Thirty-day hospital readmission predictors in older patients receiving hospital-at-home: a 3-year retrospective study in France
Bibliographic record
Abstract
OBJECTIVE: This study described older patients receiving hospitalisation-at-home (HaH) services and identified factors associated with 30-day hospital readmission. DESIGN: 3-year retrospective study in 2017-2019 in France. PARTICIPANTS: 75 108 patients aged 75 years and older who were discharged from hospital medical wards (internal medicine and geriatric units) and admitted to HaH. PRIMARY OUTCOME MEASURE: 30-day hospital readmission. RESULTS: The mean age of patients was 83.4 years (SD 5.7), 52.3% were male and 88.4% lived in a private household. Patients were primarily discharged from the internal medicine unit (85.3%). The top four areas of care in the HaH were palliative care, complex dressing, intravenous therapy and complex nursing care. Overall, 23.5% of patients died during their HaH stay and 27.8% were readmitted to the hospital at 30 days. In the multivariate model, male (OR 1.19, 95% CI 1.16 to 1.23), supportive cancer HaH care (OR 1.78, 95% CI 1.51 to 2.11) and very high intensity care during the previous in-person hospitalisation (OR 1.45, 95% CI 1.34 to 1.57) increased the risk of hospital readmission at 30 days. Older age (OR 0.97, 95% CI 0.97 to 0.98), living in a nursing home (OR 0.51, 95% CI 0.48 to 0.54), postsurgery HaH care (OR 0.49, 95% CI 0.41 to 0.58) and having been previously hospitalised in a geriatric unit (OR 0.81, 95% CI 0.77 to 0.85) decreased the risk of hospital readmission at 30 days. CONCLUSIONS: HaH provides complex care to very old patients, which is associated with high mortality. Several factors are associated with rehospitalisation within 30 days that could be avoided with better integration of different services with higher geriatric skills. TRIAL REGISTRATION NUMBER: CNIL:2228861.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".