The association of delirium severity with patient and health system outcomes in hospitalised patients: a systematic review
Bibliographic record
Abstract
BACKGROUND: delirium is an acute state of confusion that affects >20% of hospitalised patients. Recent literature indicates that more severe delirium may lead to worse patient outcomes and health system outcomes, such as increased mortality, cognitive impairment and length of stay (LOS). METHODS: using systematic review methodology, we summarised associations between delirium severity and patient or health system outcomes in hospitalised adults. We searched MEDLINE, EMBASE, PsycINFO, CINAHL and Scopus databases with no restrictions, from inception to 25 October 2018. We included original observational research conducted in hospitalised adults that reported on associations between delirium severity and patient or health system outcomes. Quality of included articles was assessed using the Newcastle-Ottawa Scale. The level of evidence was quantified based on the consistency of findings and quality of studies reporting on each outcome. RESULTS: we included 20 articles evaluating associations that reported: mortality (n = 11), cognitive ability (n = 3), functional ability (n = 3), patient distress (n = 1), quality of life (n = 1), hospital LOS (n = 4), intensive care unit (ICU) LOS (n = 2) and discharge home (n = 2). There was strong-level evidence that delirium severity was associated with increased ICU LOS and a lower proportion of patients discharged home. There was inconclusive evidence for associations between delirium severity and mortality, hospital LOS, functional ability, cognitive ability, patient distress and quality of life. CONCLUSION: delirium severity is associated with increased ICU LOS and a lower proportion of patients discharged home. Delirium severity may be a useful adjunct to existing delirium screening to determine the burden to health care system resources.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 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".