned leads and magnetic resonance imaging scans. Knowledge of ERI behavior is essential to avoid device interactions.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".