Factors associated with venous thromboembolism in the paediatric intensive care unit: A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Background Paediatric critical care practitioners have become increasingly concerned about venous thromboembolism (VTE). Although a wide range of factors associated with VTE in paediatric intensive care units (PICUs) have been identified, the strength and consistency of effect sizes are uncertain. Study Design Systematic review and meta‐analysis of case–control and cohort studies. We searched six electronic databases from inception to 20 June 2021. The study population consisted of hospitalized children aged 0–21 years. Our primary outcome was factors associated with VTE in the PICU. We pooled effect sizes as odds ratios (ORs) using random‐effects models for each factor that was examined in at least three distinct samples. We assessed study quality using the Newcastle–Ottawa Scale and examined between‐study heterogeneity. Results The meta‐analysis showed that age <1 year (OR = 1.80, 95% confidence interval [CI]: 1.08–3.01), sepsis (OR = 3.14, 95% CI: 1.55–6.38), central venous catheterization (CVC; OR = 7.76, 95% CI: 3.63–16.61), and mechanical ventilation (OR = 2.80, 95% CI: 1.90–4.13) was associated with an increased VTE risk. Conclusion We identified that infancy, sepsis, and the use of CVC and mechanical ventilation therapies are more likely to cause VTE in critically ill children. It is still necessary to identify the association between VTE and important clinical variables (e.g., postpubertal age, postoperative status). Relevance to Clinical Practice This review provides evidence that supports the factors associated with VTE in specific clinical settings and suggests that nurses should focus on the precise identification of the factors associated with VTE.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.042 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".