Low inappropriate shock rates in patients with single- and dual/triple-chamber implantable cardioverter-defibrillators using a novel suite of detection algorithms: PainFree SST trial primary results
Bibliographic record
Abstract
BACKGROUND: The benefits of implantable cardioverter-defibrillators (ICDs) have been well demonstrated in many clinical trials, and ICD shocks for ventricular tachyarrhythmias save lives. However, inappropriate and unnecessary shock delivery remains a significant clinical issue with considerable consequences for patients and the healthcare system. OBJECTIVE: The purpose of the PainFree SmartShock Technology (SST) study was to investigate new-generation ICDs to reduce inappropriate and unnecessary shocks through novel discrimination algorithms with modern programming strategies. METHODS: This prospective, multicenter clinical trial enrolled 2790 patients with approved indication for ICD implantation (79% male, mean age 65 years; 69% primary prevention indication, 27% single-chamber ICD, 33% replacement or upgrade). Patients were followed for a minimum of 12 months, and mean follow-up was 22 months. The primary end-point of the study was the percentage of patients remaining free of inappropriate shocks at 1 year postimplant, analyzed separately for dual/triple-chamber ICDs (N = 2019) and single-chamber ICDs (N = 751). RESULTS: The inappropriate shock rate at 1 year was 1.5% for patients with dual/triple-chamber ICDs and 2.5% for patients with single-chamber devices. Two years postimplant, the inappropriate shock rate was 2.8% for patients with dual-/triple chamber ICDs and 3.7% for those with single-chamber ICDs. The most common cause of an inappropriate shock in both groups was atrial fibrillation or flutter. CONCLUSION: In a large patient cohort receiving ICDs for primary or secondary prevention, the adoption of novel enhanced detection algorithms in conjunction with routine implementation of modern programming strategies led to a very low inappropriate shock rate.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".