Deriving high-quality evidence from national healthcare databases to improve suicidality detection and treatment outcomes in PTSD

项目总结 创伤后应激障碍(PTSD)通常具有复杂的共生疾病特征， 与自我伤害的高风险有关，包括自杀，这是导致死亡的主要原因，特别是 在退伍军人中。创伤后应激障碍的药物治疗严重缺乏进展，如增加的 在证据有限的情况下使用标签外药物和多药联用(同时使用多种药物) 他们的相对风险和收益。此外，创伤后应激障碍以及自杀和非自杀的自残行为往往依然存在。 未记录在电子健康记录(EHR)中。疾病结果的可预测性也很差，因为 药物治疗的变化频繁，多种改变的共生条件 包括抑郁症、躁郁症、精神分裂症、物质使用障碍、创伤性脑损伤以及 睡眠障碍。我们的长期目标是改进诊断、二级/三级预防和治疗 通过提高电子病历利用率，创伤后应激障碍及其共生状况的转归。为了达到我们的目标，我们 将分析来自退伍军人健康管理局(VHA)和非VHA的电子病历和行政索赔数据 数据库，总共涵盖180万创伤后应激障碍患者。具体来说，我们的目标是：(1)识别未被检测到的和 未编码的共生心理健康表型，使用机器学习和 描述其文档中的差异；(2)创建稳健的模型，考虑到偏差和 共发条件，以确定创伤后应激障碍失代偿/恢复的临床轨迹 以及(3)比较使用不同治疗方法的创伤后应激障碍患者的自残风险和住院风险。 通过国际网络研究对表型进行编码和归类。我们将比较两种方法的有效性 创伤后应激障碍精神药物单一疗法、多药联用和心理疗法，以指导选择治疗 改善了患者的预后。通过增强和验证正向无标记机器学习方法 由我们的团队开发，我们将认为未记录/未检测到的心理健康状况与 VHA和非VHA数据库中的创伤后应激障碍，并描述与文档相关的因素 差距。我们将通过增强的潜在类别/潜在轨迹分析对疾病轨迹进行建模，重点是 创伤后应激障碍中的自我伤害、物质使用障碍和精神科住院。最后，我们将执行 迄今为止最大规模的创伤后应激障碍治疗对&gt；100单一疗法和 除心理治疗干预外，使用因果模型和方法治疗多种药物疗法 解决偏见问题。这些研究将为住院和自杀风险提供高质量的证据。 行为/自残。成功完成这些调查将提高临床精神病学的质量 决策，并指导向退伍军人和非退伍军人提供更好的服务 创伤后应激障碍/创伤后应激障碍，和/或高风险的自我伤害/自杀。

PROJECT SUMMARY Post-traumatic stress disorder (PTSD) often has complex profiles of co-occurring medical conditions and is associated with high risk of self-harm, including suicidality, which is a leading cause of death, particularly among Veterans. There is a critical lack of advancement in PTSD pharmacotherapy, as illustrated by increased use of off-label medications and polypharmacy (multiple drugs used simultaneously) with limited evidence on their relative risks and benefits. Moreover, PTSD and suicidal and nonsuicidal self-harm often remain undocumented in electronic health records (EHR). There is also poor predictability of disease outcomes since there are frequent changes in pharmacological treatment and multiple modifying co-occurring conditions including depression, bipolar disorder, schizophrenia, substance use disorders, traumatic brain injury, and sleep disorders. Our long-term goal is to improve diagnostics, secondary/tertiary prevention, and treatment outcomes of PTSD and its co-occurring conditions via enhanced EHR utilization. To achieve our objectives, we will analyze EHR and administrative claims data from Veterans Health Administration (VHA) and non-VHA databases, collectively covering >1.8M patients with PTSD. Specifically, we aim to: (1) Identify undetected and uncoded co-occurring mental health phenotypes that impact PTSD outcomes using machine learning and characterize disparities in their documentation; (2) Create robust models, accounting for biases and co-occurring conditions, to identify clinical trajectories of PTSD decompensation/recovery in response to time-varying treatments; and (3) Compare risk of self-harm and hospitalization among PTSD treatments using coded and imputed phenotypes through an international network study. We will compare the effectiveness of PTSD psychotropic monotherapies, polypharmacy, and psychotherapy to guide the choice of treatment for improved patient outcomes. By enhancing and validating a positive-unlabeled machine learning approach developed by our team, we will impute unrecorded/undetected mental health conditions co-occurring with PTSD in both VHA and non-VHA databases, and characterize factors associated with documentation disparities. We will model disease trajectories with enhanced latent class / latent trajectory analysis, focusing on self-harm, substance use disorders, and psychiatric hospitalization in PTSD. Finally, we will perform the largest comparative effectiveness studies to date of PTSD treatments on >100 monotherapy and polypharmacy regimens, in addition to psychotherapy interventions, using causal models and methods for addressing biases. These studies will provide high-quality evidence on the risk of hospitalizations and suicidal acts/self-harm. Successful completion of these investigations will improve the quality of clinical psychiatric decision-making, and guide improved service delivery to the Veteran and non-Veteran populations with PTSD/TBI, and/or high risk of self-harm/suicidality.