Subcutaneous implantable cardioverter-defibrillator generator removal: a multicenter analysis
Bibliographic record
Abstract
Abstract Funding Acknowledgements Type of funding sources: Public hospital(s). Main funding source(s): University Hospital Cologne Introduction The subcutaneous implantable cardioverter-defibrillator (S-ICD) was a significant recent advance in sudden cardiac death prevention. Device-related complications, including infection or lead fracture, or changes in the medical status, such as necessity for cardiac pacing, may necessitate device removal. (1) Real-world multicenter data on S-ICD removal are sparse. Purpose This multicenter analysis sought to assess the incidence and indications for S-ICD generator removal. Methods Retrospective data and the most recent follow-up data on S-ICD devices implanted at 14 participating centers in Europe, the US and Canada, and information on subsequent device removal was submitted to an online database (2). Limited baseline patient information and the incidence of and reasons for S-ICD generator removal or failure were reported. Premature device removal was defined as device removal for any reason other than regular battery depletion (>5 years of longevity). Results Data from 1106 devices was analyzed. Devices were implanted between 15.10.2009 and 16.04.2021 at the participating centers. The first-generation model 1010 generator was implanted in 55 (5%), the newer models A209 or A219 were implanted in 457 (41.3%) and 594 (53.7%) respectively. Patients were aged 46.7±16.1 years and the indication for ICD therapy was primary prevention in 616 (55.7%) and secondary prevention in 484 (43.8%). During the follow-up period, 37 (3%) patients deceased, and 96 patients were excluded from the analysis due to lack of follow-up (<4 weeks). Premature device removal was performed in 103 (9.3%) during the follow-up period of 31.3±19.5 months. The most common reasons for removal were battery depletion (52%), of which premature, defined as <5 years longevity, depletion accounted for 57%, upgrade to transvenous ICD or CRT (17%) and infection (11%). Other indications for removal included heart transplantation (4%) or ventricular assist device implantation (1.5%), noise detection or inappropriate shocks (6.5%), and patient discomfort (0.7%). (see figure 1) Regular battery depletion occurred in 34 devices after 69.2±5.3 months. There was no difference in device longevity between the different generator models (1010, A209, and A219). There were no significant differences between the different generator models with respect to indications for explanation. Conclusions In this real-world retrospective multicenter analysis, premature S-ICD generator removal was necessary in 9.3% of patients. Premature battery depletion, infections, and the need for pacing were the most common indications for unplanned generator removal.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".