External validation of cerebral aneurysm rupture probability model with data from two patient cohorts.
External validation of cerebral aneurysm rupture probability model with data from two patient cohorts.
复制标题
使用两个患者队列的数据对脑动脉瘤破裂概率模型进行外部验证。
DOI：
10.1007/s00701-018-3712-8
复制
发表时间：
2018
影响因子：
2.4
通讯作者：
Cebral,JuanR
中科院分区：
文献类型：
--
作者：
Detmer,FelicitasJ;Fajardo-Jiménez,Daniel;Mut,Fernando;Juchler,Norman;Hirsch,Sven;Pereira,VitorMendes;Bijlenga,Philippe;Cebral,JuanR
对于未破裂脑动脉瘤的治疗决策，医生和患者需要权衡治疗风险与动脉瘤破裂引起出血性卒中的风险。本研究的目的是外部评估最近开发的统计动脉瘤破裂概率模型，这可能会支持这样的治疗decision.MethodsSegmented图像数据和患者信息从两个患者队列，包括203例患者249动脉瘤被用于患者特定的计算流体动力学模拟和随后的评估的统计模型的准确性，歧视，和拟合度。通过识别训练队列中的动脉瘤，与外部队列中的给定动脉瘤相比，在血流动力学和形状方面相似，将模型的性能与基于相似性的破裂评估方法进行了进一步比较。（受试者操作特征曲线下面积AUC = 0.82），与训练群体中的最佳校正AUC（AUC = 0.84）相比，其仅略微降低。准确度指标表明，与训练数据相比，准确度略有下降（错误分类误差为0.24 vs. 0.21）。该模型的预测精度提高时，结合相似性的方法（误分类误差为0.14）。ConclusionsThe模型的性能指标表明，在不同的临床机构获得的数据具有良好的概括性。结合基于模型和基于相似性的方法可以进一步改善对新病例的评估和解释，证明其在临床风险评估中的潜在用途。
BackgroundFor a treatment decision of unruptured cerebral aneurysms, physicians and patients need to weigh the risk of treatment against the risk of hemorrhagic stroke caused by aneurysm rupture. The aim of this study was to externally evaluate a recently developed statistical aneurysm rupture probability model, which could potentially support such treatment decisions.MethodsSegmented image data and patient information obtained from two patient cohorts including 203 patients with 249 aneurysms were used for patient-specific computational fluid dynamics simulations and subsequent evaluation of the statistical model in terms of accuracy, discrimination, and goodness of fit. The model’s performance was further compared to a similarity-based approach for rupture assessment by identifying aneurysms in the training cohort that were similar in terms of hemodynamics and shape compared to a given aneurysm from the external cohorts.ResultsWhen applied to the external data, the model achieved a good discrimination and goodness of fit (area under the receiver operating characteristic curve AUC = 0.82), which was only slightly reduced compared to the optimism-corrected AUC in the training population (AUC = 0.84). The accuracy metrics indicated a small decrease in accuracy compared to the training data (misclassification error of 0.24 vs. 0.21). The model’s prediction accuracy was improved when combined with the similarity approach (misclassification error of 0.14).ConclusionsThe model’s performance measures indicated a good generalizability for data acquired at different clinical institutions. Combining the model-based and similarity-based approach could further improve the assessment and interpretation of new cases, demonstrating its potential use for clinical risk assessment.