Computational Strategies to Tailor Existing Interventions for First Major Depressive Episodes to Inform and Test Personalized Interventions

摘要 重度抑郁症（MDD）是一种慢性复发性疾病，影响20.6%的美国人口， 造成严重残疾，每年造成3262亿美元的经济影响。最大的风险因素之一 抑郁症慢性和残疾是抗抑郁药反应不足，定义为低于50% 开始抗抑郁治疗后抑郁症状改善。抗抑郁药被 被推荐为一线抑郁症治疗药物，70%的抑郁症患者服用。不足 50-60%开始抗抑郁药治疗的患者会出现抗抑郁反应， 47%的经济影响和由MDD引起的残疾。因此，识别风险不充分 在患者首次抑郁发作的临床表现期间，早期抗抑郁反应将是 一个创新的，迫切需要的第一步，以防止复发性抑郁发作，减少 抑郁症慢性化和残疾，改善MDD的结果。这一步骤与国家研究所保持一致 战略计划目标3.2：“制定战略， （抗抑郁药）以优化（抑郁发作）结果。” 虽然以前的研究确定了抗抑郁药反应的独立预测因子，但迄今为止还没有研究关注 整合已知和新的抗抑郁药反应不足的预测因素， 抑郁发作这种知识差距的存在是因为没有在不同人群中进行的大型研究整合 在一个模型中综合临床、人口统计学、遗传学和行为学信息， 在首次抗抑郁药治疗前的抗抑郁反应。这些信息对于改善患者的 护理，减少抑郁症的慢性化和残疾，并定制现有的患者干预措施，以优化 MDD结局。利用来自三个大型综合医疗保健系统的电子健康记录数据 代表超过690万名会员（Kaiser Permanente（KP）北方加州、KP华盛顿和 HealthPartners），我们的目标是量化患者首次服用抗抑郁药时抗抑郁药反应不足的风险。 通过整合临床、人口统计学、遗传学和行为学，确定抑郁发作的临床表现 一个预测模型中的信息。为了实现这一目标，我们将使用翻译机器学习， 预测建模，内部和外部模型验证和测试，前瞻性验证，以及现有的 全基因组基因型数据。此外，我们还将研究临床应用的障碍和促进因素， MDD的预测模型，以促进临床翻译和预测模型的实施， 研究创新和临床应用之间的时间。我们的长期目标是开发一种临床工具 为决策提供信息并促进MDD治疗优化。

ABSTRACT Major depressive disorder (MDD) is a chronic, recurrent illness impacting 20.6% of the U.S. population, causing significant disability and an economic impact of $326.2 billion annually. One of the largest risk factors for depression chronicity and disability is inadequate antidepressant response, defined as less than a 50% improvement in depressive symptoms after starting antidepressant treatment. Antidepressants are recommended as a first-line depression treatment and taken by 70% of patients with depression. Inadequate antidepressant response is experienced by 50-60% of patients starting an antidepressant and is responsible for 47% of the economic impact and disability caused by MDD. As such, identifying risk for inadequate antidepressant response early, during a patient’s first clinical presentation for a depressive episode, would be an innovative, urgently needed first step towards preventing recurrent depressive episodes, reducing depression chronicity and disability, and improving MDD outcomes. This step aligns with The National Institute of Mental Health (NIMH) Strategic Plan Objective 3.2 to “develop strategies for tailoring existing interventions (antidepressants) to optimize (depressive episode) outcomes.” While previous studies identified separate predictors of antidepressant response, no study to date has focused on integrating known and novel predictors of inadequate antidepressant response during a patient’s first depressive episode. This knowledge gap exists as no large studies in diverse populations have integrated comprehensive clinical, demographic, genetic, AND behavioral information in one model to predict inadequate antidepressant response prior to first antidepressant treatment. Such information is crucial to improve patient care, reduce depressive disorder chronicity and disability, and tailor existing patient interventions to optimize MDD outcomes. Utilizing electronic health record data from three large, integrated healthcare systems representing over 6.9 million members (Kaiser Permanente (KP) Northern California, KP Washington, and HealthPartners), we aim to quantify inadequate antidepressant response risk at the time of a patient’s first clinical presentation for a depressive episode by integrating clinical, demographic, genetic, and behavioral information in one predictive model. To accomplish this aim, we will use translational machine learning and predictive modeling, internal and external model validation and testing, prospective validation, and existing genome-wide genotypic data. Further, we will examine barriers and facilitators to clinical applications of predictive models for MDD to facilitate clinical translation and implementation of the predictive model, reducing the time between research innovation and clinical application. Our long-term goal is to develop a clinical tool informing decision making and promoting MDD treatment optimization.