Recurrent events analysis for examination of hospitalizations in heart failure: insights from the Enhanced Feedback for Effective Cardiac Treatment (EFFECT) trial
Bibliographic record
Abstract
Aims: Hospitalizations often occur multiple times during the disease course of a heart failure (HF) patient. However, repeated hospitalizations have not been explored in a fulsome way in this setting. We investigated the association between patient factors and the risk of hospitalization among patients with HF using an extension of the Cox model for the analysis of recurrent events. Methods and results: We examined hospitalizations and predictors of readmission among newly discharged patients with HF in the Enhanced Feedback For Effective Cardiac Treatment phase 1 (April 1999-March 2001) study with the Prentice-Williams-Peterson model with total time. Of 8948 individuals discharged alive from hospital, 7562 (84.5%) were hospitalized at least once during 15-year follow-up. More than 31 000 hospitalizations were observed. There was a progressive shortening of the interval length between hospitalization episodes. An increasing number of comorbidities (average 2.3 per patient) was associated to an increasing hazard of being readmitted to hospital. Most patient factors associated with the risk of hospitalization have been previously described in the literature. However, the estimates were smaller in comparison to a traditional analysis based on the Cox model. Conclusion: The importance of patient factors for the risk of being admitted to hospital was variable over the course of the disease. Conditions such as diabetes and chronic pulmonary obstructive disease had a sustained association with the rate of hospitalization across all episodes examined. The analysis of recurrent events can explore the longitudinal aspect of HF and the critical issue of hospitalizations in this population.
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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 | 0.019 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| 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) | 0.004 | 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".