Mucoactive agents for acute respiratory failure in the critically ill: a systematic review and meta-analysis
Bibliographic record
Abstract
Purpose Acute respiratory failure (ARF) is a common cause of admission to intensive care units (ICUs). Mucoactive agents are medications that promote mucus clearance and are frequently administered in patients with ARF, despite a lack of evidence to underpin clinical decision making. The aim of this systematic review was to determine if the use of mucoactive agents in patients with ARF improves clinical outcomes. Methods We searched electronic and grey literature (January 2020). Two reviewers independently screened, selected, extracted data and quality assessed studies. We included trials of adults receiving ventilatory support for ARF and involving at least one mucoactive agent compared with placebo or standard care. Outcomes included duration of mechanical ventilation. Meta-analysis was undertaken using random-effects modelling and certainty of the evidence was assessed using Grades of Recommendation, Assessment, Development and Evaluation. Results Thirteen randomised controlled trials were included (1712 patients), investigating four different mucoactive agents. Mucoactive agents showed no effect on duration of mechanical ventilation (seven trials, mean difference (MD) −1.34, 95% CI −2.97 to 0.29, I 2 =82%, very low certainty) or mortality, hospital stay and ventilator-free days. There was an effect on reducing ICU length of stay in the mucoactive agent groups (10 trials, MD −3.22, 95% CI −5.49 to −0.96, I 2 =89%, very low certainty). Conclusion Our findings do not support the use of mucoactive agents in critically ill patients with ARF. The existing evidence is of low quality. High-quality randomised controlled trials are needed to determine the role of specific mucoactive agents in critically ill patients with ARF. PROSPERO registration number CRD42018095408.
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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 | 0.017 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.023 | 0.037 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".