COVID-19 Variants of Concern: An Analysis of Critical Care Admission in Hospitalized Patients in a Canadian Health Region
Bibliographic record
Abstract
PURPOSE: To examine outcomes in COVID-19 positive acute care patients and the differential impact of the presence of COVID-19 Variants of Concern (VOCs). METHODS & MATERIALS: This study was a cross-sectional analysis using patient data from the patient's electronic medical records. Inclusion criteria were COVID-19-positive patients hospitalized within acute care sites in Fraser Health (British Columbia) between January 1 and April 30, 2021. Data analysis was conducted using SAS Studio 3.8 and STATA 17.0. RESULTS: Of the patients included in the study, 934 (33%) were classified as having a VOC. The proportion of VOC-related COVID-19 cases steadily increased from 0.6% of all COVID-19 admissions in January 2021 to 67.2% in April 2021. Males were more likely to have VOCs than females (36% vs. 30%). The age groups with the highest proportion of VOCs were 40-49 (51%), 50-59 (44%), and 60-69 (40%). After controlling for sex and age, it was shown that patients with VOCs were more than twice as likely to require critical care admission than those without VOCs (OR=2.04, 95%CI:1.67, 2.48; p<0.001). There was no statistically significant difference in overall length of stay (p=0.502) or length of stay in critical care (p=0.237) for those with VOCs after controlling for age and sex. While patients with VOCs were more than twice as likely to require critical care, there was no difference in mortality (OR=1.03, 95%CI:0.75,1.41), p=0.877). CONCLUSION: VOCs were more likely to be present in middle-aged hospitalized patients than in older patients, and were more prevalent in males. Patients with VOCs were more likely to require critical care; however, there was no difference in length of stay in critical care, or in overall mortality. This is important to understand, as VOCs make up a larger proportion of COVID-19 cases, and will likely place significant burden on critical care resources. Limitations of this study are that other factors such as co-morbidities and socioeconomic status have not been controlled for, and the findings may not be generalizable to other health regions with different populations and health care systems. This study provides groundwork for future research on this evolving topic.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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 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".