Prospective Study of Ultrasound Markers of Organ Congestion in Critically Ill Patients With Acute Kidney Injury
Bibliographic record
Abstract
Introduction Organ congestion may be a mediator of adverse outcomes in critically ill patients with severe acute kidney injury (AKI). The presence of abnormal venous Doppler waveforms could identify patients with clinically significant organ congestion who may benefit from a decongestive strategy. Methods This prospective multicenter cohort study enrolled patients with severe AKI defined as KDIGO stage 2 or higher. Patients were not eligible if they received renal replacement therapy (RRT) for more than 72 hours at the time of screening. Participants underwent serial Doppler ultrasound examinations of the portal, hepatic and intra-renal veins during the week following enrolment. We calculated the Venous Excess UltraSound (VExUS) score based on these data. The primary outcome studied was major adverse kidney events at 30 days (MAKE30) defined as death, RRT dependence, or a persistent decrease in kidney function. Results A total of 125 patients were included for whom 291 ultrasound assessments were performed. Severely abnormal venous waveforms were documented in 14.4% of portal vein assessments, 6.5% of intra-renal venous assessments, and 14.4% of hepatic vein assessments. The individual ultrasound markers were not associated with MAKE30. The VExUS score (Grade 0-1: Ref, Grade 2 – adjusted hazard ratio (aHR): 4.03, CI: 1.81; 8.99, Grade 3 – aHR: 2.70, CI: 1.10; 6.65, p=0.03), as well as severely abnormal portal, hepatic and intra-renal vein Doppler were each independently associated with mortality. Conclusions While not significantly associated with MAKE30, venous Doppler abnormalities suggestive of venous congestion were associated with higher mortality in critically ill patients with severe AKI. Trial registration Clinicaltrials.gov identifier: NCT04095143 (https://clinicaltrials.gov/ct2/show/NCT04095143), registered on September 19, 2019 (retrospectively registered)
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".