Dynamic changes of hepatic vein Doppler velocities predict preload responsiveness in mechanically ventilated critically ill patients
Bibliographic record
Abstract
BACKGROUND: Assessment of dynamic parameters to guide fluid administration is one of the mainstays of current resuscitation strategies. Each test has its own limitations, but passive leg raising (PLR) has emerged as one of the most versatile preload responsiveness tests. However, it requires real-time cardiac output (CO) measurement either through advanced monitoring devices, which are not routinely available, or echocardiography, which is not always feasible. Analysis of the hepatic vein Doppler waveform change, a simpler ultrasound-based assessment, during a dynamic test such as PLR could be useful in predicting preload responsiveness. The objective of this study was to assess the diagnostic accuracy of hepatic vein Doppler S and D-wave velocities during PLR as a predictor of preload responsiveness. METHODS: Prospective observational study conducted in two medical-surgical ICUs in Chile. Patients in circulatory failure and connected to controlled mechanical ventilation were included from August to December 2023. A baseline ultrasound assessment of cardiac function was performed. Then, simultaneously, ultrasound measurements of hepatic vein Doppler S and D waves and cardiac output by continuous pulse contour analysis device were performed during a PLR maneuver. RESULTS: Thirty-seven patients were analyzed. 63% of the patients were preload responsive defined by a 10% increase in CO after passive leg raising. A 20% increase in the maximum S wave velocity after PLR showed the best diagnostic accuracy with a sensitivity of 69.6% (49.1-84.4) and specificity of 92.8 (68.5-99.6) to detect preload responsiveness, with an area under curve of receiving operator characteristic (AUC-ROC) of 0.82 ± 0.07 (p = 0.001 vs. AUC-ROC of 0.5). D-wave velocities showed worse diagnostic accuracy. CONCLUSIONS: Hepatic vein Doppler assessment emerges as a novel complementary technique with adequate predictive capacity to identify preload responsiveness in patients in mechanical ventilation and circulatory failure. This technique could become valuable in scenarios of basic hemodynamic monitoring and when echocardiography is not feasible. Future studies should confirm these results.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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 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".