Cost-Effectiveness Studies in the ICU: A Systematic Review*
Bibliographic record
Abstract
OBJECTIVES: Cost-effectiveness analyses are increasingly used to aid decisions about resource allocation in healthcare; this practice is slow to translate into critical care. We sought to identify and summarize original cost-effectiveness studies presenting cost per quality-adjusted life year, incremental cost-effectiveness ratios, or cost per life-year ratios for treatments used in ICUs. DESIGN: We conducted a systematic search of the English-language literature for cost-effectiveness analyses published from 1993 to 2018 in critical care. Study quality was assessed using the Drummond checklist. SETTING: Critical care units. PATIENTS OR SUBJECTS: Critical care patients. INTERVENTIONS: Identified studies with cost-effectiveness analyses. MEASUREMENTS AND MAIN RESULTS: We identified 97 studies published through 2018 with 156 cost-effectiveness ratios. Reported incremental cost-effectiveness ratios ranged from -$119,635 (hypothetical cohort of patients requiring either intermittent or continuous renal replacement therapy) to $876,539 (data from an acute renal failure study in which continuous renal replacement therapy was the most expensive therapy). Many studies reported favorable cost-effectiveness profiles (i.e., below $50,000 per life year or quality-adjusted life year). However, several therapies have since been proven harmful. Over 2 decades, relatively few cost-effectiveness studies in critical care have been published (average 4.6 studies per year). There has been a more recent trend toward using hypothetical cohorts and modeling scenarios without proven clinical data (2014-2018: 19/33 [58%]). CONCLUSIONS: Despite critical care being a significant healthcare cost burden there remains a paucity of studies in the literature evaluating its cost effectiveness.
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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 | 0.047 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.012 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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; both teacher heads agree on what is shown here.
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".