Table_1_Novel Prehospital Prediction Model of Large Vessel Occlusion Using Artificial Neural Network.DOCX

Background: Identifying large vessel occlusion (LVO) patients in the prehospital triage stage to avoid unnecessary and costly delays is important but still challenging. We aim to develop an artificial neural network (ANN) algorithm to predict LVO using prehospital accessible data including demographics, National Institutes of Health Stroke Scale (NIHSS) items and vascular risk factors.Methods: Consecutive acute ischemic stroke patients who underwent CT angiography (CTA) or time of flight MR angiography (TOF-MRA) and received reperfusion therapy within 8 h from symptom onset were included. The diagnosis of LVO was defined as occlusion of the intracranial internal carotid artery (ICA), M1 and M2 segments of the middle cerebral artery (MCA) and basilar artery on CTA or TOF-MRA before treatment. Patients with and without LVO were randomly selected at a 1:1 ratio. The ANN model was developed using backpropagation algorithm, and 10-fold cross-validation was used to validate the model. The comparison of diagnostic parameters between the ANN model and previously established prehospital prediction scales were performed.Results: Finally, 300 LVO and 300 non-LVO patients were randomly selected for the training and validation of the ANN model. The mean Youden index, sensitivity, specificity and accuracy of the ANN model based on the 10-fold cross-validation analysis were 0.640, 0.807, 0.833 and 0.820, respectively. The area under the curve (AUC), Youden index and accuracy of the ANN model were all higher than other prehospital prediction scales.Conclusions: The ANN can be an effective tool for the recognition of LVO in the prehospital triage stage.

背景：在院前急救阶段的早期识别大血管闭塞（LVO）患者，以避免不必要的和昂贵的延误至关重要，但仍具挑战性。本研究旨在开发一种人工神经网络（ANN）算法，通过使用院前可获取的数据，包括人口统计学数据、美国国立卫生研究院卒中量表（NIHSS）项目以及血管风险因素，来预测LVO。方法：纳入了连续发生的急性缺血性卒中患者，这些患者接受了CT血管造影（CTA）或飞行时间磁共振血管造影（TOF-MRA）检查，并在症状出现后8小时内接受了再灌注治疗。LVO的诊断定义为在CTA或TOF-MRA检查治疗前，颅内颈内动脉（ICA）、大脑中动脉（MCA）M1和M2段以及基底动脉的闭塞。按照1:1的比例随机选取有LVO和无LVO的患者。ANN模型采用反向传播算法开发，并使用10折交叉验证来验证模型。对ANN模型与先前建立的院前预测量表之间的诊断参数进行了比较。结果：最终，随机选取了300名LVO患者和300名非LVO患者用于ANN模型的训练和验证。基于10折交叉验证分析，ANN模型的平均Youden指数、敏感性、特异性和准确性分别为0.640、0.807、0.833和0.820。ANN模型的曲线下面积（AUC）、Youden指数和准确性均高于其他院前预测量表。结论：ANN可以成为院前急救阶段识别LVO的有效工具。