Survival rate and predictors of mortality in patients hospitalised with heart failure: a cohort study on the data of Persian registry of cardiovascular disease (PROVE)
Bibliographic record
Abstract
OBJECTIVES: Heart failure (HF) has a high rate of hospitalisation and mortality. We examined its risk factors, survival rate and the predictors. METHODS: In this prospective cohort study, demographic, clinical and treatment data of 1223 patients hospitalised with HF were extracted from the Persian Registry Of cardio Vascular diseasE (PROVE)/HF registry. Survival rate and HR and their association with other variables were assessed. RESULTS: 835 (68.3%) were censored, while 388 (31.7%) patients were deceased. Mean age and frequency of hypotension during hospitalisation, tachycardia, pulmonary hypertension and anaemia, hyponatremia, heart valve disease and renal disease of the deceased patients was significantly higher than censored patients (15.2vs6.1%, 51.1vs40.1%, 24.4vs16.7%, 39.0vs31.8%, respectively, p<0.05). ACE inhibitor (ACEI)/angiotensin receptor blocker (ARB) (89.8%vs82.1%, respectively) and beta blocker (BB) (81.1%vs75.5%, respectively) were higher in follow-up in the censored group (p<0.001 and 0.02, respectively). Crude Cox regression analysis identified age, tachycardia, hypotension, anaemia, pulmonary hypertension and heart valve disease as predictors of mortality (HR >1) and using ACEI/ARB and BB as predictors of life (HR <1, p<0.05). After adjustment, all variables lost their significance, except BB (HR 0.63, p=0.03) and tachycardia (HR 1.74, p=0.01) and New York Heart Association (NYHA) class IV (HR 1.90, p=0.04) became significant predictors. CONCLUSIONS: We found a high mortality rate (31.7%). As NYHA class IV and tachycardia were significant predictors of mortality after adjustment, an effective measure can be treatment of underlying diseases, which deteriorate patients' conditions. Monitoring of medications for at-risk group, especially BB that predicts life, is important.
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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 | 0.002 |
| 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 | 0.000 |
| 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".