Safety, efficacy, and performance of new discrimination algorithms to reduce inappropriate and unnecessary shocks: the PainFree SST clinical study design
Bibliographic record
Abstract
INTRODUCTION: Implantable cardioverter defibrillator (ICD) shock therapy improves survival of patients at risk for sudden cardiac death. The high sensitivity of ICDs to detect tachycardia events is accompanied by reduced specificity resulting in inappropriate and unnecessary shocks. Up to 30% of ICD patients may experience inappropriate shocks, which are most commonly caused by lead noise, oversensing of T-waves, and supraventricular tachycardias. The new Protecta ICD and cardiac resynchronization therapy devices have been designed to minimize inappropriate and unnecessary shocks through novel SmartShock(TM) technology algorithms targeting these causes. METHODS: The PainFree SST study is a prospective, multicentre clinical trial, which will be conducted in two consecutive phases. Phase I will assess safety and any delay that may arise in ventricular fibrillation (VF) arrhythmia detection time using new algorithms. Phase II will evaluate reduction of inappropriate and unnecessary shocks at 1 year of follow-up. Additional objectives will include Quality of Life, healthcare utilization, safety of extending the ventricular tachyarrhythmia/VF interval detection duration (18 out of 24 vs. 30 out of 40 intervals), and reasons for inappropriate shock. Up to 2000 subjects in 150 centres worldwide will be enrolled with a follow-up of at least 1 year. Subjects enrolled in Phase I will continue in Phase II of the study and data from all enrolled subjects will contribute to the analysis of Phase II objectives. CONCLUSION: Inappropriate and unnecessary shock delivery remains a significant clinical issue for patients receiving device therapies, which has considerable consequences for patients and the healthcare system. The PainFree SST study will investigate the ability of new algorithms to reduce inappropriate shocks. Results from this study are expected in mid-2013.
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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 | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".