一种用于评估初级保健环境中阿片类药物使用障碍的预测算法的验证研究。
Validation Study of a Predictive Algorithm to Evaluate Opioid Use Disorder in a Primary Care Setting.
作者信息
Sharma Maneesh, Lee Chee, Kantorovich Svetlana, Tedtaotao Maria, Smith Gregory A, Brenton Ashley
机构信息
Good Samaritan Hospital, Baltimore, MD, USA.
Proove Biosciences Inc, Irvine, CA, USA.
出版信息
Health Serv Res Manag Epidemiol. 2017 Aug 24;4:2333392817717411. doi: 10.1177/2333392817717411. eCollection 2017 Jan-Dec.
BACKGROUND
Opioid abuse in chronic pain patients is a major public health issue. Primary care providers are frequently the first to prescribe opioids to patients suffering from pain, yet do not always have the time or resources to adequately evaluate the risk of opioid use disorder (OUD).
PURPOSE
This study seeks to determine the predictability of aberrant behavior to opioids using a comprehensive scoring algorithm ("profile") incorporating phenotypic and, more uniquely, genotypic risk factors.
METHODS AND RESULTS
In a validation study with 452 participants diagnosed with OUD and 1237 controls, the algorithm successfully categorized patients at high and moderate risk of OUD with 91.8% sensitivity. Regardless of changes in the prevalence of OUD, sensitivity of the algorithm remained >90%.
CONCLUSION
The algorithm correctly stratifies primary care patients into low-, moderate-, and high-risk categories to appropriately identify patients in need for additional guidance, monitoring, or treatment changes.
背景
慢性疼痛患者中的阿片类药物滥用是一个重大的公共卫生问题。初级保健提供者常常是最先给疼痛患者开阿片类药物的人，但他们并不总是有时间或资源来充分评估阿片类药物使用障碍（OUD）的风险。
目的
本研究旨在使用一种综合评分算法（“概况”）来确定异常行为对阿片类药物的可预测性，该算法纳入了表型风险因素，更独特的是还纳入了基因型风险因素。
方法与结果
在一项针对452名被诊断为OUD的参与者和1237名对照的验证研究中，该算法以91.8%的灵敏度成功将OUD高风险和中度风险患者进行了分类。无论OUD患病率如何变化，该算法的灵敏度均保持>90%。
结论
该算法可正确地将初级保健患者分为低风险、中度风险和高风险类别，以适当识别需要额外指导、监测或治疗调整的患者。
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