Academic Journal
Computational Frontiers in Arteriovenous Fistula Maturation: A Review of Fluid Dynamics and Machine Learning Models.
| Title: | Computational Frontiers in Arteriovenous Fistula Maturation: A Review of Fluid Dynamics and Machine Learning Models. |
|---|---|
| Authors: | Nowacki A; Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas., Ramirez-Mireles L; Department of Physics, Rice University, Houston, Texas., Barcena AJR; Department of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas., Marks AE; Department of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas.; The University of Texas MD Anderson Cancer Center, Graduate School of Biomedical Sciences, UTHealth Houston, Houston, Texas., Huang SY; Department of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas., Castillo E; Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas., Melancon MP; Department of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas.; The University of Texas MD Anderson Cancer Center, Graduate School of Biomedical Sciences, UTHealth Houston, Houston, Texas. |
| Source: | Journal of the American Society of Nephrology : JASN [J Am Soc Nephrol] 2026 Jul 01; Vol. 37 (7), pp. 1580-1598. Date of Electronic Publication: 2026 Apr 17. |
| Publication Type: | Journal Article; Review |
| Language: | English |
| Journal Info: | Publisher: Wolters Kluwer Health, on behalf of the American Society of Nephrology Country of Publication: United States NLM ID: 9013836 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1533-3450 (Electronic) Linking ISSN: 10466673 NLM ISO Abbreviation: J Am Soc Nephrol Subsets: MEDLINE |
| Imprint Name(s): | Publication: 2023- : Hagerstown, MD : Wolters Kluwer Health, on behalf of the American Society of Nephrology Original Publication: Baltimore, MD : Williams & Wilkins, c1990- |
| MeSH Terms: | Machine Learning* , Arteriovenous Shunt, Surgical* , Hydrodynamics* , Renal Dialysis* , Computer Simulation*, Humans ; Hemodynamics ; Predictive Learning Models |
| Abstract: | Arteriovenous (AV) fistulas, the preferred vascular access for hemodialysis, fail to mature in up to 60% of patients with kidney failure. This high failure rate is often attributed to adverse hemodynamic conditions, yet the exact mechanisms remain poorly understood. This review explores the application of computational fluid dynamics and machine learning to elucidate these mechanisms and predict clinical outcomes. Computational fluid dynamic models have been instrumental in characterizing the complex interplay between AV fistula geometry, such as anastomotic angle and curvature, and hemodynamic parameters, such as wall shear stress and oscillatory shear index. These studies consistently link disturbed flow patterns, including low wall shear stress and high oscillatory shear index, to regions prone to neointimal hyperplasia and stenosis. Concurrently, machine learning models have demonstrated significant promise in predicting AV fistula maturation, stenosis, and failure by leveraging diverse data sources, including clinical characteristics, ultrasound imaging, and acoustic bruit analysis. While powerful, the clinical utility of these computational models is often limited by small, single-center datasets, a lack of external validation, and simplifying assumptions that may not capture true physiological complexity. Future progress depends on integrating these complementary approaches, using larger and more diverse datasets, and validating models prospectively to create generalizable tools that can guide surgical planning and improve AV fistula maturation rates. (Copyright © 2026 by the American Society of Nephrology.) |
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| Grant Information: | 15BGIA25690005 United States AHA American Heart Association-American Stroke Association; R01 HL159960 United States HL NHLBI NIH HHS; T32 EB007507 United States EB NIBIB NIH HHS; 1TL1DK147564-01 NIH NIDDK - Houston Area Incubator for Kidney, Urologic and Hematologic Research Training; 5R01HL159960-04 National Institutes of Health - National Heart Lung and Blood Institute |
| Contributed Indexing: | Keywords: arteriovenous fistula; artificial intelligence; dialysis; imaging |
| Entry Date(s): | Date Created: 20260417 Date Completed: 20260624 Latest Revision: 20260726 |
| Update Code: | 20260726 |
| PubMed Central ID: | PMC13337174 |
| DOI: | 10.1681/ASN.0000001123 |
| PMID: | 41996194 |
| Database: | MEDLINE |
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