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医院在用体外心脏除颤器检测及影响因素统计分析研究 被引量:5

Statistical Analysis and Research on Detection and Influence Factors of External Cardiac Defibrillator in Hospital
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摘要 目的对体外心脏除颤器进行检测,了解医院在用体外除颤器的风险现状,提出风险控制建议。方法使用检测仪器,遵循国家计量技术规范JJF1149-2014心脏除颤器校准规范,运用统计学方法分析医院级别、使用年数、国产进口、维护保养周期、有无培训记录等因素对除颤器不合格的影响。结果14台出现一个或一个以上检测不合格项目,使用年数影响外观或一般功能正常性检查不合格,厂家来源、医院维护保养、人员培训影响充电时间或释放能量不合格,储存能量保持期间随着时间的延长释放能量出现衰减。结论通过除颤器检测及数据统计分析,明确了定期检测、维护保养的重要性,加强对释放能量失准设备的管理和确定合适的医疗设备经济使用年限能提高除颤器质量安全水平,保障临床使用的安全性、有效性、经济性。 Objective To detect performance of cardiac defibrillator in hospital and to reveal the risk of present situation,so as to put forward risk control suggestions.Methods According to National Metrological technical specification(JJF1149-2014 Calibration Specification for Cardiac Defibrillators),testing instrument was applied to obtain data and then statistical method was using to analyze the influence of hospital level,years of use,domestic or import,maintenance cycle,training record on the failure of defibrillator.Results One or more unqualified items were found in 14 sets,and the use years affected the appearance or normal function,and the source of manufacturer,hospital maintenance and personnel training affected the charging time or energy release.During the storage period,the energy decreased with time.Conclusion Regular inspection and maintenance are important,and strengthening the management of energy-released equipment and determining the appropriate economic service life can improve the quality and safety level of defibrillator,and also ensure the effectiveness and economy of clinical use.
作者 温润龙 李亮华 陈伟胜 罗万婷 何恩彦 谢炯 WEN Runlong;LI Lianghua;CHEN Weisheng;LUO Wanting;HE Enyan;XIE Jiong(Center for Adverse Drug Reaction Monitoring of Zhongshan,Zhongshan Guangdong 528400,China;Center for Adverse Drug Reaction Monitoring of Shunde,Foshan Guangdong 528300,China)
出处 《中国医疗设备》 2020年第8期27-30,35,共5页 China Medical Devices
基金 广东省食品药品监督管理局科技创新项目(2018TDB25) 中山市社会公益科技研究项目(2019B2083)。
关键词 体外心脏除颤器 检测 影响因素 数据统计分析 风险 cardiac defibrillator detection influencing factors statistical analysis risk
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