Learning curve for fenestrated-branched endovascular aortic repair using machine learning: A prospective national multicenter registry study

Document Type

Article

Publication Date

4-1-2026

Publication Title

Journal of vascular surgery

Keywords

Humans, Learning Curve, Endovascular Procedures, Aortic Aneurysm, Thoracic, Registries, Male, Female, Prospective Studies, Aged, Aortic Aneurysm, Abdominal, Blood Vessel Prosthesis Implantation, Treatment Outcome, Machine Learning, Time Factors, Clinical Competence, Risk Factors, Postoperative Complications, Aged, 80 and over, Prosthesis Design, Blood Vessel Prosthesis, Risk Assessment, United States, Middle Aged, Endovascular Aneurysm Repair

Abstract

OBJECTIVE: Fenestrated-branched endovascular aortic repair (F-BEVAR) is a complex procedure that requires significant experience and advanced technical proficiency. This study leverages machine learning methods to analyze the learning curve for F-BEVAR in the treatment of complex abdominal and thoracoabdominal aortic aneurysms (TAAAs).

METHODS: Patients undergoing three- and four-vessel F-BEVAR for intact complex abdominal aortic aneurysms (cAAAs) and TAAA between January 2014 and September 2024 were identified in the Society for Vascular Surgery Vascular Quality Initiative, a prospective, nationwide, multicenter registry. cAAA were defined as juxtarenal, pararenal, or suprarenal AAAs. F-BEVAR procedures performed by each individual surgeon were chronologically ordered and numbered to represent each physician's experience with F-BEVAR at the time of each operation. A deep learning neural network model was developed to quantify the learning curve by predicting outcome rates based on physician experience. Primary outcomes were perioperative mortality, procedural technical success, and major adverse events (MAEs), defined as the composite outcome of mortality, conversion to open surgery, spinal cord ischemia, visceral ischemia, renal ischemia, myocardial infarction, stroke, dialysis, pneumonia, and blood loss of >1000 mL. Secondary outcomes included perioperative aortic reintervention, procedural metrics (operative time, fluoroscopy time, blood loss, and contrast volume), and the individual components of the MAE composite measure.

RESULTS: A total of 5540 patients underwent F-BEVAR by 539 unique physicians, among whom 2956 patients underwent three- and four-vessel F-BEVAR. Of those, 64.4% (n = 1901) were treated for cAAA and 35.6% (n = 1055) for TAAA. The incidence of MAE gradually decreased from 31.0% (95% confidence interval [CI], 30.7%-31.1%) in initial procedures to a low of 18% (95% CI, 17.7%-18.1%) with increased physician experience, with a learning plateau in MAE rates (23%; 95% CI, 22.5%-23.2%) occurring between 40 and 70 procedures. Procedural technical success increased substantially from 91% (95% CI, 90.8%-91.5%) to 97% (95% CI, 96.5%-97.0%) with increasing physician experience, with a learning plateau observed once again between 40 and 70 procedures when a success rate of 96.0% (95% CI, 95.7%-96.2%) was achieved. Increasing physician experience was associated with a decrease in mortality from 4.4% (95% CI, 4.3%-4.6%) to 2.0% (95% CI, 1.9%-2.1%) and aortic reintervention rates from 8% (95% CI, 6.9%-8.1%) to 5% (95% CI, 4.9%-5.3%). Marked improvements were observed in procedural metrics with increasing physician experience, including blood loss declining from 551 mL (95% CI, 508-594 mL) to 179 mL (95% CI, 136-222 mL), fluoroscopy time decreasing from 91 minutes (95% CI, 87-95 minutes) to 55 minutes (95% CI, 51-59 minutes), and operative time decreasing from 320 minutes (95% CI, 306-331 minutes) to 242 minutes (95% CI, 230-255 minutes).

CONCLUSIONS: The learning curve for F-BEVAR demonstrates a proficiency plateau achieved after 40 to 70 cases, providing a valuable benchmark for surgeons and institutions adopting this complex procedure. As the use of F-BEVAR continues to grow in the treatment of cAAA and TAAA, these findings can help inform practice guidelines and support data-driven decision-making to ensure a safe and effective nationwide expansion of this procedure.

Medical Subject Headings

Humans; Learning Curve; Endovascular Procedures; Aortic Aneurysm, Thoracic; Registries; Male; Female; Prospective Studies; Aged; Aortic Aneurysm, Abdominal; Blood Vessel Prosthesis Implantation; Treatment Outcome; Machine Learning; Time Factors; Clinical Competence; Risk Factors; Postoperative Complications; Aged, 80 and over; Prosthesis Design; Blood Vessel Prosthesis; Risk Assessment; United States; Middle Aged; Endovascular Aneurysm Repair

PubMed ID

41265585

Volume

83

Issue

4

First Page

1023

Last Page

1033

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