Comorbidities, symptoms and end-of-life medication use in hospitalised decedents before and during the COVID-19 pandemic: a retrospective regional cohort study in Ottawa, Canada
Bibliographic record
Abstract
OBJECTIVE: To compare comorbidities, symptoms and end-of-life (EoL) palliative medication (antisecretories, opioids, antipsychotics and sedatives) use among decedents before and during the COVID-19 pandemic. DESIGN: In a retrospective cohort study, decedent records in three acute care hospitals were abstracted, generating a prepandemic (November 2019-February 2020) group (pre-COVID) and two intrapandemic (March-August 2020, wave 1) groups, one without (COVID-ve) and one with COVID-19 infection (COVID+ve). Control group decedents were matched 2:1 on age, sex and care service (medicine/intensive care unit (ICU)) with COVID+ve decedents. SETTING: Three regional acute care teaching hospitals in Ottawa, Canada PARTICIPANTS: Decedents (N=425): COVID+ve (n=85), COVID-ve (n=170) and pre-COVID (n=170). MAIN OUTCOME MEASURES: Data were abstracted regarding demographics, admission comorbidities and symptoms, and EoL medication use; opioid doses were standardised to parenteral morphine equivalent daily dose (MEDD), and the predictors of upper quartile MEDD in the last 24 hours of life were examined in multivariable logistic regression with adjusted ORs (aORs) and 95% CIs. RESULTS: The prevalence of dementia (41% vs 28% and 26%, p=0.03), breathlessness (63.5% vs 42% and 47%, p<0.01), cough (40% vs 27% and 19%, p<0.01) and fever (54% vs 9% and 13.5%) was higher in COVID+ve versus pre-COVID and COVID-ve groups, respectively. The median (IQR) of MEDD over the last 72 hours of life was 16.7 (9-36.5) vs 13.5 (5.7-21.8) and 10.5 (5.3-23.8) for COVID+ve versus pre-COVID and COVID-ve groups, respectively, (p=0.007). Male sex, COVID+ve grouping, ICU death and high-flow nasal cannula use predicted upper quartile MEDD dose, aORs (95% CIs): 1.84 (1.05 to 3.22), 2.62 (1.29 to 5.3), 5.14 (2.47 to 10.7) and 1.93 (1.05 to 3.52), respectively. COVID+ve group decedents used highest lorazepam and propofol doses. CONCLUSIONS: COVID-19 decedents, particularly those in ICU, required higher EoL opioid and sedating medication doses than matched prepandemic or intrapandemic controls. These findings should inform and guide clinical practice.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".