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Contrast-enhanced ultrasound evaluation of renal perfusion before angioplasty and its predictive value for hypertension.
- Source :
- Technology & Health Care; 2024, Vol. 32 Issue 2, p963-976, 14p
- Publication Year :
- 2024
-
Abstract
- BACKGROUND: Atherosclerotic renal artery stenosis (ARAS) is a common disease in the elderly population. OBJECTIVE: The aim was to develop a contrast-enhanced ultrasound (CEUS)-based model for predicting post-angioplasty improvement in hypertension in patients with severe ARAS. METHODS: Thirty-five patients with severe ARAS (⩾ 70%) were included in this study, and 42 renal arteries received percutaneous transluminal renal arterial stenting. An optimal integral formula was developed from pre-interventional color-coded duplex sonography (CCDS) and CEUS parameters using least absolute shrinkage and selection operator (LASSO) regression and receiver operating characteristic (ROC) curve analysis. A model for predicting short-term hypertension improvement was established using the integral formula and clinical risk factors. Bootstrapping was used for internal validation. RESULTS: Two integral formulas, LASSO.CCDS and LASSO.CEUS, were established. ROC curves of the two integral formulas showed that LASSO.CEUS was the better formula for predicting hypertension improvement (AUC 0.816, specificity 78.6%). Univariate and multivariate regression analyses showed that duration of hypertension (OR 0.841, P = 0.027), diabetes (OR = 0.019, P = 0.010), and LASSO.CEUS (OR 7.641, P = 0.052) were predictors of short-term hypertension improvement after interventional therapy. Using LASSO.CEUS combined with clinical risk factors, the following prediction model was established: logit (short-term improvement in hypertension) = 1.879–0.173 × hypertension duration – 3.961 × diabetes + 2.034 × LASSO.CEUS (AUC 0.939). CONCLUSIONS: The model established using CEUS parameters and clinical risk factors could predict hypertension improvement after interventional therapy, but further research and verification are needed. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09287329
- Volume :
- 32
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- Technology & Health Care
- Publication Type :
- Academic Journal
- Accession number :
- 176365931
- Full Text :
- https://doi.org/10.3233/THC-230357