Dual doppler dynamics: integrating femoral venous doppler and VExUS for predicting organ dysfunction in acute heart failure
Bibliographic record
Abstract
BACKGROUND: Heart failure (HF) leads to venous congestion (VC), leading to organ dysfunction. Traditional VC assessments include pulmonary artery catheterization and IVC ultrasound. Newer tools like venous excess ultrasound (VExUS) and femoral venous doppler (FVD) quantify VC severity. We aimed to compare FVD with VExUS score to predict organ dysfunction and its progression in acute HF patients. METHODS: We conducted a 6-month prospective study in a 36-bed Cardiac ICU, enrolling 111 adults with acute decompensated HF. We evaluated FVD and VExUS to predict organ dysfunction and its progression. Key parameters were recorded on ICU admission and Day 3. We followed up patients at 90-days using the MAKE-90 criteria. Sensitivity, specificity, and predictive values of FVD and VExUS were calculated and compared using McNemar's test. RESULTS: VC was higher in the organ dysfunction group, with higher VExUS scores (55% vs. 31%, p = 0.018) and FVD-defined congestion (85% vs. 57%, p = 0.002). This group also revealed worse LUS, lower TAPSE:PASP ratios, more severe AKI, higher creatinine, and increased use of non-invasive ventilation (all p < 0.01). Mortality (39% vs. 24%) and MAKE-90 events (56% vs. 39%) were higher but not statistically significant. FVD had higher sensitivity but lower specificity than VExUS in detecting AKI, and lung congestion. VExUS had higher specificity for RV coupling and organ dysfunction; FVD correlated more with organ dysfunction. CONCLUSION: FVD and VExUS provide complementary insights into venous congestion, reinforcing the need for an integrated approach rather than reliance on a single modality. A multimodal strategy combining these tools with clinical and biochemical markers may offer a more precise framework for guiding management in acute heart failure. TRIAL REGISTRATION: This trial was registered with Clinical Trial Registry-India ( https://www.ctri.nic.in/ ), Trial No-CTRI/2023/10/058186 on 3/10/2023.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".