Proportional-assist ventilation with load-adjustable gain factors for mechanical ventilation: a cost-utility analysis
Bibliographic record
Abstract
BACKGROUND: Mechanical ventilation is an important component of patient critical care, but it adds expense to an already high-cost setting. This study evaluates the cost-utility of 2 modes of ventilation: proportional-assist ventilation with load-adjustable gain factors (PAV+ mode) versus pressure-support ventilation (PSV). METHODS: We adapted a published Markov model to the Canadian hospital-payer perspective with a 1-year time horizon. The patient population modelled includes all patients receiving invasive mechanical ventilation who have completed the acute phase of ventilatory support and have entered the recovery phase. Clinical and cost inputs were informed by a structured literature review, with the comparative effectiveness of PAV+ mode estimated via pragmatic meta-analysis. Primary outcomes of interest were costs, quality-adjusted life years (QALYs) and the (incremental) cost per QALY for patients receiving mechanical ventilation. Results were reported in 2017 Canadian dollars. We conducted probabilistic and scenario analyses to assess model uncertainty. RESULTS: Over 1 year, PSV had costs of $50 951 and accrued 0.25 QALYs. Use of PAV+ mode was associated with care costs of $43 309 and 0.29 QALYs. Compared to PSV, PAV+ mode was considered likely to be cost-effective, having lower costs (-$7642) and increased QALYs (+0.04) after 1 year. In cost-effectiveness acceptability analysis, 100% of simulations would be cost-effective at a willingness-to-pay threshold of $50 000 per QALY gained. INTERPRETATION: Use of PAV+ mode is expected to benefit patient care in the intensive care unit (ICU) and be a cost-effective alternative to PSV in the Canadian setting. Canadian hospital payers may therefore consider how best to optimally deliver mechanical ventilation in the ICU as they expand ICU capacity.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.005 | 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 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".