The changing nature of the population of intensive-care patients
Bibliographic record
Abstract
Background: The increase in the number of Australia’s frail, very elderly ( 80 years of age) population will have an impact on admissions to intensive care. As the number of very elderly patients increase, it will be important to have information about what the impact of increasing age will have on aspects such as: the impact of age and chronic health conditions on intensive care treatment, and the impact on prognosis in the short and longer-term as well as how we should be involving the very elderly in determining their own goals of care.Objective: To evaluate the long-term trend in the rates of the very elderly ( 80 years of age) admitted to intensive care, as well as describe their chronic health conditions, length of stay, and mortality rates.Methods: This study was a retrospective review that used a database from a 40-bed, multidisciplinary, adult intensive care unit (ICU), located in South-Western Sydney, Australia. The setting is an 877-bed tertiary hospital that has medical and surgical specialties; including a referral trauma unit, with approximately 80,000 admissions a year. Data were acquired over 15-years, from January 1st, 2000 to December 31st, 2015.Results: Data were available for 32,796 patients, and of these, 4,137 (12.5%) were aged ≥ 80 years. The percentage of the very elderly admitted to ICU progressively increased from 8.6% in 2000 to (14.5% in 2015, p < .001). Overall, the median length of stay (LOS) in the ICU was 2-days (interquartile range: 1.2-4.1), and increased from 2.0 to 2.3 (p < .001). Similarly, the median hospital LOS increased over time from 9 to 11 days (p < .001). Intensive care and hospital death rates decreased over time from 19.9% to 9.8% (p < .001), and 31.8% to 19.9% (p < .001), respectively. The majority of the very elderly were admitted from the emergency department (ED) (38.1%), other sources of admission being from the operating theatres (OT) (33.5%), and the general ward (18.1%).Conclusions: The number and percentage of very elderly patients being managed in ICU is increasing, representing a different population from the one that much of our practice has been previously based. For example, we may need to review the way we estimate severity of illness on admission to the ICU with more weight given to the chronic health component of the very elderly. The acute indications for admission to ICU such as falls and infections are relatively straightforward to manage and usually have a good outcome. However, because age and the chronic health status of the very elderly are largely progressive and irreversible, we as health care professionals working in intensive care may have to consider longer-term post hospital outcomes as a basis for evaluating the effectiveness of the interventions in ICU.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".