The use of lung ultrasound in evaluation of extravascular lung water in hemodialysis patients: Systematic review and meta‐analysis
Bibliographic record
Abstract
RATIONALE AND OBJECTIVES: Determining dry weight is crucial for optimizing hemodialysis, influencing efficacy, cardiovascular outcomes, and overall survival. Traditional clinical assessment methods for dry weight, relying on factors such as blood pressure and edema, frequently lack reliability. Lung ultrasound stands out as a promising tool for assessing volume status, given its non-invasiveness and reproducibility. This study aims to explore the role of Lung ultrasound in evaluating the impact of hemodialysis and ultrafiltration on extravascular lung water, with a specific focus on changes in B-lines post-hemodialysis compared to pre-hemodialysis. MATERIALS AND METHODS: The research encompassed searches across PubMed, WOS, and Scopus databases for studies related to lung ultrasound and hemodialysis. A meta-analysis was then performed to determine the mean differences in various parameters before compared to after, hemodialysis, including the number of B-lines, indexed end-inspiratory and end-expiratory inferior vena cava diameters, inferior vena cava collapsibility index, weight, blood pressure, and serum levels of NT-pro-BNP. RESULTS: Our meta-analysis, included 33 studies with 2301 hemodialysis patients, revealed a significant decrease in the number of B-lines post-hemodialysis (mean difference = 8.30, 95% CI [3.55 to 13.05]). Furthermore, there was a noteworthy reduction in inspiratory and expiratory inferior vena cava diameters post-hemodialysis (mean difference = 2.32, 95% CI [0.31 to 4.33]; mean difference = 4.05, 95% CI [2.44 to 5.65], respectively). Additionally, a significant positive correlation was observed between B-lines and the maximum inferior vena cava diameter both pre- and post-hemodialysis (correlation coefficient = 0.39; correlation coefficient = 0.32, respectively). CONCLUSION: These findings indicate the effectiveness of lung ultrasound in detection of volume overload and assessment of response to ultrafiltration in hemodialysis patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".