Janssen Research and Development Titusville, Titusville, NJ, United States of America.
PLoS One. 2020 Feb 13;15(2):e0228632. doi: 10.1371/journal.pone.0228632. eCollection 2020.
Some patients who are given opioids for pain could develop opioid use disorder. If it was possible to identify patients who are at a higher risk of opioid use disorder, then clinicians could spend more time educating these patients about the risks. We develop and validate a model to predict a person's future risk of opioid use disorder at the point before being dispensed their first opioid.
A cohort study patient-level prediction using four US claims databases with target populations ranging between 343,552 and 384,424 patients. The outcome was recorded diagnosis of opioid abuse, dependency or unspecified drug abuse as a proxy for opioid use disorder from 1 day until 365 days after the first opioid is dispensed. We trained a regularized logistic regression using candidate predictors consisting of demographics and any conditions, drugs, procedures or visits prior to the first opioid. We then selected the top predictors and created a simple 8 variable score model.
We estimated the percentage of new users of opioids with reported opioid use disorder within a year to range between 0.04%-0.26% across US claims data. We developed an 8 variable Calculator of Risk for Opioid Use Disorder (CROUD) score, derived from the prediction models to stratify patients into higher and lower risk groups. The 8 baseline variables were age 15-29, medical history of substance abuse, mood disorder, anxiety disorder, low back pain, renal impairment, painful neuropathy and recent ER visit. 1.8% of people were in the high risk group for opioid use disorder and had a score > = 23 with the model obtaining a sensitivity of 13%, specificity of 98% and PPV of 1.14% for predicting opioid use disorder.
CROUD could be used by clinicians to obtain personalized risk scores. CROUD could be used to further educate those at higher risk and to personalize new opioid dispensing guidelines such as urine testing. Due to the high false positive rate, it should not be used for contraindication or to restrict utilization.
一些接受阿片类药物治疗疼痛的患者可能会出现阿片类药物使用障碍。如果能够识别出更容易出现阿片类药物使用障碍的患者，那么临床医生就可以花更多的时间对这些患者进行风险教育。我们开发并验证了一种模型，以在开具第一剂阿片类药物之前预测患者未来出现阿片类药物使用障碍的风险。
这是一项使用四个美国索赔数据库的队列研究患者水平预测，目标人群范围在 343552 至 384424 名患者之间。结果是记录从第一天到开具第一剂阿片类药物后 365 天内，阿片类药物滥用、依赖或未特指药物滥用的诊断，作为阿片类药物使用障碍的替代指标。我们使用包括人口统计学和任何疾病、药物、程序或就诊情况在内的候选预测因子，对正则逻辑回归进行了训练。然后，我们选择了最佳预测因子并创建了一个简单的 8 变量评分模型。
我们估计，在美国索赔数据中，新使用阿片类药物的患者中，在一年内报告出现阿片类药物使用障碍的比例在 0.04%-0.26%之间。我们开发了一个 8 变量阿片类药物使用障碍风险计算器（CROUD）评分，该评分源自预测模型，用于将患者分层为高风险和低风险组。这 8 个基线变量是年龄 15-29 岁、物质滥用的病史、情绪障碍、焦虑障碍、下腰痛、肾功能不全、痛性神经病和最近的急诊就诊。1.8%的人处于阿片类药物使用障碍的高风险组，其评分≥23，该模型的敏感性为 13%，特异性为 98%，阳性预测值为 1.14%，用于预测阿片类药物使用障碍。
CROUD 可以由临床医生用于获得个性化的风险评分。CROUD 可用于对高风险人群进行进一步教育，并为个性化新的阿片类药物发放指南（如尿液检测）提供依据。由于假阳性率较高，不应将其用于禁忌症或限制使用。
