Comparison of baseline characteristics, treatment and celinical outcomes of critically ill COVID-19 patients admitted in the first and second waves in Australia
Bibliographic record
Abstract
Objective: To report longitudinal differences in baseline characteristics, treatment, and outcomes in patients with coronavirus disease 2019 (COVID-19) admitted to intensive care units (ICUs) between the first and second waves of COVID-19 in Australia. Design, setting and participants: SPRINT-SARI Australia is a multicentre, inception cohort study enrolling adult patients with COVID-19 admitted to participating ICUs. The first wave of COVID-19 was from 27 February to 30 June 2020, and the second wave was from 1 July to 22 October 2020. Results: A total of 461 patients were recruited in 53 ICUs across Australia; a higher number were admitted to the ICU during the second wave compared with the first: 255 (55.3%) versus 206 (44.7%). Patients admitted to the ICU in the second wave were younger (58.0 v 64.0 years; P = 0.001) and less commonly male (68.9% v 60.0%; P = 0.045), although Acute Physiology and Chronic Health Evaluation (APACHE) II scores were similar (14 v 14; P = 0.998). High flow oxygen use (75.2% v 43.4%; P < 0.001) and non-invasive ventilation (16.5% v 7.1%; P = 0.002) were more common in the second wave, as was steroid use (95.0% v 30.3%; P < 0.001). ICU length of stay was shorter (6.0 v 8.4 days; P = 0.003). In-hospital mortality was similar (12.2% v 14.6%; P = 0.452), but observed mortality decreased over time and patients were more likely to be discharged alive earlier in their ICU admission (hazard ratio, 1.43; 95% CI, 1.13–1.79; P = 0.002). Conclusion: During the second wave of COVID-19 in Australia, ICU length of stay and observed mortality decreased over time. Multiple factors were associated with this, including changes in clinical management, the adoption of new evidence-based treatments, and changes in patient demographic characteristics but not illness severity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.102 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".