Very Long-term Longitudinal Follow-up of Heart Failure on the REMADHE Trial
Bibliographic record
Abstract
Abstract Background Heart failure (HF) is associated with frequent hospitalization and worse prognosis. Prognosis factors and survival in very long-term follow-up have not been reported in HF. HF disease management programs(DMP) results are contradictory. DMP efficacy in very long-term follow-up is unknown. We studied the very long-term follow-up of up to 23.6 years and prognostic factors of HF in 412 patients under GDMT included in the REMADHE trial. Methods The REMADHE trial was a prospective, single-center, randomized trial comparing DMP versus usual care(C). The first patient was randomized on October 5, 1999. The primary outcome of this extended REMADHE was all-cause mortality. Results The all-cause mortality rate was 88.3%. HF was the first cause of death followed by death at home. Mortality was higher in the first 6-year follow-up. The predictive variables in multivariate analysis associated with mortality were age ≥52 years (P=0.015), Chagas etiology (P=0.010), LVEF <45% (P=0.008), use of digoxin (P=0.002), functional class IV (P=0.01), increase in urea (P=0.03), and reduction of lymphocytes (P=0.005). In very long-term follow-up, DMP did not affect mortality in patients under GDMT. HF as a cause of death was more frequent in the C group. Chagas disease, LVEF <45%, and renal function were associated with different modes of death. Conclusion DMP was not effective in reducing very-long term mortality; however, the causes of death had changed. Our findings that age, LVEF, Chagas’ disease, functional class, renal function, lymphocytes, and digoxin use were associated with poor prognosis could influence future strategies to improve HF management.
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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) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".