The State of Heart Failure Care in Canada: Minimal Improvement in Readmissions Over Time Despite an Increased Number of Evidence-Based Therapies
Bibliographic record
Abstract
Background: An unanswered question is whether the combination of advances in medical and device therapy over the past decade has translated into improved outcomes for patients with heart failure (HF) in Canada. Methods: The Canadian Institute for Health Information (CIHI) Hospital Morbidity Database was used to identify hospitalizations for HF among patients aged 18 years and older in Canadian hospitals during fiscal years 2009/2010 and 2018/2019. We assessed interprovincial differences in age, sex, length of stay (LOS), discharge disposition, type of admitting hospital, and most responsible service, for all HF admissions. National and provincial rates of HF admissions and all-cause 30-day readmissions were calculated. Results: After adjusting for age, the rate of HF admissions in Canada was 216 per 100,000 population in 2009/2010 and 2018/2019. The majority of patients with HF were admitted to general internal medicine and community hospitals in both 2009/2010 and 2018/2019. The national, crude, all-cause 30-day readmission rate stayed constant at 20.6%, and the majority of patients were readmitted with the diagnosis of HF in both 2009/2010 (62.5%) and 2018/2019 (59.0%). Median and interquartile range of HF LOS also remained unchanged at 7 days (3-14). Conclusions: The national rate of HF admissions, 30-day readmissions, and HF LOS have remained unchanged from 2009/2010 to 2018/2019, despite advances in medical and device therapy during this timeframe.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".