Characteristics and Outcomes of ICU Patients Without COVID-19 Infection—Pandemic Versus Nonpandemic Times: A Population-Based Cohort Study
Bibliographic record
Abstract
IMPORTANCE: Outcomes for critically ill COVID-19 are well described; however, the impact of the pandemic on critically ill patients without COVID-19 infection is less clear. OBJECTIVES: To demonstrate the characteristics and outcomes of non-COVID patients admitted to an ICU during the pandemic, compared with the previous year. DESIGN: A population-based study conducted using linked health administrative data comparing a cohort from March 1, 2020, to June 30, 2020 (pandemic) to a cohort from March 1, 2019, to June 30, 2019 (nonpandemic). SETTING AND PARTICIPANTS: Adult patients (18 yr old) admitted to an ICU in Ontario, Canada, without a diagnosis of COVID-19 during the pandemic and nonpandemic periods. MAIN OUTCOMES AND MEASURES: The primary outcome was all-cause in-hospital mortality. Secondary outcomes included hospital and ICU length of stay, discharge disposition, and receipt of resource intensive procedures (e.g., extracorporeal membrane oxygenation, mechanical ventilation, renal replacement therapy, bronchoscopy, feeding tube insertion, and cardiac device insertion). We identified 32,486 patients in the pandemic cohort and 41,128 in the nonpandemic cohort. Age, sex, and markers of disease severity were similar. Fewer patients in the pandemic cohort were from long-term care facilities and had fewer cardiovascular comorbidities. There was an increase in all-cause in-hospital mortality among the pandemic cohort (13.5% vs 12.5%; p < 0.001) representing a relative increase of 7.9% (adjusted odds ratio, 1.10; 95% CI, 1.05–1.56). Patients in the pandemic cohort admitted with chronic obstructive pulmonary disease exacerbation had an increase in all-cause mortality (17.0% vs 13.2%; p = 0.013), a relative increase of 29%. Mortality among recent immigrants was higher in the pandemic cohort compared with the nonpandemic cohort (13.0% vs 11.4%; p = 0.038), a relative increase of 14%. Length of stay and receipt of intensive procedures were similar. CONCLUSIONS AND RELEVANCE: We found a modest increase in mortality among non-COVID ICU patients during the pandemic compared with a nonpandemic cohort. Future pandemic responses should consider the impact of the pandemic on all patients to preserve quality of care.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".