Using existing data to understand and ameliorate risk in opioid agonist therapy

项目摘要/摘要 美国正处于阿片类药物流行之中，导致前所未有的过量死亡 以及其他伤害。阿片类激动剂疗法是治疗阿片类药物使用障碍的有效方法。 (燕麦)加美沙酮或丁丙诺啡。然而，存在关于不良反应风险的重大问题。 临床结果，包括治疗期间和治疗后的死亡率和住院率，以及计划外治疗 停止。这些风险的大小是什么，患者、治疗环境和提供者因素可能是什么 是促成风险还是防范风险？此外，越来越明显的是，更复杂的方法 需要比目前使用的更好的患者评估和治疗计划，以将风险降至最低。我们的目标是：1. 确定特定不良临床结果(例如死亡率、住院时间和ED)的风险大小 出现症状和计划外停药)在使用美沙酮和丁丙诺啡的燕麦中和之后； 确定与不良临床结果相关的患者、治疗环境和提供者风险因素 以及美沙酮和丁丙诺啡燕麦后；以及3.开发风险预测模型来识别患者 在燕麦片治疗期间和之后出现不良临床结果的风险最大。为了实现这些目标，本项目将使用 现有基于人口的澳大利亚OAT数据，链接到几个卫生和刑事司法数据集，以 提供对治疗暴露和结果的丰富了解。这些数据将被用于通知 指导在美国提供高质量阿片类药物使用障碍治疗的战略。 具体地说，该项目将提供有关不良临床结果风险大小的数据。 具体的治疗和治疗后时期，并确定患者、治疗环境和提供者因素 影响风险。此外，它还将使用创新的机器学习技术来展示 常规收集的数据用于在护理点评估患者风险，从而允许制定 量身定制的治疗计划，最大限度地减少风险和最大限度地保留治疗。

PROJECT SUMMARY/ABSTRACT The United States is in the midst of an opioid epidemic, leading to unprecedented levels of overdose deaths and other harms. Effective treatment for opioid use disorders is available, in the form of opioid agonist therapy (OAT) with methadone or buprenorphine. However, there are significant questions about the risk of adverse clinical outcomes, including mortality and hospitalization during and after treatment, and unplanned treatment cessation. What is the magnitude of these risks, and what patient, treatment setting, and provider factors may contribute to or protect against risk? Additionally, it is increasingly clear that more sophisticated approaches to patient assessment and treatment planning than are currently used are needed to minimise risk. We aim to: 1. Determine the magnitude of risk for specific adverse clinical outcomes (e.g. mortality, hospitalization and ED presentation, and unplanned treatment cessation) during and after OAT with methadone and buprenorphine; 2. Identify patient, treatment setting, and provider risk factors associated with adverse clinical outcomes during and after OAT with methadone and buprenorphine; and 3. Develop a risk prediction model to identify patients at greatest risk of adverse clinical outcomes during and after OAT. To achieve these aims, this project will use existing population-based Australian data on OAT, linked to several health and criminal justice datasets to provide a rich understanding of treatment exposures and outcomes. These data will be used to inform strategies to guide the delivery of high-quality treatment for opioid use disorder in the United States. Specifically, the project will provide data about the magnitude of risk of adverse clinical outcomes during specific treatment and post-treatment periods, and identify patient, treatment setting and provider factors that influence risk. Additionally, it will use innovative machine learning techniques to demonstrate the potential for routinely collected data to be used to assess patient risk at point-of-care, allowing for the development of tailored treatment plans that minimize risk and maximize treatment retention.