Use of intensive care at the end of life in the United States: An epidemiologic study*
Bibliographic record
Abstract
OBJECTIVE: Despite concern over the appropriateness and quality of care provided in an intensive care unit (ICU) at the end of life, the number of Americans who receive ICU care at the end of life is unknown. We sought to describe the use of ICU care at the end of life in the United States using hospital discharge data from 1999 for six states and the National Death Index. DESIGN: Retrospective analysis of administrative data to calculate age-specific rates of hospitalization with and without ICU use at the end of life, to generate national estimates of end-of-life hospital and ICU use, and to characterize age-specific case mix of ICU decedents. SETTING: All nonfederal hospitals in the states of Florida, Massachusetts, New Jersey, New York, Virginia, and Washington. PATIENTS: All inpatients in nonfederal hospitals in the six states in 1999. INTERVENTION: None. MEASUREMENTS AND MAIN RESULTS: We found that there were 552,157 deaths in the six states in 1999, of which 38.3% occurred in hospital and 22.4% occurred after ICU admission. Using these data to project nationwide estimates, 540,000 people die after ICU admission each year. The age-specific rate of ICU use at the end of life was highest for infants (43%), ranged from 18% to 26% among older children and adults, and fell to 14% for those >85 yrs. Average length of stay and costs were 12.9 days and $24,541 for terminal ICU hospitalizations and 8.9 days and $8,548 for non-ICU terminal hospitalizations. CONCLUSIONS: One in five Americans die using ICU services. The doubling of persons over the age of 65 yrs by 2030 will require a system-wide expansion in ICU care for dying patients unless the healthcare system pursues rationing, more effective advanced care planning, and augmented capacity to care for dying patients in other settings.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".