Characteristics, treatment and delirium incidence of older adults hospitalized with COVID-19: a multicentre retrospective cohort study
Bibliographic record
Abstract
Background: The COVID-19 pandemic has affected older adults disproportionately, and delirium is a concerning consequence; however, the relationship between delirium and corticosteroid use is uncertain. The objective of the present study was to describe patient characteristics, treatments and outcomes among older adults hospitalized with COVID-19, with a focus on dexamethasone use and delirium incidence. Methods: We completed this retrospective cohort study at 7 sites (including acute care, rehabilitation and long-term care settings) in Toronto, Ontario, Canada. We included adults aged 65 years or older, consecutively hospitalized with confirmed SARS-CoV-2 infection, between Mar. 11, 2020, and Apr. 30, 2021. We abstracted patient characteristics and outcomes from charts and analyzed them descriptively. We used a logistic regression model to determine the association between dexamethasone use and delirium incidence. Results: During the study period, 927 patients were admitted to the acute care hospitals with COVID-19. Patients’ median age was 79.0 years (interquartile range [IQR] 72.0–87.0), and 417 (45.0%) were female. Most patients were frail (61.9%), based on a Clinical Frailty Scale score of 5 or greater. The prevalence of delirium was 53.6%, and the incidence was 33.1%. Use of restraints was documented in 20.4% of patients. In rehabilitation and long-term care settings (n = 115), patients’ median age was 86.0 years (IQR 78.5–91.0), 72 (62.6%) were female and delirium occurred in 17 patients (14.8%). In patients admitted to acute care during wave 2 of the pandemic (Aug. 1, 2020, to Feb. 20, 2021), dexamethasone use had a nonsignificant association with delirium incidence (adjusted odds ratio 1.38, 95% confidence interval 0.77–2.50). Overall, in-hospital death occurred in 262 (28.4%) patients in acute care settings and 28 (24.3%) patients in rehabilitation or long-term care settings. Interpretation: In-hospital death, delirium and use of restraints were common in older adults admitted to hospital with COVID-19. Further research should be directed to improving the quality of care for this population with known vulnerabilities during continued waves of the COVID-19 pandemic.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".