Patients With Heart Failure Readmitted to the Original Hospital Have Better Outcomes Than Those Readmitted Elsewhere
Bibliographic record
Abstract
Background Up to one fifth of readmissions after a heart failure hospitalization occur at a different hospital. This negatively impacts information continuity, but whether site of readmission impacts subsequent outcomes is unclear. Methods and Results Retrospective cohort study of all patients discharged with a primary diagnosis of heart failure in Canada between April 2004 and December 2013. We compared patients readmitted within 30 days to the original hospital versus a different hospital. Of the 217 039 heart failure patients (mean age, 76.8 years, 50.1% male), 39 368 (18.1%) were readmitted within 30 days—32 771 (83.2%) to the original hospital and 6597 (16.8%) to a different hospital (increasing over time from 15.6% in 2004 to 18.5% by 2013; P for trend=0.001). Patients readmitted to different hospitals were younger and were more likely to be male, have a rural residence, a more‐recent discharge year, an index hospitalization at a teaching hospital, and to be brought in by ambulance at the time of the readmission. Readmissions to the original hospital were substantially shorter (mean, 10.4 days [95% CI , 10.3–10.6] versus 11.6 days [95% CI , 11.3–12.0]; adjusted means, 11.0 versus 12.0; P <0.0001) and had lower mortality (14.4% versus 15.0%; adjusted odds ratio, 0.89; 95% CI , 0.82–0.96) than readmissions to different hospitals. Conclusions Readmissions to a different hospital are becoming more frequent over time and are associated with longer stays and higher mortality rates than readmissions to the original hospital. Our findings provide further evidence that care fragmentation may be deleterious for patients with heart failure.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".