Long Haoxian, Che Wenqiang, Yang Chenyou, Liao Yonglong, Wu Jiamin, Chen Chuan, Wang Xiangyu, Wen Jun
Department of Neurosurgery, The First Affiliated Hospital of Jinan University, Guangzhou, China.
Department of Neurosurgery, The First Affiliated Hospital of Jinan University, Guangzhou, China; Department of Neurosurgery, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, P.R. China.
World Neurosurg. 2025 Feb;194:123552. doi: 10.1016/j.wneu.2024.12.011. Epub 2025 Jan 10.
Predicting rupture risk in small intracranial aneurysms (IAs) < 5 mm is crucial for guiding clinical decisions. This study aims to identify key clinical and morphological risk factors associated with rupture in small IAs, providing better insight for decision-making.
A retrospective analysis was performed on patients with small IAs from one center, with external validation data from another center. Logistic regression identified significant risk factors for aneurysm rupture, which were incorporated into a predictive model. The model's performance was evaluated using the area under the receiver operating characteristic curve, calibration plots, and the Hosmer-Lemeshow goodness-of-fit test. Clinical utility was assessed via decision curve analysis.
The training cohort consisted of 226 patients (ruptured, n = 181; unruptured, n = 92), while 136 patients (ruptured, n = 100; unruptured, n = 59) were used for external validation. Significant risk factors included hypertension, smoking, anterior communicating artery aneurysms, daughter sacs, aspect ratio, and size ratio. The model demonstrated strong predictive ability with area under the curves of 0.969 and 0.967 in the training and validation cohorts, respectively. Calibration plots indicated a good agreement between predicted and observed rupture risks, while decision curve analysis highlighted the model's clinical relevance.
This study identifies and validates critical risk factors associated with small IA rupture and presents a clinically useful, high-accuracy predictive model to aid in individualized patient management.
预测直径小于5毫米的小型颅内动脉瘤（IA）破裂风险对于指导临床决策至关重要。本研究旨在识别与小型IA破裂相关的关键临床和形态学风险因素，为决策提供更好的依据。
对来自一个中心的小型IA患者进行回顾性分析，并使用来自另一个中心的外部验证数据。逻辑回归确定了动脉瘤破裂的显著风险因素，并将其纳入预测模型。使用受试者操作特征曲线下面积、校准图和Hosmer-Lemeshow拟合优度检验评估模型的性能。通过决策曲线分析评估临床实用性。
训练队列包括226例患者（破裂，n = 181；未破裂，n = 92），而136例患者（破裂，n = 100；未破裂，n = 59）用于外部验证。显著风险因素包括高血压、吸烟、前交通动脉瘤、子囊、纵横比和大小比。该模型在训练队列和验证队列中的曲线下面积分别为0.969和0.967，显示出强大的预测能力。校准图表明预测和观察到的破裂风险之间具有良好的一致性，而决策曲线分析突出了该模型的临床相关性。
本研究识别并验证了与小型IA破裂相关的关键风险因素，并提出了一个临床有用的、高精度的预测模型，以帮助进行个体化患者管理。
