Wang Xiguang, Huang Xu
Department of Research & Development Management, Shanghai Aohua Photoelectricity Endoscope Co., Ltd., Shanghai, China.
Front Physiol. 2024 Sep 5;15:1454016. doi: 10.3389/fphys.2024.1454016. eCollection 2024.
Cerebral aneurysms are abnormal dilations of blood vessels in the brain that have the potential to rupture, leading to subarachnoid hemorrhage and other serious complications. Early detection and prediction of aneurysm rupture are crucial for effective management and prevention of rupture-related morbidities and mortalities. This review aims to summarize the current knowledge on risk factors and predictive indicators of rupture in cerebral aneurysms. Morphological characteristics such as aneurysm size, shape, and location, as well as hemodynamic factors including blood flow patterns and wall shear stress, have been identified as important factors influencing aneurysm stability and rupture risk. In addition to these traditional factors, emerging evidence suggests that biological and genetic factors, such as inflammation, extracellular matrix remodeling, and genetic polymorphisms, may also play significant roles in aneurysm rupture. Furthermore, advancements in computational fluid dynamics and machine learning algorithms have enabled the development of novel predictive models for rupture risk assessment. However, challenges remain in accurately predicting aneurysm rupture, and further research is needed to validate these predictors and integrate them into clinical practice. By elucidating and identifying the various risk factors and predictive indicators associated with aneurysm rupture, we can enhance personalized risk assessment and optimize treatment strategies for patients with cerebral aneurysms.
脑动脉瘤是大脑血管的异常扩张，有可能破裂，导致蛛网膜下腔出血和其他严重并发症。早期检测和预测动脉瘤破裂对于有效管理和预防与破裂相关的发病率和死亡率至关重要。本综述旨在总结目前关于脑动脉瘤破裂的危险因素和预测指标的知识。动脉瘤大小、形状和位置等形态学特征，以及包括血流模式和壁面剪应力在内的血流动力学因素，已被确定为影响动脉瘤稳定性和破裂风险的重要因素。除了这些传统因素外，新出现的证据表明，炎症、细胞外基质重塑和基因多态性等生物学和遗传因素也可能在动脉瘤破裂中起重要作用。此外，计算流体动力学和机器学习算法的进展使得能够开发用于破裂风险评估的新型预测模型。然而，在准确预测动脉瘤破裂方面仍然存在挑战，需要进一步研究来验证这些预测指标并将其纳入临床实践。通过阐明和识别与动脉瘤破裂相关的各种危险因素和预测指标，我们可以加强个性化风险评估，并优化脑动脉瘤患者的治疗策略。
