全面触发工具在药品不良事件检测中的应用初探

目的：探讨全面触发工具（ GTT）在我国医疗机构检测药品不良事件（ ADE）的可行性。方法调取1013年1月1日至11月31日首都医科大学宣武医院出院患者的信息，按照出院时间排序后，使用Microsoft Excel 1007软件中的随机抽样工具，每半个月随机抽取30例患者的病历。根据入选标准（患者年龄≥18岁，1013年住院次数为1次，住院时间﹥1 d）、排除标准（入住产科、计划生育科、康复科、肿瘤科、日间病房、儿科的患者）剔除不合格病历，使用GTT推荐以及根据国外相关研究和首都医科大学宣武医院自身经验确定的35项触发器（包含实验室指标、解救剂、临床症状和处理措施等）按样本随机顺序每半个月审查10份病历（若符合入选标准者不足10份则全部纳入），检测到触发器者标记为触发器阳性，对触发器阳性所涉及情况进一步审查以确定或排除 ADE，对确定为ADE者进行分级。使用Microsoft Excel 1007软件对阳性触发器和ADE进行整理分析，计算阳性触发器的阳性预测值。结果审查病历共465份，涉及465例患者，其中男性156例，女性109例，年龄19~91岁，平均57岁，住院时间1~37 d，平均10 d。465例患者中触发器阳性者108例（44.7%）。35项触发器中有11项触发器呈阳性（61.9%），共检出341例次。确定ADE 18例次，涉及16例患者，ADE检出率为3.4%（16/465）。18例次ADE中13例次有相对应的触发器，涉及8项触发器。11项阳性触发器的总体阳性预测值为3.8%。18例次ADE包括肺炎、肝损伤、寒战和皮疹各1例次，抗菌药物相关性腹泻、头痛、头晕、恶心呕吐、低血糖、过度镇静、谵妄、出血、白细胞减少症和兴奋各1例次；14例次为E级，4例次为F级；涉及药物11种，包括抗菌药物5种，血液系统药物和精神系统药物各3种，心血管系统药物、激素类药物和中成药各1种，调脂药、影响骨代?

Pilot study of detecting adverse drug events with Global Trigger Tool

Objective To investigate the feasibility of detecting adverse drug event（ ADE）using Global Trigger Tool（ GTT）in Chinese medical institutions. Methods Discharged patients＆#39; records of the Xuanwu Hospital of Capital Medical University from January 1st to December 31st 1013 were collected. After sorting by discharged date,30 cases were selected in a half month period by a random sampling tool of Microsoft Excell1007 software. Unqualified cases were eliminated according to the inclusion criteria（ patients aged 18 and over,one time admission in 1013,and hospitalization for more than 1 day）and exclusion criteria（ patients in the Department of Obstetrics,Family Planning,Rehabilitation,Oncology,Pediatrics, and day-care ward）. The 10 cases were reviewed every half a month in sequence of random sampling using 35 triggers,including laboratory indexes,antidotes,clinical symptoms,and treatment measures,that were identified by GTT recommendation,relevant foreign researches,and self-experience of Xuanwu Hospital of＆amp;nbsp;Capital Medical University. All cases were enrolled if the number of cases which met the inclusion criteria was less than 10. The cases in whom triggers could be detected were marked as the cases with positive triggers. The cases with positive triggers-related situations were further reviewed in order to identify or exclude ADE and then the identified ADEs were classified. The positive triggers and ADEs were analyzed by Microsoft Excell1007 software and the positive predictive values of positive triggers were calculated. Results Totally 465 cases were reviewed. Of them,156 were male and 109 female with the mean age of 57（19~91）years. The time of hospital stay was 1 to 37 days with the mean hospital stay of 10 days. Of the 465 patients,in 108 patients（44. 7%）positive triggers could be detected. Of all the 35 triggers,11 triggers（61. 9%）were positive referring to 341 times. There were 18 ADEs identified involving 16 patients and the detectable rate was 3. 4%（16/465）. Of t