Utilization of Ultrasound to Assess Volume Status in Heart Failure
Bibliographic record
Abstract
Heart failure (HF) represents a significant financial burden to the US health care system, affecting approximately 5.7 million Americans. By 2030, the prevalence of HF is expected to increase by 23%. Clinicians generally evaluate volume status in patients with HF by visualizing jugular venous distension to estimate right atrial pressure; a method with an estimated accuracy of only 50%. Currently, the only endorsed methods for acute HF diagnosis in the 2017 American College of Cardiology (ACC) guidelines are brain natriuretic peptide (BNP) or N-terminal pro-B-type natriuretic peptide (NT-proBNP), pre-discharge BNP or NT-proBNP, and myocardial fibrosis markers. However, serial testing of BNP to monitor therapy remains controversial. Moreover, an elevated BNP cannot be attributed solely to a cardiac cause. Given the limitations of the current methods, a robust tool is needed to reliably assess volume status in HF patients. It is now known that hemodynamic congestion from increases in intracardiac pressure occurs days to weeks prior to the onset of typical HF symptoms, such as weight gain and shortness of breath. It has been postulated that assessing the inferior vena cava (IVC) diameter with a portable ultrasound, may be the simple, reliable, and cost-effective method of evaluating right atrial pressure, and thus, the severity of HF. Given this exciting new tool in assessing volume status in patients with HF, we pose the question of whether this imaging modality can be used to risk-stratify patients and guide management. The aim of this paper is to highlight the many benefits of portable ultrasound in assessing volume status in this population, and to discuss whether this imaging modality can help guide physicians in the management of their HF patients.
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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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.005 |
| 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".