Modified Prehospital Acute Stroke Severity (mPASS) Scale to Predict Emergent Large Arterial Occlusion

• Main Authors: Xiaoli Si, Jie Ruan, Lingfei Li, Shan Lu, Huan Huang, Wenqing Xia, Keqin Liu, Tianwen Chen, Lin Jiang, Congguo Yin
• Format: Article
• Language: English
• Published: Hindawi Limited 2021-01-01
• Series: BioMed Research International
• Online Access: http://dx.doi.org/10.1155/2021/5568696

Introduction. To date, identifying emergent large vessel occlusion (ELVO) patients in the prehospital stage is important but still challenging. In this present study, we aimed to design a modified prehospital acute stroke severity (mPASS) scale to identify ELVO patients and compared the scale to the PASS scale which has been published. Methods. We retrospectively evaluated a consecutive cohort of acute ischemic stroke (AIS) in our stroke unit who visited the emergercy department. These patients underwent CT angiography (CTA), MR angiography (MRA), or digital subtraction angiography (DSA) at admission. The mPASS scale was calculated based on the National Institutes of Health Stroke Scale (NIHSS) items retrospectively, including the level of consciousness commands, gaze, arm weakness, and aphasia/dysarthria. Receiver operating characteristic (ROC) analysis was used to obtain the area under the curve (AUC) of the mPASS scale, NIHSS, and PASS scale. U-statistics was used to compare the AUC of the mPASS scale to the NIHSS and PASS scale. Results. A total of 382 AIS patients were enrolled. The AUC and specificity of the mPASS scale (0.92, 84.4) were all higher than those of the PASS scale. Cortical symptoms such as gaze palsy and consciousness disorder were more specific indicators for ELVO than motor deficits. Conclusions. The mPASS scale had a better discrimination for identifying ELVO than the PASS scale in our retrospective cohort. It might predict ELVO in an effective and simple way for paramedics in the prehospital triage stage or emergency stage. Moreover, cortical symptoms might have relatively high specificities to predict ELVO on their own.