Intensive care unit and hospital mortality for non-COVID critically ill patients before, and during the COVID-19 pandemic in Alberta hospitals: retrospective, observational cohort study
Bibliographic record
Abstract
Abstract Objectives The Coronavirus Disease-2019 (COVID-19) pandemic had indirect impacts on healthcare provided to critically ill intensive care unit (ICU) non-COVID patients and their outcomes. Therefore, we examined the effect of the COVID-19 pandemic on patients with non-COVID critical illness during the pandemic and pre-pandemic epochs. Design Retrospective, population-based, observational cohort study. Setting All adult patients admitted to general ICUs in Alberta, Canada, from March 2017-2020 (pre-pandemic period) and March 2020-2023 (pandemic period). Data sources Data was captured from an integrated critical care clinical information system (eCritical Alberta) and Alberta Health Services (AHS) administrative databases were utilized. Measurements and main results A total of 80,540 non-COVID patients were admitted to ICUs in Alberta in the period between March 2017 and March 2023, equally distributed between the pandemic (40,196, 50.1%) and pre-pandemic (40,344, 49.9%) periods. For pandemic versus pre-pandemic cohorts, patient mean age was 57.7 ± 16.2 vs 58.5 ± 16.6 years and mean APACHE II score was 19.5 ± 8.8 vs 18.8 ± 8.5, respectively. ICU mortality was higher during the pandemic compared with pre-pandemic (14.2% vs. 11.2%, mean difference [MD]: +3.1%, adjusted odds ratio [OR]: 1.12; 95% CI: 1.05-1.20, p<0.001) after logistic regression adjustment. The hospital mortality was significantly higher in the pandemic vs. the pre-pandemic period (23.6% vs. 15.9%; MD: +7.7%, adjusted OR: 1.91, 95% CI: 1.81-2.02, p<0.001). There was greater ICU (5.95 ± 9.4 vs. 5.35 ± 8.5 days, MD: +0.63 days, 95% CI: 0.47 to 0.72 days, p<0.001), and lower hospital (18.0 ± 28.0 vs. 19.9 ± 33.3 days, MD: -1.89 days, 95% CI: -1.45 to -2.32 days p<0.001) lengths of stay (LOS) during the pandemic compared with pre-pandemic. Conclusion During the pandemic period, non-COVID patients had worse outcomes, greater adjusted ICU and hospital mortality, and higher resource utilization with increased ICU lengths of stay, when compared with pre-pandemic periods.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 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".