Randomised controlled trials for the prevention of venous thromboembolism in critically ill patients: A systematic review of sample size methodologies
Bibliographic record
Abstract
Venous thromboembolism (VTE), encompassing deep vein thrombosis and pulmonary embolism, contributes significantly to morbidity and mortality in critically ill patients. This increased incidence is influenced by factors such as indwelling central venous catheters, continuous sedation, and vasoactive infusions, in addition to traditional VTE risk factors such as immobility. Optimal VTE prophylaxis remains uncertain owing to significant variability with regard to differences across existing randomized controlled trials (RCTs) in terms of patient populations, types and dosing of prophylactic interventions, and definitions of VTE outcomes. RCTs conducted on critically ill patients are often limited by over-optimistic estimates of baseline event rates and potential treatment benefit. We conducted a systematic review to evaluate the described sample size methodologies and justification amongst VTE-prevention RCTs for critically ill patients. We searched MEDLINE and Embase from inception until 9 September 2024, and included 12 RCTs enrolling 9154 patients. Sample sizes ranged from 36 to 3746 participants. Only six studies met their recruitment targets, while four did not report a sample size calculation. Among the seven studies that reported an estimated control event rate, only one met that estimate. No studies achieved their planned absolute risk reduction, and only one met its relative risk reduction target. Prognostic enrichment strategies were employed in 41.6% of trials, while predictive enrichment was rarely used. These findings suggest that intensive care unit VTE prophylaxis trials frequently overestimate baseline risk and treatment effect size, contributing to underpowered studies and potential premature abandonment of promising interventions. Future trials should incorporate enrichment strategies and justify assumptions based on clinical relevance and patient-centred outcomes.
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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 | 0.088 | 0.261 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.014 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".