Rationale and Design of the OPTION Study: Optimal Antitachycardia Therapy in ICD Patients without Pacing Indications
Bibliographic record
Abstract
BACKGROUND: Implantable cardioverter-defibrillators (ICDs) represent the treatment of choice for primary and secondary prevention of sudden cardiac death but ICD therapy is also plagued by inappropriate shocks due to supraventricular tachyarrhythmias. Dual-chamber (DC) ICDs are considered to exhibit an enhanced discrimination performance in comparison to single-chamber (SC) ICDs, which results in reduction of inappropriate detections in a short- to mid-term follow-up. Comparative data on long-term follow-up and especially on inappropriate shocks are limited. METHODS: The aim of the OPTION study is to assess whether an optimized treatment with DC ICDs improves patient outcome and decreases the rate of inappropriate shocks in comparison to SC ICDs. DC ICD therapy optimization is achieved by optimal customizing of antitachycardia therapy parameters, activation of discrimination algorithms, antitachycardia pacing in the slow ventricular tachycardia zone, and avoidance of right ventricular pacing with the SafeR algorithm mode. The OPTION study, a prospective, multicenter, randomized, single-blinded, parallel study, will randomize 450 patients on a 1:1 allocation to either an SC arm with backup pacing at VVI 40 beats per minute (bpm) or to the DC arm with SafeR pacing at 60 bpm. Patients will be followed for 27 months. Primary outcome measure is the time to first occurrence of inappropriate shock and a combined endpoint of cardiovascular morbidity and all-cause mortality. CONCLUSION: The study will evaluate the relative performance of DC in comparison to SC ICDs in terms of inappropriate shock reduction and patient outcome.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.048 | 0.035 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".