Prehospital Identification of Stroke Subtypes in Chinese Rural Areas  

作　　者：Hai-Qiang Jin Jin-Chao Wang Yong-An Sun Pu Lyu Wei Cui Yuan-Yuan Liu Zhi-Gang Zhen Yi-Ning Huang 

机构地区：[1]Department of Neurology, Peking University First Hospital, Beijing 100034, China [2]Department of Neurology, Yutian County Hospital, Tangshan, Hebei 064100, China

出　　处：《Chinese Medical Journal》2016年第9期1041-1046,共6页中华医学杂志（英文版）

摘　　要：Background： Differentiating intracerebral hemorrhage （ICH） from cerebral infarction as early as possible is vital tbr the timely initiation of different treatments. This study developed an applicable model for the ambulance system to differentiate stroke subtypes. Methods： From 26,163 patients initially screened over 4 years, this study comprised 1989 consecutive patients with potential first-ever acute stroke with sudden onset of the focal neurological deficit, conscious or not, and given ambulance transport for admission to two county hospitals in Yutian County of Hebei Province. All the patients underwent cranial computed tomography （CT） or magnetic resonance imaging to confirm the final diagnosis based on stroke criteria. Correlation with stroke subtype clinical features was calculated and Bayes＇ discriminant model was applied to discriminate stroke subtypes. Results： Among the 1989 patients, 797,689, 109, and 394 received diagnoses of cerebral infarction, ICH, subarachnoid hemorrhage, and other forms of nonstroke, respectively. A history of atrial fibrillation, vomiting, and diabetes mellitus were associated with cerebral infarction, while vomiting, systolic blood pressure _〉180 mmHg, and age 〈65 years were more typical of ICH. For noncomatose stroke patients, Bayes＇ discriminant model for stroke subtype yielded a combination of multiple items that provided 72.3% agreement in the test model and 79.3% in the validation model; for comatose patients, corresponding agreement rates were 75.4% and 73.5%. Conclusions： The model herein presented, with multiple parameters, can predict stroke subtypes with acceptable sensitivity and specificity before CT scanning, either in alert or comatose patients. This may facilitate prehospital management for patients with stroke.Background： Differentiating intracerebral hemorrhage （ICH） from cerebral infarction as early as possible is vital tbr the timely initiation of different treatments. This study developed an applicable model for the ambulance system to differentiate stroke subtypes. Methods： From 26,163 patients initially screened over 4 years, this study comprised 1989 consecutive patients with potential first-ever acute stroke with sudden onset of the focal neurological deficit, conscious or not, and given ambulance transport for admission to two county hospitals in Yutian County of Hebei Province. All the patients underwent cranial computed tomography （CT） or magnetic resonance imaging to confirm the final diagnosis based on stroke criteria. Correlation with stroke subtype clinical features was calculated and Bayes＇ discriminant model was applied to discriminate stroke subtypes. Results： Among the 1989 patients, 797,689, 109, and 394 received diagnoses of cerebral infarction, ICH, subarachnoid hemorrhage, and other forms of nonstroke, respectively. A history of atrial fibrillation, vomiting, and diabetes mellitus were associated with cerebral infarction, while vomiting, systolic blood pressure _〉180 mmHg, and age 〈65 years were more typical of ICH. For noncomatose stroke patients, Bayes＇ discriminant model for stroke subtype yielded a combination of multiple items that provided 72.3% agreement in the test model and 79.3% in the validation model; for comatose patients, corresponding agreement rates were 75.4% and 73.5%. Conclusions： The model herein presented, with multiple parameters, can predict stroke subtypes with acceptable sensitivity and specificity before CT scanning, either in alert or comatose patients. This may facilitate prehospital management for patients with stroke.

关 键 词：Bayes＇ Discriminant Model Prehospital Identification Stroke Subtypes 

分 类 号：S858.31[农业科学—临床兽医学] TP391.41[农业科学—兽医学]