High-risk prescribing and opioid overdose: prospects for prescription drug monitoring program-based proactive alerts
High-risk prescribing and opioid overdose: prospects for prescription drug monitoring program-based proactive alerts
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DOI：
10.1097/j.pain.0000000000001078
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发表时间：
2018-01-01
期刊：
影响因子：
7.4
通讯作者：
Deyo, Richard A.
中科院分区：
文献类型：
--
作者：
Geissert, Peter;Hallvik, Sara;Deyo, Richard A.
为了开发一个简单、有效的模型来识别阿片类药物过量相关住院和死亡的高风险患者，俄勒冈州处方药监测计划、生命记录和医院出院数据被链接到估计2个Logistic模型；第一个模型包括文献中的广泛风险因素，第二个简化模型。对模型的受试者工作特征曲线、灵敏度和特异度进行分析。最终模型中保留的变量包括年龄超过35岁、处方医生数量、药店数量以及长效阿片类药物、苯二氮卓类药物或镇静剂或卡利索前列醇的处方。该模型区分服药过量和未服药患者的能力相当好(受试者工作特征曲线下面积=0.82，Nagelkerke R-2=0.11)。模型的阳性预测值较低。计算简单的模型可以仅根据处方历史来识别高危患者，但提高模型的预测价值可能需要来自处方药监测计划之外的信息。预测阿片类药物过量的患者或处方特征可能不同于预测转移的特征。
To develop a simple, valid model to identify patients at high risk of opioid overdose-related hospitalization and mortality, Oregon prescription drug monitoring program, Vital Records, and Hospital Discharge data were linked to estimate 2 logistic models; a first model that included a broad range of risk factors from the literature and a second simplified model. Receiver operating characteristic curves, sensitivity, and specificity of the models were analyzed. Variables retained in the final model were categories such as older than 35 years, number of prescribers, number of pharmacies, and prescriptions for long-acting opioids, benzodiazepines or sedatives, or carisoprodol. The ability of the model to discriminate between patients who did and did not overdose was reasonably good (area under the receiver operating characteristic curve = 0.82, Nagelkerke R-2 = 0.11). The positive predictive value of the model was low. Computationally simple models can identify high-risk patients based on prescription history alone, but improvement of the predictive value of models may require information from outside the prescription drug monitoring program. Patient or prescription features that predict opioid overdose may differ from those that predict diversion.