Trends in implantable cardioverter-defibrillator programming practices and its impact on therapies: Insights from a North American Remote Monitoring Registry 2007–2018
Bibliographic record
Abstract
BACKGROUND: Recent evidence has revealed the utility of prolonged arrhythmia detection duration and increased rate cutoff to reduce implantable cardioverter-defibrillator (ICD) therapies. Data on real-world trends in ICD programming and its impact on outcomes are limited. OBJECTIVE: The purpose of this study was to evaluate trends in ICD programming and its impact on ICD therapy using a large remote monitoring database. METHODS: A retrospective analysis of patients with ICD implanted from 2007 to 2018 was conducted using the de-identified Medtronic CareLink database. Data on ICD programming (number of intervals to detection [NID] and therapy rate cutoff) and delivered ICD therapies were collected. RESULTS: Among 210,810 patients, the proportion programmed to a rate cutoff of ≥188 beats/min increased from 41% to 49% and an NID of ≥30/40 increased from 17% to 67% before May 2013 vs after February 2016. Programming to a rate cutoff of ≥188 beats/min, a ventricular fibrillation (VF) NID of ≥30/40, or a combined rate cutoff of ≥188 beats/min and VF NID of ≥30/40 were associated with reductions in ICD therapy. The largest reductions in ICD therapy occurred when the combination of rate cutoff ≥ 188 beats/min and VF NID ≥ 30/40 was programmed (antitachycardia pacing: hazard ratio [HR] 0.35; 95% confidence interval [CI] 0.34-0.36; P < .001; shocks: HR 0.67; 95% CI 0.65-0.69; P < .001; and antitachycardia pacing/shocks: HR 0.43; 95% CI 0.42-0.44; P < .001). CONCLUSION: Despite evidence supporting the use of prolonged detection duration and high rate cutoff, implementation of shock reduction programming strategies in real-world clinical practice has been modest. The use of evidence-based ICD programming is associated with reduced ICD shocks over long-term follow-up.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".