Prevalence of cognitive morbidity including delirium in 51,202 emergency hospital admissions across 29 medical and surgical specialties in ORCHARD-EPR: a cross-sectional study
Bibliographic record
Abstract
Background: Older people account for a growing proportion of unplanned hospital admissions and many have complex conditions. However, there are few data on cognitive morbidity (delirium, dementia and low cognitive test score) by specialty to plan services and guide policy. We therefore determined the occurrence of cognitive morbidity hospital-wide in older hospital patients using electronic patient record (EPR) data. Methods: The Oxford and Reading Cognitive Comorbidity, Frailty and Ageing Research Database (ORCHARD-EPR) includes data on consecutive patients aged ≥70 years with length of stay of ≥1 day (1st January 2017-31st December 2019) admitted to four hospitals covering Oxfordshire, UK. ORCHARD-EPR includes information from a mandatory on-admission cognitive screen comprising the 10-point Abbreviated Mental Test (AMT), dementia history and documentation of delirium where delirium diagnosis is based on a holistic assessment incorporating the AMT, Confusion Assessment Method (CAM) and clinical notes. Delirium and dementia diagnosis from the cognitive screen was supplemented by discharge ICD-10 coding. Prevalence of cognitive morbidity was determined hospital-wide and then by specialty. Findings: Among 51,202 admissions (mean/SD age = 82/7 years), any cognitive morbidity was present in 18,225 (35.6%, 95% CI 35.2-36.0%): delirium occurred in 24.0% (n = 12,289, of which 14.3% (n = 7332) had delirium only and 9.7% (n = 4957) had delirium + dementia) dementia only in 8.7%, (n = 4450), AMTS <8 in 2.9% (n = 1486). The prevalence of cognitive morbidity was highest in geriatrics (44.5%; n = 134/301), general medicine (42.8%; n = 14,346/33,512), trauma/orthopaedics (36.4%; n = 1337/3673), palliative care (36.0%; n = 128/356), stroke (30.8%; n = 144/468), infectious disease (27.6%; n = 42/152), neurosurgery (22.9%; n = 161/702) and general surgery (21.5%; n = 822/3819) and was 10-20% in all other specialties except two. Delirium was the most prevalent cognitive morbidity subtype in 24/29 specialties. Interpretation: Cognitive morbidity was common in older people with unplanned hospital admission across a broad range of specialties, with delirium accounting for most cases. Findings support the need for hospital-wide delirium screening and access to multidisciplinary team input for all specialties. Funding: Rhodes Trust, Canadian Institutes of Health Research, National Institutes for Health Research.
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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 | 0.003 | 0.152 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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 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".