INSERM, CIC 1413, Clinique des données, University Hospital Centre Nantes, Nantes, Pays de la Loire, France.
CNRS, INSERM, L'institut du thorax, University of Nantes, Nantes, Pays de la Loire, France.
J Neurol Neurosurg Psychiatry. 2021 Feb;92(2):122-128. doi: 10.1136/jnnp-2020-324371. Epub 2020 Oct 23.
The ever-growing availability of imaging led to increasing incidentally discovered unruptured intracranial aneurysms (UIAs). We leveraged machine-learning techniques and advanced statistical methods to provide new insights into rupture intracranial aneurysm (RIA) risks.
We analysed the characteristics of 2505 patients with intracranial aneurysms (IA) discovered between 2016 and 2019. Baseline characteristics, familial history of IA, tobacco and alcohol consumption, pharmacological treatments before the IA diagnosis, cardiovascular risk factors and comorbidities, headaches, allergy and atopy, IA location, absolute IA size and adjusted size ratio (aSR) were analysed with a multivariable logistic regression (MLR) model. A random forest (RF) method globally assessed the risk factors and evaluated the predictive capacity of a multivariate model.
Among 994 patients with RIA (39.7%) and 1511 patients with UIA (60.3 %), the MLR showed that IA location appeared to be the most significant factor associated with RIA (OR, 95% CI: internal carotid artery, reference; middle cerebral artery, 2.72, 2.02-3.58; anterior cerebral artery, 4.99, 3.61-6.92; posterior circulation arteries, 6.05, 4.41-8.33). Size and aSR were not significant factors associated with RIA in the MLR model and antiplatelet-treatment intake patients were less likely to have RIA (OR: 0.74; 95% CI: 0.55-0.98). IA location, age, following by aSR were the best predictors of RIA using the RF model.
The location of IA is the most consistent parameter associated with RIA. The use of 'artificial intelligence' RF helps to re-evaluate the contribution and selection of each risk factor in the multivariate model.
随着影像学技术的不断发展，越来越多的未破裂颅内动脉瘤（UIAs）被偶然发现。我们利用机器学习技术和先进的统计方法，为破裂颅内动脉瘤（RIA）的风险提供了新的见解。
我们分析了 2016 年至 2019 年间发现的 2505 例颅内动脉瘤（IA）患者的特征。使用多变量逻辑回归（MLR）模型分析了基线特征、IA 家族史、吸烟和饮酒、IA 诊断前的药物治疗、心血管危险因素和合并症、头痛、过敏和特应性、IA 位置、IA 绝对大小和调整大小比（aSR）。随机森林（RF）方法全面评估了危险因素，并评估了多变量模型的预测能力。
在 994 例 RIA 患者（39.7%）和 1511 例 UIA 患者（60.3%）中，MLR 显示 IA 位置似乎是与 RIA 最相关的因素（OR，95%CI：颈内动脉，参考；大脑中动脉，2.72，2.02-3.58；大脑前动脉，4.99，3.61-6.92；后循环动脉，6.05，4.41-8.33）。大小和 aSR 在 MLR 模型中不是与 RIA 相关的显著因素，接受抗血小板治疗的患者发生 RIA 的可能性较小（OR：0.74；95%CI：0.55-0.98）。使用 RF 模型，IA 位置、年龄，其次是 aSR，是预测 RIA 的最佳指标。
IA 的位置是与 RIA 最相关的参数。使用“人工智能”RF 有助于重新评估多变量模型中每个危险因素的贡献和选择。
