Factors Associated With the Increasing Rates of Discharges Directly Home From Intensive Care Units—A Direct From ICU Sent Home Study
Bibliographic record
Abstract
Objectives: To evaluate the relationship between rates of discharge directly to home (DDH) from the intensive care unit (ICU) and bed availability (ward and ICU). Also to identify patient characteristics that make them candidates for safe DDH and describe transfer delay impact on length of stay (LOS). Methods: Retrospective cohort study of all adult patients who survived their stay in our medical–surgical–trauma ICU between April 2003 and March 2015. Results: Median age was 49 years (interquartile range [IQR]: 33.5-60.4), and the majority of the patients were males (54.8%). Median number of preexisting comorbidities was 5 (IQR: 2-7) diagnoses. Discharge directly to home increased from 28 (3.1% of all survivors) patients in 2003 to 120 (12.5%) patients in 2014. The mean annual rate of DDH was between 11% and 12% over the last 6 years. Approximately 62% (n = 397) of patients waited longer than 4 hours for a ward bed, with a median delay of 2.0 days (IQR: 0.5-4.7) before being DDH. There was an inverse correlation between ICU occupancy and DDH rates ( r P = −.55, P < .0001, 95% confidence interval [CI] = −0.36 to −0.69, R 2 = .29). There was no correlation with ward occupancy and DDH rates ( r s = −.055, P = .64, 95% CI = −0.25 to 0.21). Conclusions: The DDH rates have been increasing over time at our institution and were inversely correlated with ICU bed occupancy but were not associated with ward occupancy. The DDH patients are young, have few comorbidities on admission, and few discharge diagnoses, which are usually reversible single system problems with low disease burden. Transfers to the ward are delayed in a majority of cases, leading to increased ICU LOS and likely increased overall hospital LOS as well.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".