Outcomes and Complications After Endovascular Treatment of Brain Arteriovenous Malformations: A Prognostication Attempt Using Artificial Intelligence.
OBJECTIVE
To identify factors influencing outcome in brain arteriovenous malformations (BAVM) treated with endovascular embolization. We also assessed the feasibility of using machine learning techniques to prognosticate and predict outcome and compared this to conventional statistical analyses.


METHODS
A retrospective study of patients undergoing endovascular treatment of BAVM during a 22-year period in a national neuroscience center was performed. Clinical presentation, imaging, procedural details, complications, and outcome were recorded. The data was analyzed with artificial intelligence techniques to identify predictors of outcome and assess accuracy in predicting clinical outcome at final follow-up.


RESULTS
One-hundred ninety-nine patients underwent treatment for BAVM with a mean follow-up duration of 63 months. The commonest clinical presentation was intracranial hemorrhage (56%). During the follow-up period, there were 51 further hemorrhagic events, comprising spontaneous hemorrhage (n = 27) and procedural related hemorrhage (n = 24). All spontaneous events occurred in previously embolized BAVMs remote from the procedure. Complications included ischemic stroke in 10%, symptomatic hemorrhage in 9.8%, and mortality rate of 4.7%. Standard regression analysis model had an accuracy of 43% in predicting final outcome (mortality), with the type of treatment complication identified as the most important predictor. The machine learning model showed superior accuracy of 97.5% in predicting outcome and identified the presence or absence of nidal fistulae as the most important factor.


CONCLUSIONS
BAVMs can be treated successfully by endovascular techniques or combined with surgery and radiosurgery with an acceptable risk profile. Machine learning techniques can predict final outcome with greater accuracy and may help individualize treatment based on key predicting factors.
Neurointerventional Service, Department of Radiology, Beaumont Hospital, Dublin, Ireland; School of Medicine, Faculty of Health, Deakin University, Waurn Ponds, Australia. Electronic address: asadi.hamed@gmail.com.
Hamed
H
Asadi
Interventional Radiology Service, Department of Radiology, Beaumont Hospital, Dublin, Ireland.
Hong Kuan
HK
Kok
Neurointerventional Service, Department of Radiology, Beaumont Hospital, Dublin, Ireland.
Seamus
S
Looby
Neurointerventional Service, Department of Radiology, Beaumont Hospital, Dublin, Ireland.
Paul
P
Brennan
Neurointerventional Service, Department of Radiology, Beaumont Hospital, Dublin, Ireland.
Alan
A
O'Hare
Neurointerventional Service, Department of Radiology, Beaumont Hospital, Dublin, Ireland.
John
J
Thornton
D000328:Adult; D001185:Artificial Intelligence; D002533:Cerebral Angiography; D057510:Endovascular Procedures / Q000009:adverse effects*; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D002538:Intracranial Arteriovenous Malformations / Q000000981:diagnostic imaging / Q000601:surgery*; D020300:Intracranial Hemorrhages / Q000000981:diagnostic imaging / Q000209:etiology; D008297:Male; D008875:Middle Aged; D017063:Outcome Assessment, Health Care; D011183:Postoperative Complications / Q000175:diagnosis / Q000000981:diagnostic imaging / Q000209:etiology; D012189:Retrospective Studies; D016896:Treatment Outcome; D055815:Young Adult
Arteriovenous malformation; Endovascular embolization; Interventional neuroradiology; Neurosurgery; Radiosurgery