Lifetime Analysis of Hospitalizations and Survival of Patients Newly Admitted With Heart Failure
Bibliographic record
Abstract
BACKGROUND: Hospital readmissions for heart failure (HF) contribute to increased morbidity and resource burden. Predictors of hospitalization and patterns of cardiovascular events over the lifetime of patients with HF have not been elucidated. METHODS AND RESULTS: We examined recurrent hospitalizations, cardiovascular events, and survival among newly discharged (April 1999-March 2001) patients with HF in the Enhanced Feedback For Effective Cardiac Treatment phase 1 study. During 10-year follow-up, we examined all new cardiovascular hospitalizations and selected predictors of readmission. Among 8543 patients (mean age, 77.4±10.5 years; 51.6% women) followed for 22 567 person-years, 60.7% had ischemic etiology, and 67.3% had HF with reduced ejection fraction (left ventricular ejection fraction ≤45% versus >45% [HF with preserved ejection fraction]). Overall, 10-year mortality was 98.8%, with 35 966 hospital readmissions occurring over the lifetime of the cohort. Adjusted hazards ratios (HRs) for first cardiovascular hospitalization were 1.36 for ischemic HF (95% CI, 1.28-1.44; P<0.001), 1.10 for HF with reduced ejection fraction (95% CI; 1.00-1.20; P=0.045), and 1.00 for men (95% CI, 0.94-1.06; P=0.979). On repeated-events time-to-event analysis, ischemic HF was a predictor of cardiovascular (HR, 1.24; 95% CI, 1.18-1.29), HF (HR, 1.20; 95% CI, 1.13-1.27), and coronary heart disease (HR, 2.01; 95% CI, 1.81-2.24) hospitalizations (all P<0.001). Of all recurrent HF hospitalizations, 26.8% occurred in the first and 39.8% in the last deciles of cohort survival duration. Similarly, 29.7% and 52.3% of all cardiovascular readmissions occurred in the first and last deciles of the cohort survival duration, respectively. CONCLUSIONS: Among newly discharged patients with HF, cardiovascular events were clustered at early postdischarge and prefatal time periods, and were increased among those with ischemic etiology.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".