Associations between patient characteristics and delayed acute care discharge post-hip fracture surgery: a cohort study using linked health administrative data in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Older patients frequently experience delays in discharge post-hip fracture surgery. Our study aimed to describe the sociodemographic and clinical characteristics of patients who had a surgical repair for a hip fracture and to examine the associations between these characteristics and delayed discharge (> 6 days post-surgery) for frail vs. non frail patients. METHODS: We conducted a retrospective population-based cohort study using routinely collected health administrative data housed at ICES. The study population included all individuals aged 50 to 105 years with a hip fracture who had a surgical repair in Ontario, Canada between January 1, 2015, and December 31, 2021. We used descriptive statistics and multivariable logistic regression models to characterize the association of patient socio-demographics, baseline health, and characteristics of the acute care episode with delayed discharge between non-frail and frail groups. RESULTS: We included 74,838 patients, with a mean age of 80.9 (SD 10.7) years, among which 37,234 (49.8%) had a delayed discharge. Some factors increased the odds of delayed discharge in both non-frail and frail groups included prior location in complex continuing care (non-frail OR 1.64, 95% CI 1.14,2.35, P = 0.007; frail OR 2.33, 95% CI 1.70,3.21, P < 0.0001), as well as prior residence in the community with home care (non-frail OR 9.71, 95% CI 8.89,10.6, P < 0.0001; frail OR 13.8, 95% CI 12.0,15.8, P < 0.0001), or without home care (non-frail OR 5.81, 95% CI 5.35,6.32, P < 0.0001; frail OR 4.60, 95% CI 4.14,5.11, P < 0.0001) compared to long-term care as well as residing in a neighbourhood with a higher Racialized and Newcomer Populations Index quintile (non-frail OR 1.45, 95% CI 1.37,1.55, P < 0.0001; frail OR 1.90, 95% CI 1.68,2.16, P < 0.0001). Factors that reduced the odds of delayed discharge in both non-frail and frail groups included individuals living in rural areas (non-frail OR 0.44, 95% CI 0.42,0.47, P < 0.0001; frail OR 0.41, 95% CI 0.36,0.46, P < 0.0001), or having previous fragility fractures (non-frail OR 0.44, 95% CI 0.40,0.49, P < 0.0001; frail OR 0.56, 95% CI 0.51,0.62, P < 0.0001). However, patients in the non-frail group were more likely to be delayed for the presence of comorbidities including mood or mental health conditions (OR 1.22, 95% CI 1.16,1.28, P < 0.0001), stroke (OR 1.46, 95% CI 1.28,1.67, P < 0.0001), chronic obstructive pulmonary disease (OR 1.35, 95% CI 1.24,1.47, P < 0.0001), dementia (OR 1.41, 95% CI 1.34,1.48, P < 0.0001), or diabetes (OR 1.26, 95% CI 1.20,1.32, P < 0.0001). Factors that reduced the odds of delayed discharge in the non-frail group were female sex (OR 0.87, 95% CI 0.83,0.90, P < 0.0001), or having cancer (OR 0.94, 95% CI 0.90,0.98, P = 0.0089). CONCLUSION: Delayed discharge was common after hip fracture surgery in both the non-frail and frail groups. Preoperative residential status, comorbidities and sociodemographic factors are associated with delayed discharge. These data can help to inform strategies to improve timely discharge from acute care and the overall outcomes of the older adult hip fracture population.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".