Mortality and Heart Failure After Upgrade to Cardiac Resynchronization Therapy
Bibliographic record
Abstract
Background Cardiac resynchronization therapy (CRT) is effective in treating advanced heart failure (HF), but data describing benefits and long-term outcomes for upgrades from a preexisting device are limited. This study sought to compare long-term outcomes in de novo CRT implants with those eligible for CRT with a prior device. Methods This is a retrospective cohort study using data from a provincial registry (2002-2015). Patients were included if they had mild-moderate HF, left ventricular ejection fraction ≤ 35%, and QRS duration ≥ 130 ms. Patients were classified as de novo CRT or upgraded to CRT from a prior device. Outcomes were mortality and composite mortality and HF hospitalization. Results There were 342 patients included in the study. In a multivariate model, patients in the upgraded cohort (n = 233) had a higher 5-year mortality rate (adjusted hazard ratio, 2.86; 95% confidence interval, 1.59-5.15; P = 0.0005) compared with the de novo cohort (n = 109) and higher composite mortality and HF hospitalization (adjusted hazard ratio, 2.60; 95% confidence interval, 1.54-4.37; P = 0.0003). Conclusions Implantation of de novo CRTs was associated with lower mortality and HF hospitalization compared with upgraded CRTs from preexisting devices. It is unknown whether these differences are due to the timing of CRT implementation or other clinical factors. Further work in this area may be helpful to determine how to improve outcomes for these patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".