Use of diagnosis codes for detection of clinically significant opioid poisoning in the emergency department: A retrospective analysis of a surveillance case definition
Use of diagnosis codes for detection of clinically significant opioid poisoning in the emergency department: A retrospective analysis of a surveillance case definition
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DOI：
10.1186/s12873-016-0075-4
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发表时间：
2016-02-08
影响因子：
2.5
通讯作者：
Waller, Anna E.
中科院分区：
文献类型：
--
作者：
Reardon, Joseph M.;Harmon, Katherine J.;Waller, Anna E.
背景：虽然从1999年到2008年，致命的阿片类药物中毒增加了两倍，但描述非致命性中毒的数据很少。公共卫生当局需要的工具，以跟踪阿片类药物中毒在近真实的time.Methods：我们确定了实用的ICD-9-CM诊断代码识别临床显着的阿片类药物中毒在全州范围内的急诊科（艾德）监测系统。我们从2009年7月至2012年6月期间的四家医院抽样就诊，诊断代码为965.00、965.01、965.02和965.09（阿片类药物和相关麻醉剂中毒）和/或外部损伤原因代码E850.0-E850.2（阿片类药物和相关麻醉剂意外中毒），并制定了一个新的病例定义，以确定在哪些情况下阿片类药物中毒促使艾德访视。我们计算了具有临床意义的阿片类药物中毒编码访视的百分比，并将其与实际存在阿片类药物中毒的非阿片类药物中毒编码访视的百分比进行了比较。我们建立了一个多元回归模型，以确定是否其他收集的分诊数据可以提高阳性预测值的诊断代码单独检测临床显着的阿片类药物poison.Results：70.1%的访问（标准误差2.4%）编码为阿片类药物中毒主要是由阿片类药物中毒。其余访视代表在其他原发性疾病背景下的阿片类药物暴露。在审查的非阿片类药物中毒代码中，高达36%被重新分类为阿片类药物中毒。在多变量分析中，只有纳洛酮的使用提高了ICD-9-CM代码的阳性预测值，用于确定临床上显着的阿片类药物中毒，但与高假阴性率。结论：这种监测机制确定了许多临床上显着的阿片类药物过量具有较高的阳性预测值。通过进一步验证，它可能有助于采取有针对性的控制措施，如处方者教育和药房监测。
Background: Although fatal opioid poisonings tripled from 1999 to 2008, data describing nonfatal poisonings are rare. Public health authorities are in need of tools to track opioid poisonings in near real time.Methods: We determined the utility of ICD-9-CM diagnosis codes for identifying clinically significant opioid poisonings in a state-wide emergency department (ED) surveillance system. We sampled visits from four hospitals from July 2009 to June 2012 with diagnosis codes of 965.00, 965.01, 965.02 and 965.09 (poisoning by opiates and related narcotics) and/or an external cause of injury code of E850.0-E850.2 (accidental poisoning by opiates and related narcotics), and developed a novel case definition to determine in which cases opioid poisoning prompted the ED visit. We calculated the percentage of visits coded for opioid poisoning that were clinically significant and compared it to the percentage of visits coded for poisoning by non-opioid agents in which there was actually poisoning by an opioid agent. We created a multivariate regression model to determine if other collected triage data can improve the positive predictive value of diagnosis codes alone for detecting clinically significant opioid poisoning.Results: 70.1 % of visits (Standard Error 2.4 %) coded for opioid poisoning were primarily prompted by opioid poisoning. The remainder of visits represented opioid exposure in the setting of other primary diseases. Among non-opioid poisoning codes reviewed, up to 36 % were reclassified as an opioid poisoning. In multivariate analysis, only naloxone use improved the positive predictive value of ICD-9-CM codes for identifying clinically significant opioid poisoning, but was associated with a high false negative rate.Conclusions: This surveillance mechanism identifies many clinically significant opioid overdoses with a high positive predictive value. With further validation, it may help target control measures such as prescriber education and pharmacy monitoring.