Prediction of Future Chronic Opioid Use Among Hospitalized Patients
Prediction of Future Chronic Opioid Use Among Hospitalized Patients
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DOI：
10.1007/s11606-018-4335-8
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发表时间：
2018-06-01
影响因子：
5.7
通讯作者：
Colborn, K. L.
中科院分区：
文献类型：
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作者：
Calcaterra, S. L.;Scarbro, S.;Colborn, K. L.
阿片类药物在医院中普遍使用；然而，对于哪些患者在出院后会进展为慢性阿片类药物治疗（COT）却知之甚少。我们将COT定义为在180天内接受90天的阿片类药物供应且供应间隔小于30天，或者在1年内接受10次阿片类药物处方。用于识别有未来慢性阿片类药物使用风险的住院患者的预测工具可能具有临床实用性，可改善住院期间和出院时的疼痛管理策略以及患者教育。本研究的目的是确定一个简约的统计模型，用于预测住院前未接受COT的住院患者未来的COT情况。使用逻辑回归对2008年至2014年的电子健康记录（EHR）数据进行回顾性分析。城市安全网医院的住院患者。自变量包括医疗和心理健康诊断、物质和烟草使用障碍、慢性或急性疼痛、住院期间的手术干预、过去一年接受阿片类或非阿片类镇痛药或苯二氮䓬类药物、出院时接受阿片类药物、每住院日开具的吗啡等效剂量以及其他因素。使用受试者工作特征曲线下面积、准确性、敏感性和特异性来评估模型预测性能。通过对数据的随机下采样子集进行逐步逻辑回归，选择了一个具有13个协变量的模型。使用约登指数优化敏感性和特异性。该模型对79%的患者的COT情况预测正确，对78%的患者未发生COT的情况预测正确。我们的模型利用EHR数据预测了住院患者中79%的未来COT情况。在EHR中应用这样一个预测模型可以识别出未来有慢性阿片类药物使用高风险的患者，使临床医生能够就疼痛管理策略对患者进行早期教育，并且在可能的情况下，在出院前减少阿片类药物的使用，同时将替代疼痛治疗方法纳入出院计划。
Opioids are commonly prescribed in the hospital; yet, little is known about which patients will progress to chronic opioid therapy (COT) following discharge. We defined COT as receipt of 90-day supply of opioids with < 30-day gap in supply over a 180-day period or receipt of 10 opioid prescriptions over 1 year. Predictive tools to identify hospitalized patients at risk for future chronic opioid use could have clinical utility to improve pain management strategies and patient education during hospitalization and discharge.The objective of this study was to identify a parsimonious statistical model for predicting future COT among hospitalized patients not on COT before hospitalization.Retrospective analysis electronic health record (EHR) data from 2008 to 2014 using logistic regression.Hospitalized patients at an urban, safety net hospital.Independent variables included medical and mental health diagnoses, substance and tobacco use disorder, chronic or acute pain, surgical intervention during hospitalization, past year receipt of opioid or non-opioid analgesics or benzodiazepines, opioid receipt at hospital discharge, milligrams of morphine equivalents prescribed per hospital day, and others.Model prediction performance was estimated using area under the receiver operator curve, accuracy, sensitivity, and specificity. A model with 13 covariates was chosen using stepwise logistic regression on a randomly down-sampled subset of the data. Sensitivity and specificity were optimized using the Youden's index. This model predicted correctly COT in 79% of the patients and no COT correctly in 78% of the patients.Our model accessed EHR data to predict 79% of the future COT among hospitalized patients. Application of such a predictive model within the EHR could identify patients at high risk for future chronic opioid use to allow clinicians to provide early patient education about pain management strategies and, when able, to wean opioids prior to discharge while incorporating alternative therapies for pain into discharge planning.