Artificial neural networks analysis predicts long-term fistula function in hemodialysis patients following percutaneous transluminal angioplasty
Kidney failure is particularly common in the United States, where it affects over 700,000 individuals. It is typically treated through repeated sessions of hemodialysis to filter and clean the blood. Hemodialysis requires vascular access, in about 70% of cases through an arteriovenous fistula (AVF)...
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Elsevier
2024-06-01
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| author | Aichi Chien Ayush Lall Maitraya Patel Lucas Cusumano Justin McWilliams |
| author_facet | Aichi Chien Ayush Lall Maitraya Patel Lucas Cusumano Justin McWilliams |
| author_sort | Aichi Chien |
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| description | Kidney failure is particularly common in the United States, where it affects over 700,000 individuals. It is typically treated through repeated sessions of hemodialysis to filter and clean the blood. Hemodialysis requires vascular access, in about 70% of cases through an arteriovenous fistula (AVF) surgically created by connecting an artery and vein. AVF take 6 weeks or more to mature. Mature fistulae often require intervention, most often percutaneous transluminal angioplasty (PTA), also known as fistulaplasty, to maintain the patency of the fistula. PTA is also the first-line intervention to restore blood flow and prolong the use of an AVF, and many patients undergo the procedure multiple times. Although PTA is important for AVF maturation and maintenance, research into predictive models of AVF function following PTA has been limited. Therefore, in this paper we hypothesize that based on patient-specific information collected during PTA, a predictive model can be created to help improve treatment planning. We test a set of rich, multimodal data from 28 patients that includes medical history, AVF blood flow, and interventional angiographic imaging (specifically excluding any post-PTA measurements) and build deep hybrid neural networks. A hybrid model combining a 3D convolutional neural network with a multi-layer perceptron to classify AVF was established. We found using this model that we were able to identify the association between different factors and evaluate whether the PTA procedure can maintain primary patency for more than 3 months. The testing accuracy achieved was 0.75 with a weighted F1-score of 0.75, and AUROC of 0.75. These results indicate that evaluating multimodal clinical data using artificial neural networks can predict the outcome of PTA. These initial findings suggest that the hybrid model combining clinical data, imaging and hemodynamic analysis can be useful to treatment planning for hemodialysis. Further study based on a large cohort is needed to refine the accuracy and model efficiency. |
| format | Article |
| id | doaj-art-378342d2d33e4ab49f060d2a485db62c |
| institution | Kabale University |
| issn | 2950-4899 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Elsevier |
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| series | EngMedicine |
| spelling | doaj-art-378342d2d33e4ab49f060d2a485db62c2025-01-11T06:42:27ZengElsevierEngMedicine2950-48992024-06-0111100010Artificial neural networks analysis predicts long-term fistula function in hemodialysis patients following percutaneous transluminal angioplastyAichi Chien0Ayush Lall1Maitraya Patel2Lucas Cusumano3Justin McWilliams4Department of Radiology, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA; Corresponding author.Department of Radiology, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USADivision of Ultrasonography, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USADepartment of Radiology, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USADepartment of Radiology, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USAKidney failure is particularly common in the United States, where it affects over 700,000 individuals. It is typically treated through repeated sessions of hemodialysis to filter and clean the blood. Hemodialysis requires vascular access, in about 70% of cases through an arteriovenous fistula (AVF) surgically created by connecting an artery and vein. AVF take 6 weeks or more to mature. Mature fistulae often require intervention, most often percutaneous transluminal angioplasty (PTA), also known as fistulaplasty, to maintain the patency of the fistula. PTA is also the first-line intervention to restore blood flow and prolong the use of an AVF, and many patients undergo the procedure multiple times. Although PTA is important for AVF maturation and maintenance, research into predictive models of AVF function following PTA has been limited. Therefore, in this paper we hypothesize that based on patient-specific information collected during PTA, a predictive model can be created to help improve treatment planning. We test a set of rich, multimodal data from 28 patients that includes medical history, AVF blood flow, and interventional angiographic imaging (specifically excluding any post-PTA measurements) and build deep hybrid neural networks. A hybrid model combining a 3D convolutional neural network with a multi-layer perceptron to classify AVF was established. We found using this model that we were able to identify the association between different factors and evaluate whether the PTA procedure can maintain primary patency for more than 3 months. The testing accuracy achieved was 0.75 with a weighted F1-score of 0.75, and AUROC of 0.75. These results indicate that evaluating multimodal clinical data using artificial neural networks can predict the outcome of PTA. These initial findings suggest that the hybrid model combining clinical data, imaging and hemodynamic analysis can be useful to treatment planning for hemodialysis. Further study based on a large cohort is needed to refine the accuracy and model efficiency.http://www.sciencedirect.com/science/article/pii/S2950489924000101AngioplastyClinical predictive modelsDeep learningFistulaHemodialysisMedical informatics |
| spellingShingle | Aichi Chien Ayush Lall Maitraya Patel Lucas Cusumano Justin McWilliams Artificial neural networks analysis predicts long-term fistula function in hemodialysis patients following percutaneous transluminal angioplasty EngMedicine Angioplasty Clinical predictive models Deep learning Fistula Hemodialysis Medical informatics |
| title | Artificial neural networks analysis predicts long-term fistula function in hemodialysis patients following percutaneous transluminal angioplasty |
| title_full | Artificial neural networks analysis predicts long-term fistula function in hemodialysis patients following percutaneous transluminal angioplasty |
| title_fullStr | Artificial neural networks analysis predicts long-term fistula function in hemodialysis patients following percutaneous transluminal angioplasty |
| title_full_unstemmed | Artificial neural networks analysis predicts long-term fistula function in hemodialysis patients following percutaneous transluminal angioplasty |
| title_short | Artificial neural networks analysis predicts long-term fistula function in hemodialysis patients following percutaneous transluminal angioplasty |
| title_sort | artificial neural networks analysis predicts long term fistula function in hemodialysis patients following percutaneous transluminal angioplasty |
| topic | Angioplasty Clinical predictive models Deep learning Fistula Hemodialysis Medical informatics |
| url | http://www.sciencedirect.com/science/article/pii/S2950489924000101 |
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