Demographic Shifts, Case Mix, Activity, and Outcome for Elderly Patients Admitted to Adult General ICUs in England, Wales, and Northern Ireland
Bibliographic record
Abstract
OBJECTIVES: Major increases in the proportion of elderly people in the population are predicted worldwide. These population increases, along with improving therapeutic options and more aggressive treatment of elderly patients, will have major impact on the future need for healthcare resources, including critical care. Our objectives were to explore the trends in admissions, resource use, and risk-adjusted hospital mortality for older patients, admitted over a 20-year period between 1997 and 2016 to adult general ICUs in England, Wales, and Northern Ireland. DESIGN: RETROSPECTIVE ANALYSIS OF NATIONAL CLINICAL AUDIT DATABASE. SETTING: The Intensive Care National Audit & Research Centre Case Mix Programme Database, the national clinical audit for adult general ICUs in England, Wales, and Northern Ireland. PATIENTS: All adult patients 16 years old or older admitted to adult general ICUs contributing data to the Case Mix Programme Database between January 1, 1997, and December 31, 2016. MEASUREMENTS AND MAIN RESULTS: The annual number, trends, and outcomes for patients across four age bands (16-64, 65-74, 75-84, and 85+ yr) admitted to ICUs contributing to the Case Mix Programme Database from 1997 to 2016 were examined. Case mix, activity, and outcome were described in detail for the most recent cohort of patients admitted in 2015-2016. Between 1997 to 2016, the annual number of admissions to ICU of patients in the older age bands increased disproportionately, with increases that could not be explained solely by general U.K. demographic shifts. The risk-adjusted acute hospital mortality decreased significantly within each age band over the 20-year period of the study. Although acute severity at ICU admission was comparable with that of the younger age group, apart from cardiovascular and renal dysfunction, older patients received less organ support. Older patients stayed longer in hospital post-ICU discharge, and hospital mortality increased with age, but the majority of patients surviving to hospital discharge returned home. CONCLUSIONS: Over the past two decades, elderly patients have been more commonly admitted to ICU than can be explained solely by the demographic shift. Importantly, as with the wider population, outcomes in elderly patients admitted to ICU are improving over time, with most patients returning home.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".