The impact of intensive care strain on patients’ outcomes—a multinational observational cohort (UNITE-COVID) study
Bibliographic record
Abstract
Abstract Purpose Intensive care unit (ICU) strain is associated with increased mortality. Most strain metrics focus on ‘simple’ measures such as bed occupancy or admission rates. There is limited data on mitigation strategies, such as procedure teams or staff well-being services on strain, or the impact of increased patient-to-nurse ratios and non-ICU trained nurses working in ICU. Methods Using the multi-national UNITE-COVID study, collecting data from ICUs on their day of peak bed occupancy in two periods (2020 and 2021) of the COVID-19 pandemic, we evaluated metrics of strain (Bed occupancy, patient: nurse ratio, use of non-ICU staff and shortages of consumables) and potential mitigators (procedural support teams and staff well-being interventions). We examined how these related to outcomes (mortality, complications, length of stay). Results In both epochs, ICUs experienced significant strain, with ICU bed expansion to 133% and 163% respectively, whilst patient-to-nurse ratios increased by 0.4 and 0.3. Consumable shortages were widespread in 2020. Mortality was inversely correlated with staff well-being interventions in both epochs. Complications were inversely correlated with procedure support teams, and positively correlated with staffing ratios. In regression models, pressure sores were reduced in presence of support teams (p = 0.004) and increased with increasing patients per nurse (p = 0.05) whilst unplanned extubations were related to non-ICU trained staff working in ICU(p = 0.02). Conclusions COVID-19 induced ICU strain had effects beyond mortality, including increases in complications. Staff pressure and lack of ICU training were related to specific complications, whilst support teams and well-being interventions were associated with improved outcomes.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".