Ultrasound assessment of central venous pressure: A systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Ultrasound is increasingly relied upon to estimate central venous pressure (CVP) in the echocardiography lab and using point-of-care systems in the intensive care unit and the emergency department. However, there is uncertainty regarding the diagnostic accuracy of ultrasound-based parameters as reported in diverse studies.Methods A systematic review was performed by searching MEDLINE, EMBASE, and the Cochrane Database for studies evaluating ultrasound-based indicators of filling pressures in relation to catheterization-based CVP. Studies were screened for predefined inclusion criteria and rated for quality by duplicate observers. Standardized correlation coefficients for each ultrasound-based indicator were meta-analyzed using a random effects model.Results 3949 articles were screened and 64 met the criteria for inclusion. Inferior vena cava (IVC) diameter was assessed in 34 study measures and the pooled standardized correlation with invasive CVP was 0.74 (95% CI 0.63 to 0.84). IVC collapsibility was assessed in 20 study measures and the pooled standardized correlation with invasive CVP was -0.57 (95% CI -0.70 to -0.44). Tricuspid E/Ep was assessed in 6 study measures and the pooled standardized correlation with invasive CVP was 0.59 (95% CI 0.26 to 0.93). IVC parameters but not E/Ep remained correlated with CVP in mechanically ventilated patients, including cardiac surgery patients. Results were similar in studies featuring non-traditional users and cardiac specialists.Conclusions Echocardiographic IVC diameter, collapsibility, and tricuspid E/Ep ratio are significantly correlated with invasive CVP, albeit with important heterogeneity between studies. Most of these indicators are equally valid when applied in ventilated patients and by non-traditional users.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.032 |
| Bibliometrics | 0.006 | 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) | 0.004 | 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".