Assessing the accuracy of opioid overdose and poisoning codes in diagnostic information from electronic health records, claims data, and death records
Assessing the accuracy of opioid overdose and poisoning codes in diagnostic information from electronic health records, claims data, and death records
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DOI：
10.1002/pds.4157
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发表时间：
2017-05-01
影响因子：
2.6
通讯作者：
Coplan, Paul M.
中科院分区：
文献类型：
--
作者：
Green, Carla A.;Perrin, Nancy A.;Coplan, Paul M.
目的
本研究的目的是相对于病历审查，评估国际疾病分类（ICD）-9/10诊断编码在识别阿片类药物过量和中毒方面的阳性预测值（PPV）。
方法
数据来自美国西北凯撒医疗集团和北加利福尼亚。来自俄勒冈州、华盛顿州和加利福尼亚州的电子健康记录、提交的索赔以及州死亡记录中的诊断数据被关联起来。通过与病历审核比较，对个体阿片类相关中毒编码（例如，965.xx和X42）以及阿片类药物不良反应编码（例如，E935.xx）与可能表明过量的诊断（例如，呼吸抑制）相结合的情况进行了评估。
结果
在127份病历中评估发现，阿片类药物不良反应编码在检测过量方面的阳性预测值较低（13.4%），因此未再进一步研究。相反，对2008年至2012年期间电子健康记录中有阿片类中毒编码的2100人进行了评估。其中，2100人中有10人没有可用信息，2100人中有241人可能因与麻醉相关而被排除。在剩余的1849名有阿片类中毒编码的个体中，1495起事件被准确识别为阿片类药物过量；69起为错误编码或错误识别，285起为阿片类药物不良反应，而非过量。因此，阳性预测值为81%。在1849起事件中的1780起（96.3%）中，阿片类药物不良反应或过量被准确识别。
结论
阿片类中毒编码在识别阿片类药物过量方面的预测值为81%，这表明ICD阿片类中毒编码可用于监测过量率以及评估减少过量的干预措施。评估敏感性、特异性和阴性预测值的进一步研究正在进行中。版权所有（c）2017约翰威立父子有限公司
PurposeThe purpose of this study is to assess positive predictive value (PPV), relative to medical chart review, of International Classification of Diseases (ICD)-9/10 diagnostic codes for identifying opioid overdoses and poisonings.MethodsData were obtained from Kaiser Permanente Northwest and Northern California. Diagnostic data from electronic health records, submitted claims, and state death records from Oregon, Washington, and California were linked. Individual opioid-related poisoning codes (e.g., 965.xx and X42), and adverse effects of opioids codes (e.g., E935.xx) combined with diagnoses possibly indicative of overdoses (e.g., respiratory depression), were evaluated by comparison with chart audits.ResultsOpioid adverse effects codes had low PPV to detect overdoses (13.4%) as assessed in 127 charts and were not pursued. Instead, opioid poisoning codes were assessed in 2100 individuals who had those codes present in electronic health records in the period between the years 2008 and 2012. Of these, 10/2100 had no available information and 241/2100 were excluded potentially as anesthesia-related. Among the 1849 remaining individuals with opioid poisoning codes, 1495 events were accurately identified as opioid overdoses; 69 were miscodes or misidentified, and 285 were opioid adverse effects, not overdoses. Thus, PPV was 81%. Opioid adverse effects or overdoses were accurately identified in 1780 of 1849 events (96.3%).ConclusionsOpioid poisoning codes have a predictive value of 81% to identify opioid overdoses, suggesting ICD opioid poisoning codes can be used to monitor overdose rates and evaluate interventions to reduce overdose. Further research to assess sensitivity, specificity, and negative predictive value are ongoing. Copyright (c) 2017 John Wiley & Sons, Ltd.