Addressing Competing Risks When Assessing the Impact of Health Services Interventions on Hospital Length of Stay
Bibliographic record
Abstract
BACKGROUND: Although hospital length of stay is generally modeled continuously, it is increasingly recommended that length of stay should be considered a time-to-event outcome (i.e., time to discharge). Additionally, in-hospital mortality is a competing risk that makes it impossible for a patient to be discharged alive. We estimated the effect of trauma center accreditation on risk of being discharged alive while considering in-hospital mortality as a competing risk. We also compared these results with those from the "naive" approach, with length of stay modeled continuously. METHODS: Data include admissions to a level I trauma center in Quebec, Canada, between 2008 and 2017. We computed standardized risk of being discharged alive at specific days by combining inverse probability weighting and the Aalen-Johansen estimator of the cumulative incidence function. We estimated effect of accreditation using pre-post, interrupted time series (ITS) analyses, and the "naive" approach. RESULTS: Among 5,300 admissions, 12% died, and 83% were discharged alive within 60 days. Following accreditation, we observed increases in risk of discharge between the 7th day (4.5% [95% CI = 2.3, 6.6]) and 30th day since admission 3.8% (95% CI = 1.5, 6.2). We also observed a stable decrease in hospital mortality, -1.9% (95% CI = -3.6, -0.11) at the 14th day. Although pre-post and ITS produced similar results, we observed contradictory associations with the naive approach. CONCLUSIONS: Treating length of stay as time to discharge allows for estimation of risk of being discharged alive at specific days after admission while accounting for competing risk of death.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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".