Price Roman D, Bhurwani Mohammad Mahdi Shiraz, Sommer Kelsey N, Monteiro Andrei, Baig Ammad A, Davies Jason M, Siddiqui Adnan H, Ionita Ciprian N
Department of Biomedical Engineering, University at Buffalo, Buffalo NY 14228.
Canon Stroke and Vascular Research Center, Buffalo, NY 14203.
Proc SPIE Int Soc Opt Eng. 2022 Feb-Mar;12036. doi: 10.1117/12.2612081. Epub 2022 Apr 4.
Subarachnoid Hemorrhage (SAH) is a lethal hemorrhagic stroke that account for 25% of cerebrovascular deaths. As a result of the initial bleed, a chain of physiological events are initiated which may lead to Delayed Cerebral Ischemia (DCI). As of now we have no diagnostic capability to identify patients which may present DCI a few weeks after initial presentation. We propose to investigate whether a data driven approach using angiographic parametric imaging (API) may predict occurrence of the DCI.
Digital Subtraction Angiographic (DSA) sequences from 125 SAH patients were used retrospectively to perform API assessment of the entire brain hemisphere where the hemorrhage was detected. Four Regions of Interests (ROIs) were placed to extract five average API biomarkers in the lateral and AP DSAs. Data driven analysis using Logistic Regression was performed for various API parameters and ROIs to find the optimal configuration to maximize the prognosis accuracy. Each model performance was evaluated using area under the curve of the receiver operator characteristic (AUROC).
Data driven approach with API has a 60% accuracy predicting DCI occurrence. We determined that location of the ROI for extraction of the API parameters is very important for the data driven model performance. Normalizing the values using the inlet velocities for each patient yield higher and more consistent results. Single API biomarkers models had poor prediction accuracies, barely better than chance.
This effectiveness exploratory study demonstrates for the first time, that prognosis of the DCI in SAH patients, is feasible and warrants a more in-depth investigation.
蛛网膜下腔出血（SAH）是一种致命的出血性中风，占脑血管死亡的25%。由于初始出血，一系列生理事件被启动，这可能导致迟发性脑缺血（DCI）。目前，我们没有诊断能力来识别在初次就诊几周后可能出现DCI的患者。我们建议研究一种使用血管造影参数成像（API）的数据驱动方法是否可以预测DCI的发生。
回顾性使用125例SAH患者的数字减影血管造影（DSA）序列，对检测到出血的整个脑半球进行API评估。放置四个感兴趣区域（ROI），以在侧位和前后位DSA中提取五个平均API生物标志物。对各种API参数和ROI进行逻辑回归的数据驱动分析，以找到最佳配置，以最大限度地提高预后准确性。使用受试者操作特征曲线下面积（AUROC）评估每个模型的性能。
使用API的数据驱动方法预测DCI发生的准确率为60%。我们确定，提取API参数的ROI位置对数据驱动模型的性能非常重要。使用每个患者的入口速度对值进行归一化可产生更高且更一致的结果。单一API生物标志物模型的预测准确率较差，仅略高于随机水平。
这项有效性探索性研究首次表明，SAH患者DCI的预后是可行的，值得进行更深入的研究。
