Clustered Emergency Room Visits Following an Acute Heart Failure Admission: A Population‐Based Study
Bibliographic record
Abstract
Background While it is well known that heart failure patients presenting to the emergency room (ER) have high short‐term mortality after discharge, the outcomes of patients with heart failure with repeated ER visits within a short time are not known. In this study, we aimed to determine whether clustering is associated with an increased risk of death. Methods and Results This is a retrospective, population‐based cohort study with an accrual window between 2003 and 2014 and maximal follow‐up up to and including March 31, 2015. Data were obtained from administrative databases from Ontario, Canada. Clustering was defined a priori as 3 or more ER visits within a 6‐month period. The main outcome of interest was time to death conditional on 6‐month survival. A total of 72 810 patients with an index hospitalization for acute heart failure were evaluated. ER clustering was observed in 15.1% of the population. Increased burden of comorbidities, primary rural residence, and lack of primary care provider were identified as factors associated with ER clustering. Age‐ and sex‐adjusted mortality for clustered patients was higher than for nonclustered (hazard ratio [HR] 1.51; 95% confidence interval, 1.47–1.55, P <0.0001). Adjusted mortality risk was also higher for patients with clustered ER visits ( HR 1.42; 95% confidence interval 1.38–1.46; P <0.0001). Conclusions Clustering, as defined by 3 or more ER visits for any reason within 6 months of index heart failure hospitalization reflects a novel risk factor associated with increased mortality. Future research into the strategies to better manage complex patients with heart failure with recurrent ER visits are warranted.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".