Prevalence and Prognostic Importance of Precipitating Factors Leading to Heart Failure Hospitalization: Recurrent Hospitalizations and Mortality
Bibliographic record
Abstract
AIMS: Hospitalizations for heart failure (HF) are common and are associated with significant morbidity, mortality and cost. However, precipitating factors leading to HF hospitalization and their importance with respect to subsequent outcomes are not well understood. METHODS AND RESULTS: The symptoms and signs present at admission and investigator-identified factors thought to have contributed to the first adjudicated HF hospitalization in the Candesartan in Heart Failure: Assessment of Reduction in Mortality and Morbidity (CHARM) programme were prospectively collected and stratified by ejection fraction (EF). Potential precipitants were collected using a specifically designed case report form and categorized according to the presence of cardiovascular (CV), non-CV and unknown factors. Associations between these factors and subsequent rehospitalization and mortality rates were examined. Of 1668 patients who experienced HF hospitalization, 1152 had reduced EF (≤40%, HFrEF) and 516 had preserved EF (HFpEF). Overall, 54% had CV, 32% had non-CV and 14% had unknown factors thought to have precipitated HF, with similar proportions in the HFrEF and HFpEF groups. The most common precipitants were arrhythmia (15%), other non-CV factors (11%) and respiratory infection (10%). Subsequent CV readmission rates were highest in those whose initial HF hospitalization was precipitated by CV factors. However, mortality rates were similar among patients with any of the three categories of precipitating factors. Results were similar in HFrEF and HFpEF. CONCLUSIONS: Among chronic HF patients hospitalized for decompensation, the investigator-reported precipitating factor was not associated with the subsequent mortality rate, but was associated with type of readmission: readmissions for CV reasons were more likely when the index precipitant was CV.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".