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  • 张洪剑,刘团结,王文安,徐建华,解炯,蒋超,徐中杰,张永巍,杨鹏飞,邓本强,朱勤忠,刘建民.智能预警系统在脑卒中患者院前院内衔接中的应用[J].第二军医大学学报,2018,39(9):970-976    [点击复制]
  • ZHANG Hong-jian,LIU Tuan-jie,WANG Wen-an,XU Jian-hua,XIE Jiong,JIANG Chao,XU Zhong-jie,ZHANG Yong-wei,YANG Peng-fei,DENG Ben-qiang,ZHU Qin-zhong,LIU Jian-min.Application of intelligent early warning system in pre-and in-hospital connection of stroke patients[J].Acad J Sec Mil Med Univ,2018,39(9):970-976   [点击复制]
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智能预警系统在脑卒中患者院前院内衔接中的应用
张洪剑1,刘团结2,王文安3,徐建华4,解炯5,蒋超6,徐中杰6,张永巍1,杨鹏飞1*,邓本强1,朱勤忠5,刘建民1
0
(1. 海军军医大学(第二军医大学)长海医院脑血管病中心, 上海 200433;
2. 上海交通大学附属第一人民医院宝山分院神经内科, 上海 200940;
3. 上海交通大学医学院附属新华医院崇明分院神经内科, 上海 200215;
4. 嘉定区中心医院神经内科, 上海 201800;
5. 上海市医疗急救中心, 上海 200003;
6. 闵行区医疗急救中心, 上海 201199
*通信作者)
摘要:
目的 探讨智能预警系统在脑卒中患者院前院内衔接中的应用价值。方法 回顾性分析2017年11月至2018年6月在海军军医大学(第二军医大学)长海医院脑血病中心进行治疗并采用智能预警系统进行院前预警的所有患者的临床资料。将院前预警定义为120在将患者转运至目的医院之前通过软件向脑卒中团队发出预警。按照院内处理方式将患者分成非脑卒中组、保守治疗组、单纯溶栓组、单纯取栓组、桥接组和脑出血组。评价各组患者救治中智能预警系统各环节的效率,急救医师判定脑卒中的能力和应用凝视-面臂语言时间(G-FAST)量表识别重度脑卒中的能力,接诊医师急救响应速度,以及初级卒中中心急救效率[入院至出院(DIDO)时间]。结果 共纳入患者248例,其中非脑卒中患者24例,脑卒中(包括出血性脑卒中和缺血性脑卒中)患者224例(保守治疗组101例、单纯溶栓组23例、单纯取栓组32例、桥接组22例、脑出血组46例)。248例患者的中位初筛时间、预警时间、响应时间分别为28.0(13.0,92.5)、11.0(7.3,15.3)、19.0(13.0,35.0)s。6名院前急救医师应用G-FAST量表识别重度脑卒中的总体灵敏度、特异度、阳性预测值、阴性预测值以及准确度分别为84%、71%、83%、72%、79%。其中8例重度脑卒中患者通过软件实行院前救治全流程监测,中位转运时间为113(82,142)min,DIDO时间为84(12,125)min。结论 智能预警系统可实现对转运时间点数据采集自动化,使数据更全面、可信度高。该系统有助于分析脑卒中患者院前院内衔接环节中急救医师、驾驶员、脑卒中团队的效率及初级卒中中心院内救治效率等,从而不断缩短院前救治时间,提高院前救治效率。
关键词:  脑卒中  院前急救  信息化  人工智能  预警系统
DOI:10.16781/j.0258-879x.2018.09.0970
投稿时间:2018-07-26修订日期:2018-08-17
基金项目:2017年上海市青年拔尖人才计划,上海市智慧医疗项目(2018ZHYL0218).
Application of intelligent early warning system in pre-and in-hospital connection of stroke patients
ZHANG Hong-jian1,LIU Tuan-jie2,WANG Wen-an3,XU Jian-hua4,XIE Jiong5,JIANG Chao6,XU Zhong-jie6,ZHANG Yong-wei1,YANG Peng-fei1*,DENG Ben-qiang1,ZHU Qin-zhong5,LIU Jian-min1
(1. Stroke Center, Changhai Hospital, Navy Medical University(Second Military Medical University), Shanghai 200433, China;
2. Department of Neurology, Baoshan Division of General Hospital of Shanghai, Shanghai Jiao Tong University, Shanghai 200940, China;
3. Department of Neurology, Chongming Division of Xin Hua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200215, China;
4. Department of Neurology, Jiading Central Hospital, Shanghai 201800, China;
5. Shanghai Medical Emergency Center, Shanghai 200003, China;
6. Minhang Medical Emergency Center, Shanghai 201199, China
*Corresponding author)
Abstract:
Objective To explore the value of intelligent early warning system in pre-and in-hospital connection in the treatment of stroke. Methods The clinical data of the patients with suspected stroke, who received treatment and pre-hospital warning with intelligent early warning system in Stroke Center of Changhai Hospital of Navy Medical University (Second Military Medical University) between Nov. 2017 and Jun. 2018, were retrospectively analyzed. Pre-hospital warning was defined as 120 alerting stroke teams using software before transferring patients to the target hospital. According to the in-hospital treatment methods, the patients were divided into non-stroke group, conservative treatment group, thrombolysis group, thrombectomy group, bridging group and cerebral hemorrhage group. The efficiency in each link of the intelligent early warning system, the ability of emergency doctor diagnosing stroke, the ability of emergency doctor diagnosing severe stroke using gaze-face arm speech time (G-FAST) scale, the speed of first aid response of the attending physician and the door-in-to-door-out (DIDO) time in primary stroke center were evaluated in each group. Results A total of 248 patients were included in this study, including 24 non-stroke patients and 224 stroke patients (101 patients in the conservative treatment group, 23 patients in the thrombolysis group, 32 patients in the thrombectomy group, 22 patients in the bridging group and 46 patients in the cerebral hemorrhage group). The median primary screening time, early warning time and response time of 248 patients were 28.0 (13.0, 92.5), 11.0 (7.3, 15.3) and 19.0 (13.0, 35.0) s, respectively. The sensitivity, specificity, positive predictive value, negative predictive value and accuracy of 6 pre-hospital emergency physicians diagnosing severe stroke using G-FAST scale were 84%, 71%, 83%, 72% and 79%, respectively. Eight patients with severe stroke underwent whole-process monitoring of pre-hospital treatment, and had a median transport time of 113 (82, 142) min and a median DIDO time of 84 (12, 125) min. Conclusion Intelligent early warning system can realize the automation of data acquisition for transfer time points, making the data more comprehensive and reliable. It is helpful to analyze the efficiency of emergency physicians, drivers, stroke teams and primary stroke center in pre-and in-hospital connection in the treatment of stroke, so as to shorten the pre-hospital treatment time and improve the pre-hospital treatment efficiency.
Key words:  stroke  pre-hospital rescue  informatization  artificial intelligence  warning system