Longitudinal Changes in ICU Admissions Among Elderly Patients in the United States*
Bibliographic record
Abstract
OBJECTIVES: Changes in population demographics and comorbid illness prevalence, improvements in medical care, and shifts in care delivery may be driving changes in the composition of patients admitted to the ICU. We sought to describe the changing demographics, diagnoses, and outcomes of patients admitted to critical care units in the U.S. hospitals. DESIGN: Retrospective cohort study. SETTING: U.S. hospitals. PATIENTS: There were 27.8 million elderly (age, > 64 yr) fee-for-service Medicare beneficiaries hospitalized with an intensive care or coronary care room and board charge from 1996 to 2010. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We aggregated primary International Classification of Diseases, 9th Revision, Clinical Modification discharge diagnosis codes into diagnoses and disease categories. We examined trends in demographics, primary diagnosis, and outcomes among patients with critical care stays. Between 1996 and 2010, we found significant declines in patients with a primary diagnosis of cardiovascular disease, including coronary artery disease (26.6 to 12.6% of admissions) and congestive heart failure (8.5 to 5.4% of admissions). Patients with infectious diseases increased from 8.8% to 17.2% of admissions, and explicitly labeled sepsis moved from the 11th-ranked diagnosis in 1996 to the top-ranked primary discharge diagnosis in 2010. Crude in-hospital mortality rose (11.3 to 12.0%), whereas discharge destinations among survivors shifted, with an increase in discharges to hospice and postacute care facilities. CONCLUSIONS: Primary diagnoses of patients admitted to critical care units have substantially changed over 15 years. Funding agencies, physician accreditation groups, and quality improvement initiatives should ensure that their efforts account for the shifting epidemiology of critical illness.
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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 | 0.003 |
| 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) | 0.001 | 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 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".