Impact of the method of calculating 30-day readmission rate after hospitalization for heart failure. Data from the VancOuver CoastAL Acute Heart Failure (VOCAL-AHF) registry
Bibliographic record
Abstract
BACKGROUND: Thirty-day readmission rate after heart failure (HF) hospitalization is widely used to evaluate healthcare quality. Methodology may substantially influence estimated rates. We assessed the impact of different definitions on HF and all-cause readmission rates. METHODS: Readmission rates were examined in 1835 patients discharged following HF hospitalization using 64 unique definitions derived from five methodological factors: (1) International Classification of Diseases-10 codes (broad vs. narrow), (2) index admission selection (single admission only first-in-year vs. random sample; or multiple admissions in year with vs. without 30-day blanking period), (3) variable denominator (number alive at discharge vs. number alive at 30 days), (4) follow-up period start (discharge date vs. day following discharge), and (5) annual reference period (calendar vs. fiscal). The impact of different factors was assessed using linear regression. RESULTS: The calculated 30-day readmission rate for HF varied more than two-fold depending solely on the methodological approach (6.5-15.0%). All-cause admission rates exhibited similar variation (18.8-29.9%). The highest rates included all consecutive index admissions (HF 11.1-15.0%, all-cause 24.0-29.9%), and the lowest only one index admission per patient per year (HF 6.5-11.3%, all-cause 18.8-22.7%). When including multiple index admissions and compared with blanking the 30-day post-discharge, not blanking was associated with 2.3% higher readmission rates. Selecting a single admission per year with a first-in-year approach lowered readmission rates by 1.5%, while random-sampling admissions lowered estimates further by 5.2% (P < 0.001). CONCLUSION: Calculated 30-day readmission rates varied more than two-fold by altering methods. Transparent and consistent methods are needed to ensure reproducible and comparable reporting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".