Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland.
Amsterdam UMC, Neurosurgery, Amsterdam Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.
Acta Neurochir (Wien). 2020 Nov;162(11):2759-2765. doi: 10.1007/s00701-020-04355-0. Epub 2020 May 1.
The decision to treat unruptured intracranial aneurysms (UIAs) or not is complex and requires balancing of risk factors and scores. Machine learning (ML) algorithms have previously been effective at generating highly accurate and comprehensive individualized preoperative predictive analytics in transsphenoidal pituitary and open tumor surgery. In this pilot study, we evaluate whether ML-based prediction of clinical endpoints is feasible for microsurgical management of UIAs.
Based on data from a prospective registry, we developed and internally validated ML models to predict neurological outcome at discharge, as well as presence of new neurological deficits and any complication at discharge. Favorable neurological outcome was defined as modified Rankin scale (mRS) 0 to 2. According to the Clavien-Dindo grading (CDG), every adverse event during the post-operative course (surgery and not surgery related) is recorded as a complication. Input variables included age; gender; aneurysm complexity, diameter, location, number, and prior treatment; prior subarachnoid hemorrhage (SAH); presence of anticoagulation, antiplatelet therapy, and hypertension; microsurgical technique and approach; and various unruptured aneurysm scoring systems (PHASES, ELAPSS, UIATS).
We included 156 patients (26.3% male; mean [SD] age, 51.7 [11.0] years) with UIAs: 37 (24%) of them were treated for multiple aneurysm and 39 (25%) were treated for a complex aneurysm. Poor neurological outcome (mRS ≥ 3) was seen in 12 patients (7.7%) at discharge. New neurological deficits were seen in 10 (6.4%), and any kind of complication occurred in 20 (12.8%) patients. In the internal validation cohort, area under the curve (AUC) and accuracy values of 0.63-0.77 and 0.78-0.91 were observed, respectively.
Application of ML enables prediction of early clinical endpoints after microsurgery for UIAs. Our pilot study lays the groundwork for development of an externally validated multicenter clinical prediction model.
颅内未破裂动脉瘤（UIAs）的治疗决策是一个复杂的问题，需要权衡风险因素和评分。机器学习（ML）算法在经蝶窦垂体和开放性肿瘤手术的高度准确和全面的个体化术前预测分析方面已经取得了很好的效果。在这项初步研究中，我们评估了基于 ML 的临床终点预测是否适用于 UIAs 的显微手术治疗。
根据前瞻性登记处的数据，我们开发并内部验证了 ML 模型，以预测出院时的神经功能结果，以及新的神经功能缺损和出院时的任何并发症的存在。良好的神经功能结果定义为改良 Rankin 量表（mRS）0-2 分。根据 Clavien-Dindo 分级（CDG），术后过程中（手术和非手术相关）的每个不良事件都被记录为并发症。输入变量包括年龄、性别、动脉瘤复杂性、直径、位置、数量和既往治疗、既往蛛网膜下腔出血（SAH）、抗凝、抗血小板治疗和高血压的存在、显微外科技术和方法以及各种未破裂动脉瘤评分系统（PHASES、ELAPSS、UIATS）。
我们纳入了 156 例 UIAs 患者（26.3%为男性；平均[标准差]年龄为 51.7[11.0]岁）：37 例（24%）患者为多发性动脉瘤，39 例（25%）患者为复杂动脉瘤。出院时 12 例（7.7%）患者出现神经功能不良结局（mRS≥3）。10 例（6.4%）患者出现新的神经功能缺损，20 例（12.8%）患者出现任何类型的并发症。在内部验证队列中，观察到曲线下面积（AUC）和准确性值分别为 0.63-0.77 和 0.78-0.91。
ML 的应用可以预测 UIAs 显微手术后的早期临床终点。我们的初步研究为开发外部验证的多中心临床预测模型奠定了基础。
