Abstract 16307: Does the Method of Calculating the 30-day Readmission Rate After Hospitalization for Heart Failure Matter? Data From the VancOuver CoastAL Acute Heart Failure Registry
Bibliographic record
Abstract
Introduction: The 30-day readmission rate after a heart failure (HF) hospitalization is widely used for assessing healthcare quality and system performance. However, there are different methodological approaches which may influence estimated rates, and no single accepted approach. The combined impact of these methods is unknown. Goals: We calculated 30-day HF readmission rates (RRs) of hospitalized patients using different published approaches to sampling and measurement. Methods: We included 1,849 patients discharged following unplanned hospitalization with a primary diagnosis of HF between 2016 to 2018 from the VancOuver CoastAL Acute Heart Failure (VOCAL-AHF) registry. We combined five distinct methodological factors (Table 1) to create 64 unique definitions and associated HF RRs. The readmission rates were averaged over 3-years. Multiple linear regression was used to determine the impact of different factors on estimated readmission rates. Results: The calculated 30-day RR for HF varied more than twofold depending solely on the methodological approach (6.4% to 15.0%, 8.6% absolute difference, 134% relative difference). The rates were highest when including all consecutive index admissions (11.1% to 15.0%), and lowest including only one index admission per patient per year (6.4% to 11.4%). The regression model ranked variables affecting RR as: index selection method, reference period, ICD-10 codes, 30-day survival, and index day (Table 2, p<0.001 in all). Conclusions: Our findings have important implications for policy and reporting. Transparent and consistent methods are needed to calculate 30-day RRs to ensure reproducible and comparable reporting.
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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 | 0.060 | 0.186 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".