The OUD Cascade of Care and Critical Outcomes: Longitudinal Linkage with Opioid Use

护理和危重结局的级联：与药物使用的纵向联系 美国服药过量死亡人数急剧增加，迫切需要改善获得 和药物治疗阿片使用障碍(OUD)的循证护理保留(Moud)，在OUD下 关怀的级联框架。级联模型强调治疗过程中的关键阶段或过渡。 降低不良后果风险的OUD患者：关键、成功的MOD启动和长期治疗 留存。然而，到目前为止，它对同时使用阿片类药物一直是不可知的。在芬太尼崛起的时代 鉴于非法药物供应的普遍性，公共卫生框架需要根据下列环境条件进行调整 可能会影响临床结果。我们的研究方法源于对阿片类药物使用、治疗的概念化 响应和不良后果，在不同的时间范围内沿着轨迹演变，强调 随着时间的推移，需要针对患者目标量身定做减少伤害的方法。当我们假设 在当地药物供应中更多地接触芬太尼将阻碍成功的治疗接触，我们 也希望丁丙诺啡治疗持续时间更长，以防止过量用药的风险，即使在患者中也是如此 在护理期间间歇性使用阿片类药物。然而，在护理中使用阿片类药物的患者可能会经历 在停止治疗后风险降低减弱。 迫切需要对治疗途径和结果进行纵向分析，包括 毒理学结果和治疗保留的交叉作用和预后意义，及其 在治疗期间和之后与过量用药风险的关系。因为目前关于最优的证据 缺乏持续时间的护理，特别是在个别病人或亚人群一级，目前几乎没有 指导临床医生和卫生系统如何最好地量身定做服务提供和 在患者层面评估不良事件的风险。我们的建议旨在填补这些关键的知识空白。 我们将链接来自多个州的丁丙诺啡提供者的95,000人的电子健康记录--美国的 最大的OBOT提供商--利用医疗补助申请和国家死亡指数(NDI)数据，创建一个新的、独特的、 跨越2014-2022年的十几个州的综合数据库，以观察患者在服药前、基线和 在护理期间超过多年，占了芬太尼攻击期间的死亡人数。医疗补助现在是 穆德的最大付款人。我们的发现将对临床护理、政策、系统设计、 质量测量开发和医疗保健管理。我们的目标是建立一个经验主义的 为临床评估患者风险(例如，包括过量用药在内的不良后果)提供信息的证据基础 个人层面。面对不断演变的阿片类药物流行的规模和严重性，至关重要的是临床和政策 战略的依据是仔细考虑对经验数据的严格分析，并保持大量的日常关注 跨越州界的人口。

The OUD Cascade of Care and Critical Outcomes: Longitudinal Linkage with Drug Use Dramatic increases in overdose deaths in the US have created an urgent need to improve access to and retention in evidence-based care for opioid use disorder (OUD) with medication (MOUD), under the OUD Cascade of Care framework. The Cascade model emphasizes key stages or transitions during the treatment of individuals with OUD to reduce risks of adverse outcomes: critically, successful MOUD initiation and long-term retention. However, it has thus far been agnostic towards concurrent opioid use. In the era of rising fentanyl prevalence in the illicit drug supply, public health frameworks need to adjust for environmental conditions that may impact clinical outcomes. Our research approach draws from a conceptualization of opioid use, treatment response, and adverse outcomes as evolving along trajectories, over varying timeframes emphasizing the need for harm reduction approaches tailored to patient goals as they evolve over time. While we hypothesize that greater exposure to fentanyl in the local drug supply will impede successful treatment engagement, we also expect longer durations of buprenorphine treatment to protect against overdose risk, even among patients with intermittent opioid use while in care. However, patients with opioid use in care will likely experience attenuated risk reductions following treatment discontinuation. There is a critical need for longitudinal analyses of treatment pathways and outcomes, including the intersecting roles and prognostic significance of toxicology results and treatment retention, and their relationship to overdose risk during and following treatment. Because current evidence concerning optimal durations of care, especially at the individual patient or sub-population level, is lacking, there is currently little empirical evidence to guide clinicians and health systems regarding how to best tailor service provision and estimate risks of adverse events at the patient level. Our proposal seeks to fill these critical gaps in knowledge. We will link EHR records for 95,000 individuals from a multi-state buprenorphine provider-- the nation’s largest OBOT provider-- with Medicaid claims and National Death Index (NDI) data to create a new, unique, integrated database spanning 2014-2022 in a dozen states to observe patients before intake, at baseline, and while in care over multiple years, accounting for mortality during the onslaught of fentanyl. Medicaid is now the largest payer for MOUD. Our findings will have broad implications for clinical care, policy, systems design, quality measurement development, and healthcare administration. Our goal is to establish an empirical evidence base to inform clinical evaluations of patient risks (e.g. adverse outcomes including overdose) at the individual level. Against the scale and gravity of the evolving opioid epidemic, it is vital that clinical and policy strategies are informed by careful consideration of rigorous analyses of empirical data with large, usual-care populations across state lines.