Quantifying systemic congestion with Point-Of-Care ultrasound: development of the venous excess ultrasound grading system
Bibliographic record
Abstract
BACKGROUND: Organ congestion is a mediator of adverse outcomes in critically ill patients. Point-Of-Care ultrasound (POCUS) is widely available and could enable clinicians to detect signs of venous congestion at the bedside. The aim of this study was to develop several grading system prototypes using POCUS and to determine their respective ability to predict acute kidney injury (AKI) after cardiac surgery. This is a post-hoc analysis of a single-center prospective study in 145 patients undergoing cardiac surgery for which repeated daily measurements of hepatic, portal, intra-renal vein Doppler and inferior vena cava (IVC) ultrasound were performed during the first 72 h after surgery. Five prototypes of venous excess ultrasound (VExUS) grading system combining multiple ultrasound markers were developed. RESULTS: The association between each score and AKI was assessed using time-dependant Cox models as well as conventional performance measures of diagnostic testing. A total of 706 ultrasound assessments were analyzed. We found that defining severe venous congestion as the presence of severe flow abnormalities in multiple Doppler patterns with a dilated IVC (≥ 2 cm) showed the strongest association with the development of subsequent AKI compared with other combinations (HR: 3.69 CI 1.65-8.24 p = 0.001). The association remained significant after adjustment for baseline risk of AKI and vasopressor/inotropic support (HR: 2.82 CI 1.21-6.55 p = 0.02). Furthermore, this severe VExUS grade offered a useful positive likelihood ratio (+LR: 6.37 CI 2.19-18.50) when detected at ICU admission, which outperformed central venous pressure measurements. CONCLUSIONS: The combination of multiple POCUS markers may identify clinically significant venous congestion.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".