Data to Clinical Action: Using Predictive Analytics to Improve Care of Veterans with Opioid Use Disorder

背景资料。治疗阿片使用障碍(Moud)的药物可防止过量用药并提高死亡率 患有OUD但保留在Moud上的退伍军人对于实现这些临床终点至关重要。只有50%的 退伍军人在穆德印心后6个月被保留在穆德。在其他需要的项目中参与度较低 护理服务是Moud早期停药的一个重要风险因素。因此，提供商的能力 在Moud期间识别需要额外护理或支持的退伍军人可能会增加退伍军人患上 继续使用Moud。有效的预测模型可以提供单个退伍军人的准确概率 体验被模拟的结果(例如，停止使用Moud)。对未来Made的预测 停产风险可以提供一种创新的实时方法来识别有需要的退伍军人 额外照顾(例如，同伴支持)。重要性/影响力。预测模型可以用来降低Moud 通过持续监测退伍军人的MUD风险，改善他们的流失风险和结果 在主动Moud治疗期间实时停止治疗，并识别需要额外帮助的退伍军人 护理(例如，如果两次就诊之间的风险增加，提供者可以在治疗计划中增加同伴支持服务)。 创新。CDA-2包含三个HSR&D研究优先领域(阿片类药物/疼痛、医疗保健 信息学和获取关怀)，同时横切高铁的大数据方法和实施科学，所有 努力改善患有OUD的退伍军人的护理和预后。这项研究也将是第一个开展的 并试行基于预测模型的临床决策支持工具(CDST)，以改善退伍军人的情绪 留存。明确的目标。(1)开发和验证Moud的预测模型PREMMOUD 停产。假设：(H1)我将开发一个具有良好区分度的预测模型(例如，c-统计量，a 适合度测量，≥0.8)，用于识别可能在最初6个月内停止使用MOD的退伍军人 (H2)使用神经网络技术生成的模型将具有比 使用随机森林和Logistic回归技术生成的模型。(2)将PREMMOUD改编为 CDST持续监测MOD停用的风险，并提供临床指南 影响PREMMOUD评分的主要危险因素。(3)评估(A)大规模进行的可行性； 随机对照试验(RCT)，以测试前MOUD CDST(P-CDST)的有效性以及(B)P- CDST在被豁免的提供商中的可接受性。假设：(H3)开展大规模 将支持RCT以评估P-CDST的有效性；(H4)P-CDST将在VHA中被接受 被豁免的提供者。方法论。使用机器学习方法和来自VHA公司数据的数据 仓库(2006-2019)，我将在全国退伍军人启动样本中培训和验证PREMMOUD 穆德(目标1)。对于目标2，我将与主要利益攸关方(VHA提供者、 接受Moud的退伍军人、VHA运营合作伙伴)通知将创建P-CDST的测试版 整合到CPRS/CENER中。为了构建P-CDST，我将使用VHA CDW数据、PREMMOUD、SQL Server 报告服务(SSRS)和商业智能服务线(BISL)平台。P-CDST将包含 患者的实时PREMMOUD评分以及临床指南，以支持提供商解决 这位老兵的特定风险因素推动了PREMMOUD得分。对于目标3，我将进行单臂，双臂- 评估研究可行性的现场试点试验(提供者注册、P-CDST使用频率和随访率) P-CDST的可接受性(P-CDST的临床可用性)。实施/后续步骤。AIM 1将支持 在第三年提交HSR&D IIR，以评估PREMMOUD是否可以用于识别哪些退伍军人， 接受Moud，可以在专科护理和非专科护理中有效治疗，哪些退伍军人 受益于额外的支持性服务。第二份IIR提案将在CDA-2之后提交，以进行 RCT，采用混合设计，评估P-CDST在VHA中的有效性和实施潜力。

Background. Medication for opioid use disorder (MOUD) prevents overdoses and improves mortality in Veterans with OUD, but retention on MOUD is critical for achieving those clinical endpoints. Only 50% of Veterans are retained on MOUD at 6-months post-MOUD initiation. Poor engagement in additional needed care services is an important risk factor for early MOUD discontinuation. Consequently, providers’ ability to identify Veterans in need of additional care or support while on MOUD may increase the likelihood of Veterans’ continued use of MOUD. Valid predictive models can provide an accurate probability of an individual Veteran experiencing the outcome being modeled (e.g., MOUD discontinuation). Prediction of future MOUD discontinuation risk could provide an innovative and real-time method for identifying Veterans in need of additional care (e.g., peer support). Significance/Impact. Predictive models could be used to lower MOUD attrition risk and improve outcomes for this Veteran population by continuously monitoring their risk of MOUD discontinuation in real-time during active MOUD treatment and identifying those Veterans in need of additional care (e.g., if increasing risk between visits, providers might add peer support services to a treatment plan). Innovation. This CDA-2 encompasses three HSR&D research priority areas (opioid/pain, health care informatics, and access to care) while crosscutting HSR methods of “big” data and implementation science, all in an effort to improve care and outcomes for Veterans with OUD. This study will also be the first to develop and pilot test a clinical decision support tool (CDST), based on a predictive model, to improve Veterans’ MOUD retention. Specific Aims. (1) To develop and validate PREMMOUD, a PREdictive Model for MOUD discontinuation. Hypotheses: (H1) I will develop a predictive model with good discrimination (e.g., c-statistic, a measure of goodness-of-fit, ≥0.8) for identifying Veterans likely to discontinue MOUD within the initial 6 months of treatment; (H2) the model generated using neural network techniques will have better discrimination than the models generated using random forest and logistic regression techniques. (2) To adapt PREMMOUD into a CDST to continuously monitor risk of MOUD discontinuation and provide clinical guidelines for addressing the primary risk factors driving the PREMMOUD score. (3) To assess (a) the feasibility of conducting a large scale, randomized controlled trial (RCT) to test PREMMOUD CDST’s (P-CDST) effectiveness as well as (b) P- CDST’s acceptability among waivered providers. Hypotheses: (H3) The feasibility of conducting a large-scale RCT to evaluate P-CDST’s effectiveness will be supported; (H4) P-CDST will be acceptable among VHA waivered providers. Methodology. Using machine-learning methods and data from the VHA Corporate Data Warehouse (2006-2019), I will train and validate PREMMOUD in a national sample of Veterans initiating MOUD (Aim 1). For Aim 2, I will conduct two rounds of focus groups with key stakeholders (VHA providers, Veterans receiving MOUD, VHA operations partners) to inform the creation of a beta-version of P-CDST to be integrated into CPRS/Cerner. To build P-CDST, I will use VHA CDW data, PREMMOUD, SQL Server Reporting Services (SSRS) and the Business Intelligence Service Line (BISL) platform. P-CDST will contain the patient’s real-time PREMMOUD score as well as clinical guidelines to support the provider in addressing the Veteran’s specific risk factors driving the PREMMOUD score. For Aim 3, I will conduct a single-arm, two- site pilot trial to assess study feasibility (provider enrollment, frequency of P-CDST use, and follow-up rates) and P-CDST’s acceptability (clinical usability of P-CDST). Implementation/Next Steps. Aim 1 will support an HSR&D IIR submission in Year 3 to assess whether PREMMOUD can be used to identify which Veterans, receiving MOUD, can effectively be treated in specialty care versus non-specialty care and which Veterans benefit from additional supportive services. A second IIR proposal will be submitted post CDA-2 to conduct an RCT, using a hybrid design, to evaluate the effectiveness and implementation potential of P-CDST in VHA.