Predicting Long-Term Patency of Radiocephalic Arteriovenous Fistulas with Machine Learning and the PREDICT-AVF Web App Article Swipe
James J. Fitzgibbon
,
Mengyuan Ruan
,
Patrick Heindel
,
Abena Appah-Sampong
,
Tanujit Dey
,
Ali Haider Khan
,
Dirk M. Hentschel
,
C. Keith Ozaki
,
Mohamad A. Hussain
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-4389336/v1
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-4389336/v1
The goal of this study was to expand our previously created prediction tool (PREDICT-AVF) and web app by estimating long-term primary and secondary patency of radiocephalic AVFs. The data source was 911 patients from PATENCY-1 and PATENCY-2 randomized controlled trials, which enrolled patients undergoing new radiocephalic AVF creation with prospective longitudinal follow up and ultrasound measurements. Models were built using a combination of baseline characteristics and post-operative ultrasound measurements to estimate patency up to 2.5 years. Discrimination performance was assessed, and an interactive web app was created using the most robust model. At 2.5 years, the unadjusted primary and secondary patency (95% CI) was 29% (26%-33%) and 68% (65%-72%). Models using baseline characteristics generally did not perform as well as those using post-operative ultrasound measurements. Overall, the Cox model(4-6 weeks ultrasound) had the best discrimination performance for primary and secondary patency, with an integrated Brier score of 0.183 (0.167, 0.199) and 0.106 (0.085, 0.126). Expansion of the PREDICT-AVF web app to include prediction of long-term patency can help guide clinicians in developing comprehensive end-stage kidney disease Life-Plans with hemodialysis access patients.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-4389336/v1
- https://www.researchsquare.com/article/rs-4389336/latest.pdf
- OA Status
- gold
- References
- 30
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4399164947
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4399164947Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.21203/rs.3.rs-4389336/v1Digital Object Identifier
- Title
-
Predicting Long-Term Patency of Radiocephalic Arteriovenous Fistulas with Machine Learning and the PREDICT-AVF Web AppWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-05-30Full publication date if available
- Authors
-
James J. Fitzgibbon, Mengyuan Ruan, Patrick Heindel, Abena Appah-Sampong, Tanujit Dey, Ali Haider Khan, Dirk M. Hentschel, C. Keith Ozaki, Mohamad A. HussainList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-4389336/v1Publisher landing page
- PDF URL
-
https://www.researchsquare.com/article/rs-4389336/latest.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://www.researchsquare.com/article/rs-4389336/latest.pdfDirect OA link when available
- Concepts
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Medicine, Brier score, Hemodialysis, Arteriovenous fistula, Baseline (sea), Ultrasound, Prospective cohort study, Proportional hazards model, Radiology, Internal medicine, Surgery, Computer science, Machine learning, Geology, OceanographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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30Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.enrolled | 42 |
| abstract_inverted_index.estimate | 71 |
| abstract_inverted_index.patency, | 141 |
| abstract_inverted_index.patients | 33, 43 |
| abstract_inverted_index.(26%-33%) | 106 |
| abstract_inverted_index.Expansion | 155 |
| abstract_inverted_index.PATENCY-1 | 35 |
| abstract_inverted_index.PATENCY-2 | 37 |
| abstract_inverted_index.assessed, | 80 |
| abstract_inverted_index.end-stage | 174 |
| abstract_inverted_index.generally | 114 |
| abstract_inverted_index.long-term | 20, 165 |
| abstract_inverted_index.model(4-6 | 129 |
| abstract_inverted_index.patients. | 181 |
| abstract_inverted_index.secondary | 23, 100, 140 |
| abstract_inverted_index.(65%-72%). | 109 |
| abstract_inverted_index.Life-Plans | 177 |
| abstract_inverted_index.clinicians | 170 |
| abstract_inverted_index.controlled | 39 |
| abstract_inverted_index.developing | 172 |
| abstract_inverted_index.estimating | 19 |
| abstract_inverted_index.integrated | 144 |
| abstract_inverted_index.prediction | 12, 163 |
| abstract_inverted_index.previously | 10 |
| abstract_inverted_index.randomized | 38 |
| abstract_inverted_index.ultrasound | 55, 68, 124 |
| abstract_inverted_index.unadjusted | 97 |
| abstract_inverted_index.undergoing | 44 |
| abstract_inverted_index.PREDICT-AVF | 158 |
| abstract_inverted_index.combination | 62 |
| abstract_inverted_index.interactive | 83 |
| abstract_inverted_index.performance | 78, 136 |
| abstract_inverted_index.prospective | 50 |
| abstract_inverted_index.ultrasound) | 131 |
| abstract_inverted_index.hemodialysis | 179 |
| abstract_inverted_index.longitudinal | 51 |
| abstract_inverted_index.measurements | 69 |
| abstract_inverted_index.(PREDICT-AVF) | 14 |
| abstract_inverted_index.comprehensive | 173 |
| abstract_inverted_index.measurements. | 56, 125 |
| abstract_inverted_index.radiocephalic | 26, 46 |
| abstract_inverted_index.Discrimination | 77 |
| abstract_inverted_index.discrimination | 135 |
| abstract_inverted_index.post-operative | 67, 123 |
| abstract_inverted_index.characteristics | 65, 113 |
| abstract_inverted_index.<title>Abstract</title> | 0 |
| cited_by_percentile_year | |
| countries_distinct_count | 3 |
| institutions_distinct_count | 9 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.4300000071525574 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.15692733 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |