Mortality among Patients Admitted to Strained Intensive Care Units
Bibliographic record
Abstract
RATIONALE: The aging population may strain intensive care unit (ICU) capacity and adversely affect patient outcomes. Existing fluctuations in demand for ICU care offer an opportunity to explore such relationships. OBJECTIVES: To determine whether transient increases in ICU strain influence patient mortality, and to identify characteristics of ICUs that are resilient to surges in capacity strain. METHODS: Retrospective cohort study of 264,401 patients admitted to 155 U.S. ICUs from 2001 to 2008. We used logistic regression to examine relationships of measures of ICU strain (census, average acuity, and proportion of new admissions) near the time of ICU admission with mortality. MEASUREMENTS AND MAIN RESULTS: A total of 36,465 (14%) patients died in the hospital. ICU census on the day of a patient's admission was associated with increased mortality (odds ratio [OR], 1.02 per standardized unit increase; 95% confidence interval [CI]: 1.00, 1.03). This effect was greater among ICUs employing closed (OR, 1.07; 95% CI: 1.02, 1.12) versus open (OR, 1.01; 95% CI: 0.99, 1.03) physician staffing models (interaction P value = 0.02). The relationship between census and mortality was stronger when the census was composed of higher acuity patients (interaction P value < 0.01). Averaging strain over the first 3 days of patients' ICU stays yielded similar results except that the proportion of new admissions was now also associated with mortality (OR, 1.04 for each 10% increase; 95% CI: 1.02, 1.06). CONCLUSIONS: Several sources of ICU strain are associated with small but potentially important increases in patient mortality, particularly in ICUs employing closed staffing models. Although closed ICUs may promote favorable outcomes under static conditions, they are susceptible to being overwhelmed by patient influxes.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".