You Jia, Tsang Anderson C O, Yu Philip L H, Tsui Eva L H, Woo Pauline P S, Lui Carrie S M, Leung Gilberto K K
Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong, Hong Kong.
Division of Neurosurgery, Department of Surgery, The University of Hong Kong, Hong Kong, Hong Kong.
Front Neuroinform. 2020 Mar 24;14:13. doi: 10.3389/fninf.2020.00013. eCollection 2020.
The detection of large vessel occlusion (LVO) plays a critical role in the diagnosis and treatment of acute ischemic stroke (AIS). Identifying LVO in the pre-hospital setting or early stage of hospitalization would increase the patients' chance of receiving appropriate reperfusion therapy and thereby improve neurological recovery.
To enable rapid identification of LVO, we established an automated evaluation system based on all recorded AIS patients in Hong Kong Hospital Authority's hospitals in 2016. The 300 study samples were randomly selected based on a disproportionate sampling plan within the integrated electronic health record system, and then separated into a group of 200 patients for model training, and another group of 100 patients for model performance evaluation. The evaluation system contained three hierarchical models based on patients' demographic data, clinical data and non-contrast CT (NCCT) scans. The first two levels of modeling utilized structured demographic and clinical data, while the third level involved additional NCCT imaging features obtained from deep learning model. All three levels' modeling adopted multiple machine learning techniques, including logistic regression, random forest, support vector machine (SVM), and eXtreme Gradient Boosting (XGboost). The optimal cut-off for the likelihood of LVO was determined by the maximal Youden index based on 10-fold cross-validation. Comparisons of performance on the testing group were made between these techniques.
Among the 300 patients, there were 160 women and 140 men aged from 27 to 104 years (mean 76.0 with standard deviation 13.4). LVO was present in 130 (43.3%) patients. Together with clinical and imaging features, the XGBoost model at the third level of evaluation achieved the best model performance on testing group. The Youden index, accuracy, sensitivity, specificity, F1 score, and area under the curve (AUC) were 0.638, 0.800, 0.953, 0.684, 0.804, and 0.847, respectively.
To the best of our knowledge, this is the first study combining both structured clinical data with non-structured NCCT imaging data for the diagnosis of LVO in the acute setting, with superior performance compared to previously reported approaches. Our system is capable of automatically providing preliminary evaluations at different pre-hospital stages for potential AIS patients.
大血管闭塞（LVO）的检测在急性缺血性卒中（AIS）的诊断和治疗中起着关键作用。在院前环境或住院早期识别LVO将增加患者接受适当再灌注治疗的机会，从而改善神经功能恢复。
为了能够快速识别LVO，我们基于2016年香港医院管理局医院中所有记录的AIS患者建立了一个自动评估系统。在综合电子健康记录系统中，根据不成比例抽样计划随机选择300个研究样本，然后将其分为一组200例患者用于模型训练，另一组100例患者用于模型性能评估。评估系统包含基于患者人口统计学数据、临床数据和非增强CT（NCCT）扫描的三个层次模型。前两个建模层次利用结构化的人口统计学和临床数据，而第三个层次涉及从深度学习模型获得的额外NCCT成像特征。所有三个层次的建模都采用了多种机器学习技术，包括逻辑回归、随机森林、支持向量机（SVM）和极端梯度提升（XGboost）。基于10折交叉验证，通过最大约登指数确定LVO可能性的最佳截断值。对这些技术在测试组上的性能进行了比较。
在300例患者中，有160名女性和140名男性，年龄在27至104岁之间（平均76.0岁，标准差13.4）。130例（43.3%）患者存在LVO。结合临床和影像学特征，评估第三层次的XGBoost模型在测试组上实现了最佳模型性能。约登指数、准确率、敏感性、特异性、F1分数和曲线下面积（AUC）分别为0.638、0.800、0.953、0.684、0.804和0.847。
据我们所知，这是第一项将结构化临床数据与非结构化NCCT成像数据相结合用于急性情况下LVO诊断的研究，与先前报道的方法相比具有卓越性能。我们的系统能够在不同的院前阶段为潜在的AIS患者自动提供初步评估。
