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

数据到临床行动：使用预测分析来改善对患有阿片类药物使用障碍的退伍军人的护理

• 批准号: 10317224
• 负责人: Corey J Hayes
• 金额: --
• 依托单位: CENTRAL ARKANSAS VETERANS HLTHCARE SYS
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2027-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10317224
• 关键词: Address; Area; Artificial Intelligence; Automobile Driving; Big Data; Big Data Methods; Buprenorphine; Businesses; Caring; Clinical; Computers; Counseling; Data; Data Analytics; Data Science; Diagnosis; Discrimination; Dose; Drug usage; Effectiveness; Electronic Health Record; Enrollment; Ensure; Feasibility Studies; Focus Groups; Frequencies; Future; Guidelines; Health Services Accessibility; Healthcare; Hybrids; Individual; Informatics; Intelligence; Intervention; K-Series Research Career Programs; Knowledge; Learning; Logistic Regressions; Measures; Mental Health; Methadone; Methodology; Methods; Modeling; Monitor; Naltrexone; Opioid; Outcome; Overdose; Pain; Pain management; Patients; Persons; Pharmaceutical Preparations; Pharmacy facility; Predictive Analytics; Primary Health Care; Probability; Provider; Randomized Controlled Trials; Reporting; Research; Research Priority; Resources; Risk; Risk Factors; Sampling; Services; Site; Substance Use Disorder; Suicide; Techniques; Testing; Time; Training; Translating; Veterans; Visit; acceptability and feasibility; arm; base; big-data science; care outcomes; care systems; career; clinical decision support; comorbidity; data warehouse; design; effectiveness evaluation; effectiveness testing; experience; feasibility testing; feedforward neural network; follow-up; high risk; illicit drug use; implementation science; improved; improved outcome; innovation; interest; machine learning method; mathematical model; medical specialties; military veteran; modifiable risk; mortality; neural network; operation; opioid overdose; opioid use disorder; overdose risk; peer support; pilot test; pilot trial; predictive modeling; prevent; random forest; skills; standard of care; support tools; tool; treatment guidelines; treatment planning; usability; waiver

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.

背景阿片类药物使用障碍（MOUD）的药物预防过量并提高死亡率 OUD的退伍军人，但保留MOUD对于实现这些临床终点至关重要。只有50%的 退伍军人在MOUD启动后6个月保留在MOUD上。在额外需求方面的参与不足 护理服务是早期MOUD中止的重要风险因素。因此，供应商能够 确定在MOUD上需要额外护理或支持的退伍军人可能会增加退伍军人 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 经历被建模的结果（例如，MOUD中止）。未来MOUD预测 中止风险可以提供一种创新的和实时的方法来识别退伍军人需要 额外的护理（例如，同伴支持）。意义/影响。预测模型可用于降低MOUD 通过持续监测这些退伍军人的MOUD风险， 在积极的MOUD治疗期间实时中断，并确定需要额外治疗的退伍军人 护理（例如，如果两次就诊之间的风险增加，提供者可能会在治疗计划中增加同伴支持服务）。 创新该CDA-2包括三个HSR&D研究优先领域（阿片类药物/疼痛，医疗保健 信息学，并获得护理），同时横切“大”数据和实施科学的HSR方法，所有 努力改善对患有OUD的退伍军人的护理和结果。这项研究也将是第一个开发 并对基于预测模型的临床决策支持工具（CDST）进行试点测试，以改善退伍军人的MOUD 潴留具体目标。(1)开发和验证PREMMOUD，一个MOUD预测模型 中止假设：（H1）我将开发一个具有良好区分力的预测模型（例如，c-统计量，a 拟合优度指标，≥0.8），用于识别可能在最初6个月内停止MOUD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神经网络技术生成的模型更好的区分度。 使用随机森林和逻辑回归技术生成的模型。(2)将PREMMOUD改造成 CDST将持续监测MOUD停药的风险，并提供临床指南， 影响PREMMOUD评分的主要风险因素。(3)评估（a）进行大规模、 随机对照试验（RCT），以测试PREMMOUD CDST（P-CDST）的有效性以及（B）P- CDST在豁免供应商中的可接受性。假设：（H3）开展大规模的 将支持评价P-CDST有效性的RCT;（H4）VHA将接受P-CDST 放弃供应商。方法论使用机器学习方法和来自VHA Corporate Data的数据 仓库（2006-2019），我将培训和验证PREMMOUD在退伍军人发起的国家样本 MOUD（目标1）。对于目标2，我将与主要利益相关者（VHA提供者， 退伍军人接收MOUD，VHA运营合作伙伴），以通知创建P-CDST的测试版， 整合到CPRS/Cerner中。为了构建P-CDST，我将使用VHA CDW数据，PREMMOUD，SQL Server Reporting Services（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，以支持提供者解决 退伍军人的特定风险因素驱动PREMMOUD评分。对于目标3，我将进行单臂，双臂- 评估研究可行性的临床试验机构试点试验（提供者入组、P-CDST使用频率和随访率） 和P-CDST的可接受性（P-CDST的临床可用性）。执行/后续步骤。目标1将支持 在第3年提交HSR&D IIR，以评估PREMMOUD是否可用于识别哪些退伍军人， 接受MOUD，可以有效地在专科护理与非专科护理， 受益于额外的支持服务。第二个IIR提案将在CDA-2之后提交， RCT，采用混合设计，评价P-CDST在VHA中的有效性和实施潜力。