Determinants of Direct Discharge Home From Critical Care Units: A Population-Based Cohort Analysis*
Bibliographic record
Abstract
OBJECTIVE: To describe trends and patient and system factors associated with direct discharge from critical care to home in a large health system. DESIGN: Population-based cohort study of direct discharge to home rates annually over 10 years. We used a multivariable, multilevel random-effects regression model to analyze current factors associated with direct discharge home in a subcohort from the most recent 2 years. SETTING: One hundred seventy-four ICUs in 101 hospitals in Ontario. PATIENTS: All patients discharged from an ICU between April 1, 2007, and March 31, 2017. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Overall, 237,200 patients (21.1%) were discharged directly home from an ICU. The rate of direct discharge to home increased from 18.6% in 2007 to 23.1% in 2017 (annual increase of 1.02; 95% CI, 1.02-1.03). There were marked variations in rates of direct discharge to home across all critical care units. For medical and surgical units, the median odds ratio was 1.76 (95% CI, 1.59-1.92). In these units, direct discharge to home was associated with younger age (odds ratio, 0.36; 95% CI, 0.34-0.39 for age 80-105 vs age 18-39), fewer comorbidities (odds ratio, 1.74; 95% CI, 1.63-1.85 for Charlson comorbidity index of 0 vs 2), diagnoses of overdose/poisoning (odds ratio, 1.35; 95% CI, 1.23-1.47) and diabetic complications (odds ratio, 1.35; 95% CI, 1.2-1.51), and admission after a same-day procedure (odds ratio, 2.82; 95% CI, 2.46-3.23 compared with emergency department). ICU occupancy was inversely associated with direct discharge to home with an odds ratio of 0.88 (95% CI, 0.87-0.88) for each 10% increase. CONCLUSIONS: High rates of direct discharge to home with evidence of significant practice variation combined with identifiable patient characteristics suggest that further evaluation of this increasingly common transition in care is warranted.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".