The Incidence, Risk Factors, and Hospital Mortality of Prolonged Mechanical Ventilation among Cardiac Surgery Patients: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Prolonged mechanical ventilation (PMV) is a common complication after cardiac surgery and is considered a risk factor for poor outcomes. However, the incidence and in-hospital mortality of PMV among cardiac surgery patients reported in studies vary widely, and risk factors are controversial. Methods: We searched four databases (Web of Science, Cochrane Library, PubMed, and EMBASE) for English-language articles from inception to October 2023. The odds ratio (OR), 95% confidence interval (CI), PMV incidence, and in-hospital mortality were extracted. Statistical data analysis was performed using Stata software. We calculated the fixed or random effects model according to the heterogeneity. The quality of each study was appraised by two independent reviewers using the Newcastle–Ottawa scale. Results: Thirty-two studies were included. The incidence of PMV was 20%. Twenty-one risk factors were pooled, fifteen risk factors were found to be statistically significant (advanced age, being female, ejection fraction <50, body mass index (BMI), BMI >28 kg/m2, New York Heart Association Class ≥Ⅲ, chronic obstructive pulmonary disease, chronic renal failure, heart failure, arrhythmia, previous cardiac surgery, higher white blood cell count, creatinine, longer cardiopulmonary bypass (CPB) time, and CPB >120 min). In addition, PMV was associated with increased in-hospital mortality (OR, 14.13, 95% CI, 12.16–16.41, I2 = 90.3%, p < 0.01). Conclusions: The PMV incidence was 20%, and it was associated with increased in-hospital mortality. Fifteen risk factors were identified. More studies are needed to prevent PMV more effectively according to these risk factors. The PROSPERO Registration: This systematic review and meta-analysis was recorded at PROSPERO (CRD42021273953, https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=273953).
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.038 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".