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External validation of cerebral aneurysm rupture probability model with data from two patient cohorts.
Acta Neurochirurgica ( IF 2.4 ) Pub Date : 2018-10-31 , DOI: 10.1007/s00701-018-3712-8
Felicitas J Detmer 1 , Daniel Fajardo-Jiménez 1 , Fernando Mut 1 , Norman Juchler 2, 3 , Sven Hirsch 2 , Vitor Mendes Pereira 4 , Philippe Bijlenga 5 , Juan R Cebral 1
Affiliation  

BACKGROUND For 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. METHODS Segmented 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. RESULTS When 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). CONCLUSIONS The 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.

中文翻译:

使用来自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。

背景技术对于不破裂的脑动脉瘤的治疗决定,医生和患者需要权衡治疗的风险与动脉瘤破裂引起的出血性中风的风险。这项研究的目的是从外部评估最近开发的统计性动脉瘤破裂概率模型,该模型可能支持此类治疗决策。方法从包括203例249个动脉瘤的203个患者队列中获得的分割图像数据和患者信息,用于患者特定的计算流体动力学模拟以及随后在准确性,辨别力和拟合优度方面对统计模型的评估。该模型' 通过鉴定训练队列中与外部队列中给定的动脉瘤相比在血液动力学和形状方面相似的动脉瘤,将其表现进一步与基于相似性的破裂评估方法进行比较。结果当应用于外部数据时,该模型获得了良好的辨别力和拟合度(接收器工作特征曲线下的面积AUC = 0.82),与经过训练的人群中经乐观校正的AUC相比仅略有减少(AUC = 0.84)。与训练数据相比,准确性指标表明准确性略有下降(误分类误差为0.24对0.21)。与相似度方法结合使用时,模型的预测准确性得以提高(误分类误差为0.14)。结论模型 的性能指标表明在不同临床机构获得的数据具有良好的通用性。将基于模型的方法和基于相似性的方法相结合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。
更新日期:2019-11-01
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