Impact of co-morbid Heart Failure on health outcomes following hospitalization for AECOPD
Bibliographic record
Abstract
Introduction: Heart Failure (HF) is common in patients with chronic obstructive pulmonary disease (COPD). The impact of concomitant HF on outcomes in patients hospitalized for an acute exacerbation of COPD (AECOPD) is uncertain. Methods: This was a population-based cohort study of all patients hospitalized for an AECOPD in Alberta, Canada between September 2018 to December 2019 using linked administrative databases. The exposure of interest was a diagnosis of HF based on a validated ICD-10 algorithm.1 The primary outcome was 30-day hospital readmission. Secondary outcomes were 30-day emergency department (ED) visits and all-cause 90-day mortality post-discharge. We used Poisson regression and Cox proportional hazards regression to compare outcomes between patients with and without HF. Results: Of 8463 patients hospitalized with an AECOPD, the median age was 74 years, 49.8% were female, and 33.3% of patients had concomitant HF. The 30-day readmission, ED visit, and 90-day mortality rates were significantly higher in patients with HF (Figure). erj;64/suppl_68/PA1761/F1 F1 F1 Conclusions: Comorbid HF is associated with increased short-term risk of readmission, ED visits, and death in patients following hospitalization for an AECOPD. Further research will identify whether medical optimization of both COPD and HF at the time of discharge impacts the short-term risk. References 1. Schultz SE, et al. Chronic Dis Inj Can. 2013;33(3):160–6
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".