Long-term outcomes and healthcare utilization following critical illness – a population-based study
Bibliographic record
Abstract
Abstract Background The purpose of this study was to examine hospital mortality, long-term mortality, and health service utilization among critically ill patients. We also determined whether these outcomes differed according to demographic and clinical characteristics. Methods We conducted a retrospective cohort study of adults (age ≥18 years) who survived admission to an intensive care unit (ICU) in Ontario, Canada, between 1 April 2002 and 31 March 2012, excluding isolated admissions to step-down or intermediate ICUs, coronary care ICUs, or cardiac surgery ICUs. Adults (age ≥18 years) who survived an acute hospitalization that did not include an ICU stay formed the comparator group. The primary outcome was mortality following hospital discharge. Secondary outcomes were healthcare utilization, including emergency room admissions and hospital readmissions during follow-up. Results Over the study interval, 500,124 patients were admitted to ICUs and 420,187 (84 %) survived to hospital discharge. Median follow-up for survivors was 5.3 (interquartile range 2.5, 8.2) years. Patients admitted to an ICU were more likely to subsequently visit the emergency department, be readmitted to the hospital and ICU, receive home care support, require rehabilitation, and be admitted for long-term care. Those requiring more resources within the ICU required more resources after discharge. One-third of patients admitted to the ICU died during long-term follow-up, with overall probabilities of death of 11 % and 29 % at 1 year and 5 years, respectively. In the adjusted analysis, there was an increasing hazard of death with increasing age, reaching a hazard ratio of 18.08 (95 % confidence interval 16.60–19.68) for those ≥85 years of age compared with those aged 18–24 years. Conclusions Healthcare utilization after hospital discharge was higher among ICU patients, and also among those requiring more healthcare resources during their ICU admission, than among all hospitalized patients as a group. One-third of ICU patients died within the 5 years following discharge, and age was the most influential determinant of outcome. These findings should help target post–ICU discharge services for high-risk groups and better inform goals-of-care discussions for elderly critically ill patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".