Characterizing Types of Readmission After Acute Coronary Syndrome Hospitalization: Implications for Quality Reporting
Bibliographic record
Abstract
Background-—Thirty-day readmission rates have been tied to hospital reimbursement in the United States, but remain controversial as measures of healthcare quality. We profile the timing, main diagnoses, and survival outcomes of inpatient and emergency department readmissions after acute coronary syndrome (ACS), based on a large regional database. Methods and Results-—Patients enrolled in the Alberta Provincial Project for Outcomes Assessment in Coronary Heart Disease (APPROACH) registry with an ACS hospitalization between April 2008 and March 2010 (n=3411) were included. Primary outcomes were inpatient and emergency department–only readmissions, at 30 days and 1 year. Predictors of 30-day readmission were identified, and the association between 30-day readmission status and mortality was evaluated. A total of 1170 (34.3%) patients had ≥1 hospital readmission within 30 days, reaching 2106 (61.7%) within 1 year of ACS discharge. Of first readmissions, 45% were emergency department only and 53 % were for cardiovascular or possibly related diagnoses. Renal disease and diabetes predicted all-cause readmissions at 30 days and 1 year, but there were no robust predictors of cardiovascular readmissions. Thirty-day inpatient, but not emergency department, readmissions were associated with increased mortality. Conclusions-—Hospital readmissions within 30 days after discharge for ACS are common, and associated with increased mortality. However, our findings underline that readmissions are quite heterogeneous in nature, and that many readmissions are unrelated to index stay and thus not easily predicted with common clinical variables. All-cause 30-day readmission rates may be too simplistic, and perhaps even misleading, as a hospital performance metric. ( J Am Heart Assoc. 2014;3:e001046 doi: 10.1161/
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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 | 0.103 | 0.204 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| 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".