Influence of hospitalization for cardiovascular versus noncardiovascular reasons on subsequent mortality in patients with chronic heart failure across the spectrum of ejection fraction
Bibliographic record
Abstract
Purpose: Noncardiovascular (non-CV) comorbidities may contribute to hospitalizations in patients with heart failure (HF). We examined the incidence of mortality following hospitalization for cardiovascular (CV) versus non-CV reasons in patients with reduced or preserved ejection fraction (EF) in the CHARM Program. Methods: CHARM randomized 7,599 subjects (4576 with EF≤40% and 3023 with EF>40%) with NYHA class II-IV HF and prior history of cardiac hospitalization to treatment with candesartan or placebo. First hospitalizations for CV or non-CV reasons were related to subsequent risk of all-cause death over median 36.6 month follow up using time-updated proportional hazards models. Results: 4792 patients experienced a classifiable incident first hospitalization, including 2806 (58.5%) for CV reasons and 1986 (41.4%) for non-CV reasons, while 2802 were not hospitalized. Rates of CV hospitalization were higher for those with EF≤40% than those with EF>40% (p<0.001), but rates of non-CV hospitalization did not vary by EF (p=0.88, Table). The death rate (per 100-patient years) amongst those not hospitalized was 2.7 compared with 18.3 after CV and 16.2 after non-CV hospitalization (both p<0.001). Mortality at 30-days was higher after CV than non-CV hospitalization (p<0.001, Table). However, amongst 30-day survivors of CV and non-CV hospitalization, rates of subsequent mortality were similar (14.7 vs. 14.3, p=0.62). Low EF patients were at higher risk for mortality than high EF patients after both CV and non-CV hospitalization. (both p<0.001). Table 1. Incidence of Hospitalization for CV and non-CV Reasons in the CHARM trial and subsequent rates of mortality Conclusions: Non-CV reasons for hospitalization are common in HF patients across the spectrum of EF. Hospitalization for any reason is associated with high risk for subsequent mortality, with higher risk in low than preserved EF patients. Early mortality is higher after CV than non-CV hospitalization, but rates of mortality in 30-day survivors are unrelated to the cause of hospitalization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.000 | 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".