Brain Natriuretic Peptide to Predict Successful Liberation from Mechanical Ventilation in Critically Ill Patients: a Systematic Review and Meta-Analysis.
Bibliographic record
Abstract
Abstract Background Predicting successful liberation from mechanical ventilation (MV) in critically ill patients is challenging. Brain natriuretic peptide (BNP) has been proposed to help guide decision-making for readiness to liberate from MV following a spontaneous breathing trial (SBT).Methods We performed a systematic review and meta-analysis of randomized and prospective observational studies that measured BNP levels at the time of SBT in patients receiving MV. The primary endpoint was successful liberation from MV (absence of re-intubation or non-invasive ventilation at 48h). Statistical analyses include bi-variate and Moses-Littenberg models, and DerSimonian-Laird pooling of areas under ROC curve (AUROC).Results A total of 731 articles were screened. After pre-specified inclusion criteria, 18 adult and 2 pediatric studies were included. The measure of the relative variation of BNP during SBT (ΔBNP%) after exclusion of SBT failure by clinical criteria in adults yielded a sensitivity and specificity of 0.889 [0.831-0.929] and 0.828 [0.730-0.896] respectively, with a pooled AUROC of 0.92 [0.88-0.97] for successful liberation from MV. The pooled AUROC for any method of analysis for absolute variation of BNP (ΔBNP), pre-SBT BNP and post-SBT BNP were 0.89 [0.83-0.95], 0.77 [0.63-0.91], and 0.85 [0.80-0.90], respectively.Conclusion The ΔBNP during a SBT is an accurate incremental tool after SBT success by clinical indices to predict successful liberation from MV in adults. There is insufficient data to support its use as an alternate test to clinical indices of SBT or the use of ΔBNP, BNP-pre and BNP-post as an alternate or incremental test, or in pediatric patients.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.031 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".