Division of Pharmaceutical Outcomes and Policy, University of North Carolina at Chapel Hill Eshelman School of Pharmacy, Chapel Hill, North Carolina, USA
Department of Psychiatry, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, North Carolina, USA.
BMJ Open. 2022 Sep 14;12(9):e059414. doi: 10.1136/bmjopen-2021-059414.
Emergency departments (EDs) are an important point of contact for people with opioid use disorder (OUD). Universal screening for OUD is costly and often infeasible. Evidence on effective, selective screening is needed. We assessed the feasibility of using a risk factor-based machine learning model to identify OUD quickly among patients presenting in EDs.
DESIGN/SETTINGS/PARTICIPANTS: In this cohort study, all ED visits between January 2016 and March 2018 for patients aged 12 years and older were identified from electronic health records (EHRs) data from a large university health system. First, logistic regression modelling was used to describe and elucidate the associations between patient demographic and clinical characteristics and diagnosis of OUD. Second, a Gradient Boosting Classifier was applied to develop a predictive model to identify patients at risk of OUD. The predictive performance of the Gradient Boosting algorithm was assessed using F1 scores and area under the curve (AUC).
The primary outcome was the diagnosis of OUD.
Among 345 728 patient ED visits (mean (SD) patient age, 49.4 (21.0) years; 210 045 (60.8%) female), 1.16% had a diagnosis of OUD. Bivariate analyses indicated that history of OUD was the strongest predictor of current OUD (OR=13.4, CI: 11.8 to 15.1). When history of OUD was excluded in multivariate models, baseline use of medications for OUD (OR=3.4, CI: 2.9 to 4.0) and white race (OR=2.9, CI: 2.6 to 3.3) were the strongest predictors. The best Gradient Boosting model achieved an AUC of 0.71, accuracy of 0.96 but only 0.45 sensitivity.
Patients who present at the ED with OUD are high-need patients who are typically smokers with psychiatric, chronic pain and substance use disorders. A machine learning model did not improve predictive ability. A quick review of a patient's EHR for history of OUD is an efficient strategy to identify those who are currently at greatest risk of OUD.
急诊科是患有阿片类药物使用障碍（OUD）人群的重要接触点。对 OUD 进行普遍筛查既昂贵又常常不可行。需要有证据证明有效的、有针对性的筛查方法是可行的。我们评估了使用基于风险因素的机器学习模型在急诊科快速识别 OUD 的可行性。
设计/设置/参与者：在这项队列研究中，我们从一个大型大学健康系统的电子健康记录（EHR）数据中确定了 2016 年 1 月至 2018 年 3 月期间所有 12 岁及以上患者的急诊科就诊情况。首先，使用逻辑回归模型来描述和阐明患者人口统计学和临床特征与 OUD 诊断之间的关联。其次，应用梯度提升分类器来建立预测模型，以识别患有 OUD 的高风险患者。使用 F1 评分和曲线下面积（AUC）评估梯度提升算法的预测性能。
主要结局是 OUD 的诊断。
在 345728 例患者的急诊科就诊中（患者年龄的平均值（标准差）为 49.4（21.0）岁；210045 例（60.8%）为女性），1.16%的患者被诊断为 OUD。双变量分析表明，OUD 病史是当前 OUD 的最强预测因素（OR=13.4，95%CI：11.8 至 15.1）。当在多变量模型中排除 OUD 病史时，基线使用治疗 OUD 的药物（OR=3.4，95%CI：2.9 至 4.0）和白人种族（OR=2.9，95%CI：2.6 至 3.3）是最强的预测因素。最佳梯度提升模型的 AUC 为 0.71，准确性为 0.96，但敏感性仅为 0.45。
在急诊科就诊的患有 OUD 的患者是高需求患者，他们通常是吸烟者，患有精神疾病、慢性疼痛和物质使用障碍。机器学习模型并未提高预测能力。快速查看患者的 EHR 以了解 OUD 病史是识别目前处于最大 OUD 风险的有效策略。
