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 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 | 0.025 | 0.135 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.015 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".