Effectiveness of <scp>contrast‐enhanced</scp> duplex ultrasound for detecting renal artery stenosis: A systematic review
Bibliographic record
Abstract
PURPOSE: Contrast-enhanced duplex ultrasound (CEUS) might be a useful tool to diagnosing renal artery stenosis (RAS). We amalgamated and reviewed the evidence assessing the diagnostic accuracy of CEUS on detecting RAS compared to angiography. METHODS: This preregistered systematic review included studies that compared the presence of RAS via CEUS with angiography. Sources were searched in November 2022 and included Scopus, EMBASE, MEDLINE, CINAHL, and Academic Search Premier (n = 1717). The Quality Assessment of Diagnostic Studies 2 tool assessed study quality. Results are presented narratively. RESULTS: The studies included (n = 11) had a total of 447 unique participants (193 females) and average age of 56 ± 9 years. Five of eleven studies investigated CEUS using SonoVue contrast agent and reported an average accuracy (91% ± 2%), sensitivity (91% ± 3%), specificity (90% ± 5%), negative predictive value (86% ± 6%), and positive predictive value (94% ± 1%) with all values >80%. The accuracy of CEUS using other types of contrast agent (n = 6), including Levovsit (n = 3/6), Definity (n = 1/6), perfienapent emulsion (n = 1/6), and perfluorocarbon-exposed sonicated dextrose albumin (n = 1/6) was mixed. These studies detected an average accuracy of 91 ± 11% (n = 2/3% > 80%), sensitivity of 98% ± 4%, (n = 3/3% > 80%), and specificity of 86% ± 10% (n = 2/3% > 80%). Included studies had generally low risk of bias and applicability concerns except for unclear flow and timing (n = 7/11) and applicability of patient selection (n = 4/11). CONCLUSION: Despite being limited by the heterogeneity of included studies, our review indicates a high overall diagnostic accuracy for CEUS to detect RAS compared to angiography, with the largest evidence-base for SonoVue contrast. Radiologists and hospital decision makers should consider CEUS as an acceptable alternative to angiography.
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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 | 0.010 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".