Characteristics and outcomes of patients with COVID-19 admitted to hospital and intensive care in the first phase of the pandemic in Canada: a national cohort study
Bibliographic record
Abstract
BACKGROUND: Clinical data on patients admitted to hospital with coronavirus disease 2019 (COVID-19) provide clinicians and public health officials with information to guide practice and policy. The aims of this study were to describe patients with COVID-19 admitted to hospital and intensive care, and to investigate predictors of outcome to characterize severe acute respiratory infection. METHODS: This observational cohort study used Canadian data from 32 selected hospitals included in a global multisite cohort between Jan. 24 and July 7, 2020. Adult and pediatric patients with a confirmed diagnosis of COVID-19 who received care in an intensive care unit (ICU) and a sampling of up to the first 60 patients receiving care on hospital wards were included. We performed descriptive analyses of characteristics, interventions and outcomes. The primary analyses examined in-hospital mortality, with secondary analyses of the length of hospital and ICU stay. RESULTS: Between January and July 2020, among 811 patients admitted to hospital with a diagnosis of COVID-19, the median age was 64 (interquartile range [IQR] 53-75) years, 495 (61.0%) were men, 46 (5.7%) were health care workers, 9 (1.1%) were pregnant, 26 (3.2%) were younger than 18 years and 9 (1.1%) were younger than 5 years. The median time from symptom onset to hospital admission was 7 (IQR 3-10) days. The most common symptoms on admission were fever, shortness of breath, cough and malaise. Diabetes, hypertension and cardiac, kidney and respiratory disease were the most common comorbidities. Among all patients, 328 received care in an ICU, admitted a median of 0 (IQR 0-1) days after hospital admission. Critically ill patients received treatment with invasive mechanical ventilation (88.8%), renal replacement therapy (14.9%) and extracorporeal membrane oxygenation (4.0%); 26.2% died. Among those receiving mechanical ventilation, 31.2% died. Age was an influential predictor of mortality (odds ratio per additional year of life 1.06, 95% confidence interval 1.03-1.09). INTERPRETATION: Patients admitted to hospital with COVID-19 commonly had fever, respiratory symptoms and comorbid conditions. Increasing age was associated with the development of critical illness and death; however, most critically ill patients in Canada, including those requiring mechanical ventilation, survived and were discharged from hospital.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| 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".