Incidence, Predictors, and Procedural Results of Upgrade to Resynchronization Therapy
Bibliographic record
Abstract
BACKGROUND: The resynchronization-defibrillation for ambulatory heart failure trial (RAFT) study demonstrated that adding cardiac resynchronization therapy (CRT) in selected patients requiring de novo implantable cardiac defibrillators (ICD) reduced mortality as compared with ICD therapy alone, despite an increase in procedure-related adverse events. Data are lacking regarding the management of patients with ICD therapy who develop an indication for CRT upgrade. METHODS AND RESULTS: Participating RAFT centers provided data regarding de novo CRT-D (CRT with ICD) implant, upgrade to CRT-D during RAFT (study upgrade), and upgrade within 6 months after presentation of study results (substudy). Substudy centers enrolled 1346 (74.9%) patients in RAFT, including 644 de novo, 80 study upgrade, and 60 substudy CRT attempts. The success rate (initial plus repeat attempts) was 95.2% for de novo versus 96.3% for study upgrade and 90.0% for substudy CRT attempts (P=0.402). Acute complications occurred among 26.2% of de novo versus 18.8% of study upgrade and 3.4% of substudy CRT implantation attempts (P<0.001). The most common complication was left ventricular lead dislodgement. The principal reasons for not yet attempting upgrade in the substudy were patient preference (31.9%), New York Heart Association Class I (17.0%), and a QRS<150 ms (13.1%). CONCLUSIONS: Among a broad group of implant physicians, CRT upgrades were performed in patients with an ICD in situ with no difference in implant success rate and a reduced acute complication rate as compared with a de novo CRT implant. Decisions to upgrade were influenced by predictors of benefit in subgroup analyses of the RAFT study and other trials.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".