Prediction Model for Two-Year Risk of Opioid Overdose Among Patients Prescribed Chronic Opioid Therapy
Prediction Model for Two-Year Risk of Opioid Overdose Among Patients Prescribed Chronic Opioid Therapy
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DOI：
10.1007/s11606-017-4288-3
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发表时间：
2018-10-01
影响因子：
5.7
通讯作者：
Binswanger, Ingrid A.
中科院分区：
文献类型：
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作者：
Glanz, Jason M.;Narwaney, Komal J.;Binswanger, Ingrid A.
纳洛酮是一种可挽救生命的阿片类拮抗剂。慢性疼痛指南建议医生共同开纳洛酮给阿片类药物过量高风险的患者。然而，临床工具，以有效地确定谁可以受益于纳洛酮的患者是缺乏的。开发和验证过量的预测模型，可用于初级保健设置，以评估纳洛酮的需要。回顾性队列。衍生网站是一个综合卫生系统在科罗拉多;验证地点是科罗拉多的一个安全网卫生系统。我们在42人的队列中开发了一个预测模型，828例接受慢性阿片类药物治疗的患者，并在10，708例患者中外部验证了该模型。从电子健康记录中提取了潜在的预测因素和结果（非致命性药物和海洛因过量）。从州生命记录中确定了致死性药物过量结局。为了匹配纳洛酮的近似有效期，我们使用考克斯比例风险回归对2年过量风险进行建模。5变量预测模型在推导位点具有良好的校正和区分度（bootstrap校正的c-统计量= 0.73，95%可信区间[CI] 0.69-0.78），灵敏度为66.1%，特异度为66.6%。在验证中心，该模型显示出良好的区分度（c-统计量= 0.75，95%CI 0.70-0.80）和低于理想的校准，灵敏度和特异性分别为82.2%和49.5%，在慢性阿片类药物治疗的患者中，预测模型确定了66-82%的所有后续阿片类药物过量。该模型是一种有效的筛选工具，可用于识别可从纳洛酮中获益的患者，以防止过量死亡。两个研究中心的群体差异限制了验证中心的校准。
Naloxone is a life-saving opioid antagonist. Chronic pain guidelines recommend that physicians co-prescribe naloxone to patients at high risk for opioid overdose. However, clinical tools to efficiently identify patients who could benefit from naloxone are lacking.To develop and validate an overdose predictive model which could be used in primary care settings to assess the need for naloxone.Retrospective cohort.Derivation site was an integrated health system in Colorado; validation site was a safety-net health system in Colorado.We developed a predictive model in a cohort of 42,828 patients taking chronic opioid therapy and externally validated the model in 10,708 patients.Potential predictors and outcomes (nonfatal pharmaceutical and heroin overdoses) were extracted from electronic health records. Fatal overdose outcomes were identified from state vital records. To match the approximate shelf-life of naloxone, we used Cox proportional hazards regression to model the 2-year risk of overdose. Calibration and discrimination were assessed.A five-variable predictive model showed good calibration and discrimination (bootstrap-corrected c-statistic = 0.73, 95% confidence interval [CI] 0.69-0.78) in the derivation site, with sensitivity of 66.1% and specificity of 66.6%. In the validation site, the model showed good discrimination (c-statistic = 0.75, 95% CI 0.70-0.80) and less than ideal calibration, with sensitivity and specificity of 82.2% and 49.5%, respectively.Among patients on chronic opioid therapy, the predictive model identified 66-82% of all subsequent opioid overdoses. This model is an efficient screening tool to identify patients who could benefit from naloxone to prevent overdose deaths. Population differences across the two sites limited calibration in the validation site.