Temporal Trends in Mortality of Critically Ill Patients with Sepsis in the United Kingdom, 1988–2019
Bibliographic record
Abstract
Abstract Rationale Sepsis is a frequent cause of ICU admission and mortality. Objectives To evaluate temporal trends in the presentation and outcomes of patients admitted to the ICU with sepsis and to assess the contribution of changing case mix to outcomes. Methods We conducted a retrospective cohort study of patients admitted to 261 ICUs in the United Kingdom during 1988–1990 and 1996–2019 with nonsurgical sepsis. Measurements and Main Results A total of 426,812 patients met study inclusion criteria. The patients had a median (interquartile range) age of 66 (53–75) years, and 55.6% were male. The most common sites of infection were respiratory (60.9%), genitourinary (11.5%), and gastrointestinal (10.3%). Compared with patients in 1988–1990, patients in 2017–2019 were older (median age, 66 vs. 63 yr), were less acutely ill (median Acute Physiology and Chronic Health Evaluation II acute physiology score, 14 vs. 20), and more often had genitourinary sepsis (13.4% vs. 2.0%). Hospital mortality decreased from 54.6% (95% confidence interval [CI], 51.0–58.1%) in 1988–1990 to 32.4% (95% CI, 32.1–32.7%) in 2017–2019, with an adjusted odds ratio of 0.64 (95% CI, 0.54–0.75). The adjusted absolute hospital mortality reduction from 1988–1990 to 2017–2019 was 8.8% (95% CI, 5.6–12.1). Thus, of the observed 22.2–percentage point reduction in hospital mortality, 13.4 percentage points (60% of total reduction) were explained by case mix changes, whereas 8.8 percentage points (40% of total reduction) were not explained by measured factors and may be a result of improvements in ICU management. Conclusions Over a 30-year period, mortality for ICU admissions with sepsis decreased substantially. Although changes in case mix accounted for the majority of observed mortality reduction, there was an 8.8–percentage point reduction in mortality not explained by case mix.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| 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".