Does lung ultrasound‐guided ultrafiltration lead to better outcomes in acute kidney injury requiring intermittent hemodialysis: A randomized control trial
Bibliographic record
Abstract
INTRODUCTION: Optimization of ultrafiltration during hemodialysis is a critical parameter in achieving therapeutic efficacy and ensuring hemodynamic stability. While various modalities such as blood volume monitoring, inferior vena cava diameter assessment, natriuretic peptide levels, bioimpedance assay, and lung ultrasound have been widely explored in the context of maintenance hemodialysis, the concept of volume-guided ultrafiltration in dialysis patients with acute kidney injury remains unexplored. METHODS: Adult patients with acute kidney injury requiring dialysis, who were hemodynamically stable and not on ventilator support, without underlying lung pathology or cardiac failure, were randomized into two groups. All patients underwent 28-zone lung ultrasound before dialysis. The ultrafiltration was decided based on the treating physician's clinical judgment in controls. In the intervention group, the ultrafiltration orders prescribed by the treating physician were modified, based on the Kerley B line scores obtained by lung ultrasound. The rest of the dialysis prescriptions were similar. A postdialysis lung ultrasound was done in both groups to assess the postdialysis volume status 30 min after the dialysis session. RESULTS: A total of 74 patients undergoing hemodialysis for acute kidney injury were randomized. The baseline characteristics were comparable except for higher baseline B line score scores in the intervention arm. All patients received similar dialysis prescriptions. The lung ultrasound-guided ultrafiltration arm had a higher change in B line scores (BLS) from baseline (4 [0-9.5] vs. 0 [0-4]; p value 0.004) during the first dialysis session. The predialysis BLS indexed to ultrafiltration (mL/kbw/h) were significantly lower in controls, reflecting a relatively higher rate of ultrafiltration in controls compared with intervention (p = 0.006). The total number of dialysis sessions done in the control and intervention arm were 61 and 59, respectively. Among controls, 23/61 sessions (37.7%) had intradialytic adverse events, whereas, in the intervention arm, only 4/59 sessions (6.7) had any adverse intradialytic events (p < 0.01). CONCLUSION: Lung ultrasound-guided ultrafiltration was associated with a better safety profile, as demonstrated by reduced intradialytic events.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".