Volume Status Assessment by Lung Ultrasound in End-Stage Kidney Disease: A Systematic Review
Bibliographic record
Abstract
Purpose of review: Lung ultrasound is a noninvasive bedside technique that can accurately assess pulmonary congestion by evaluating extravascular lung water. This technique is expanding and is easily available. Our primary outcome was to compare the efficacy of volume status assessment by lung ultrasound with clinical evaluation, echocardiography, bioimpedance, or biomarkers. The secondary outcomes were all-cause mortality and cardiovascular events. Sources of information: We conducted a MEDLINE literature search for observational and randomized studies with lung ultrasound in patients on maintenance dialysis. Methods: From a total of 2363 articles, we included 28 studies (25 observational and 3 randomized). The correlation coefficients were pooled for each variable of interest using the generic inverse variance method with a random effects model. Among the clinical parameters, New York Heart Association Functional Classification of Heart Failure status and lung auscultation showed the highest correlation with the number of B-lines on ultrasound, with a pooled r correlation coefficient of .57 and .36, respectively. Among echocardiographic parameters, left ventricular ejection fraction and inferior vena cava index had the strongest correlation with the number of B-lines, with a pooled r coefficient of .35 and .31, respectively. Three randomized studies compared a lung ultrasound-guided approach with standard of care on hard clinical endpoints. Although patients in the lung ultrasound group achieved better decongestion and blood pressure control, there was no difference between the 2 management strategies with respect to death from any cause or major adverse cardiovascular events. Key findings: Lung ultrasound may be considered for the identification of patients with subclinical volume overload. Trials did not show differences in clinically important outcomes. The number of studies was small and many were of suboptimal quality. Limitations: The included studies were heterogeneous and of relatively limited quality.
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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 | 0.002 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".