Trends in Intensive Care for Patients with COVID-19 in England, Wales, and Northern Ireland
Bibliographic record
Abstract
Abstract Rationale By describing trends in intensive care for patients with coronavirus disease (COVID-19) we aim to support clinical learning, service planning, and hypothesis generation. Objectives To describe variation in ICU admission rates over time and by geography during the first wave of the epidemic in England, Wales, and Northern Ireland; to describe trends in patient characteristics on admission to ICU, first-24-hours physiology in ICU, processes of care in ICU and patient outcomes; and to explore deviations in trends during the peak period. Methods A cohort of 10,741 patients with COVID-19 in the Case Mix Program national clinical audit from February 1 to July 31, 2020, was used. Analyses were stratified by time period (prepeak, peak, and postpeak periods) and geographical region. Logistic regression was used to estimate adjusted differences in 28-day in-hospital mortality between periods. Measurements and Main Results Admissions to ICUs peaked almost simultaneously across regions but varied 4.6-fold in magnitude. Compared with patients admitted in the prepeak period, patients admitted in the postpeak period were slightly younger but with higher degrees of dependency and comorbidity on admission to ICUs and more deranged first-24-hours physiology. Despite this, receipt of invasive ventilation and renal replacement therapy decreased, and adjusted 28-day in-hospital mortality was reduced by 11.8% (95% confidence interval, 8.7%–15.0%). Many variables exhibited u-shaped or n-shaped curves during the peak. Conclusions The population of patients with COVID-19 admitted to ICUs, and the processes of care in ICUs, changed over the first wave of the epidemic. After adjustment for important risk factors, there was a substantial improvement in patient outcomes.
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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 | 0.002 |
| 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".