International Differences in Length of Stay and Discharge Destination Following Hospitalization for Acute Myocardial Infarction
Bibliographic record
Abstract
Background: A variety of institutional and technological factors have led to reduced lengths of stay (LOS) among patients hospitalized for acute myocardial infarction (MI) over the past two decades, but large international variations in LOS remain. One factor influencing LOS variation may be the availability of post-acute care facilities. When such facilities are unavailable, patients must be discharged in good enough condition to receive care at home. We explore this phenomenon by examining the influence of discharge destination on LOS for MI in 9 countries. Methods: Data are from a multi-site, international registry of patients who presented to acute care hospitals with MI. We used a 2-stage model to estimate the impact of LOS on the likelihood of discharge to home while controlling for patient demographics, medical history, MI severity at presentation, and country. Predicted values of LOS were generated by regressing LOS against the variables listed above, plus the sum of Medicare resource value units (RVUs) associated with procedures performed during the stay. A linear probability model was used to model discharge status as a function of predicted LOS and other variables (excluding RVUs), including country-LOS interactions. Results: Of 5573 observations, 380 patients that died during hospitalization, 435 with missing or invalid lengths of stay, 12 awaiting transplantation and 641 with missing or invalid discharge disposition were excluded, leaving a total of 4105 observations. Mean age was 65.9 years (SD 13.7); 66.8% were male. Mean LOS for the sample was 9.0 days (SD 8.8). Mean LOS was shortest for the US and longest for Canada (7.6 versus 12.0 days, p Conclusions: These data confirm large differences in LOS at the country level and indicate that patients with longer LOS are likelier to be discharged home than to another facility. This analysis is subject to several limitations, including small sample sizes in several countries, missing discharge data for ~10% of observations, and lack of post-discharge survival data. While our ability to definitely address potential factors affecting LOS across countries is limited, our results suggest a potential inefficient use of health care resources, since acute care hospitalization is comparatively more expensive than skilled nursing or rehabilitation care. Further, this analysis shows the importance controlling for the endogeneity of LOS.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".