Influence of Nonfatal Hospitalization for Heart Failure on Subsequent Mortality in Patients With Chronic Heart Failure
Bibliographic record
Abstract
BACKGROUND: Patients with chronic heart failure (HF) are at increased risk of both fatal and nonfatal major adverse cardiovascular events. We used data from the Candesartan in Heart failure: Assessment of Reduction in Mortality and morbidity (CHARM) trials to assess the influence of nonfatal hospitalizations for HF on subsequent mortality rates in a broad spectrum of HF patients. METHODS AND RESULTS: In the present study, 7599 patients with New York Heart Association class II to IV HF and reduced or preserved left ventricular ejection fraction were randomized to placebo or candesartan. We assessed the risk of death after discharge from a first hospitalization for HF using time-updated Cox proportional-hazards models on 7572 patients for whom discharge data were available. Of 7572 patients, 1455 (19%) had at least 1 HF hospitalization, and 586 of 1819 deaths occurred after discharge from an HF hospitalization. The mortality rate was increased after HF hospitalizations, even after adjustment for baseline predictors of death (hazard ratio, 3.15; 95% confidence interval, 2.83 to 3.50). Longer duration of HF hospitalization enhanced the risk of dying, as did repeat HF hospitalizations. Moreover, risk of death was highest within a month of discharge and then declined progressively over time, particularly for death resulting from HF progression and for sudden cardiac death. We observed a similar pattern of risk associated with all-cause hospitalization, although the magnitude was less than that with HF hospitalization. CONCLUSIONS: In patients with chronic HF, the risk of death is greatest in the early period after discharge after a hospitalization for HF and is directly related to the duration and frequency of HF hospitalizations. These findings suggest a role for increased surveillance in the early postdischarge period of greatest vulnerability after an HF admission.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".