Outcomes and Complications After Endovascular Treatment of Brain Arteriovenous Malformations: A Prognostication Attempt Using Artificial Intelligence
Outcomes and Complications After Endovascular Treatment of Brain Arteriovenous Malformations: A Prognostication Attempt Using Artificial Intelligence
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DOI：
10.1016/j.wneu.2016.09.086
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发表时间：
2016-12-01
影响因子：
2
通讯作者：
Thornton, John
中科院分区：
文献类型：
--
作者：
Asadi, Hamed;Kok, Hong Kuan;Thornton, John
目的：确定影响血管内栓塞治疗脑动静脉畸形（BAVM）结局的因素。我们还评估了使用机器学习技术来解释和预测结果的可行性，并将其与传统的统计分析进行了比较。方法：在一个国家神经科学中心进行了22年的BAVM血管内治疗患者的回顾性研究。记录临床表现、影像学、手术细节、并发症和结局。人工智能技术的数据进行了分析，以确定预测结果和评估准确性预测临床结局在最终follow-up.Results：一百九十九名患者接受治疗BAVM的平均随访时间为63个月。最常见的临床表现为颅内出血（56%）。在随访期间，有51起进一步出血事件，包括自发性出血（n = 27）和手术相关出血（n = 24）。所有自发事件均发生在远离手术的既往栓塞BAVM中。并发症包括缺血性卒中10%，症状性出血9.8%，死亡率4.7%。标准回归分析模型预测最终结局（死亡率）的准确率为43%，治疗并发症的类型被确定为最重要的预测因素。机器学习模型在预测结果方面显示出上级准确性为97.5%，并确定了病灶瘘管的存在或不存在为最重要的factor.Conclusions：BAVM可以通过血管内技术成功治疗，或结合手术和放射外科治疗，风险状况可接受。机器学习技术可以更准确地预测最终结果，并可能有助于基于关键预测因素进行个性化治疗。
PURPOSE: 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.