Costs and Outcomes of Patients Admitted to the Intensive Care Unit With Cancer
Bibliographic record
Abstract
Introduction: Cancer is associated with significant health-care expenditure, but few studies have examined the cost of patients with cancer in the intensive care unit (ICU). We aimed to describe the costs and outcomes of patients admitted to the ICU with cancer. Methods: We conducted a retrospective cohort study of patients admitted between 2011 and 2016 to 2 tertiary-care ICUs. We included patients with a cancer-related most responsible diagnosis using International Classification of Disease, 10th Revision, Canada codes. We compared costs and outcomes of patients having cancer with noncancer controls matched for age, sex, and Elixhauser comorbidity score. We used logistic regression to determine predictors of mortality among patients with cancer. Results: There were 1022 patients with cancer during the study period. Mean age was 63.2 years and 577 (56.5%) were male. Inhospital mortality for all patients with cancer was 24.0%. Total cost per patient was higher for patients with cancer compared to noncancer patients (CAD$57 084 vs CAD$40 730; P < .001) but there were no differences in the cost per day (CAD$2868 vs CAD$2887; P = .76) or ICU cost (CAD$30 495 vs CAD$29 382; P = .42). Among patients with cancer, the cost per day was higher for nonsurvivors (CAD$3477 vs CAD$2677; P < .001). Liver disease (odds ratio [OR]: 2.96; 95% confidence interval [CI]: 1.22-7.81), mechanical ventilation (OR: 1.73; 95% CI: 1.25-2.39), hematologic malignancy (OR: 3.88; 95% CI: 2.31-6.54), and unknown primary site (OR: 2.13; 95% CI: 1.36-3.35) were independently associated with mortality in patients with cancer. Conclusion: Patients admitted to the ICU with cancer did not differ in cost per day, ICU cost, or mortality compared to matched noncancer controls. Among patients with cancer, nonsurvivors had significantly higher cost per day compared to survivors. Hematologic and unknown primaries, liver disease, and mechanical ventilation were independently associated with mortality in patients with cancer.
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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 | 0.005 |
| 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 | 0.000 |
| 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".