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

从国家医疗保健数据库中获取高质量证据，以改善 PTSD 的自杀检测和治疗结果

• 批准号: 10587966
• 负责人: Christophe G. Lambert
• 金额: $ 74.43万
• 依托单位: UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-12-23 至 2026-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10587966
• 关键词: Accounting; Address; Affect; Aftercare; Behavior; Benefits and Risks; Bipolar Disorder; Cause of Death; Clinical; Code; Comparative Effectiveness Research; Complex; Data; Data Science; Data Set; Databases; Decision Making; Department of Defense; Detection; Diagnosis; Diagnostic; Disease; Disease Outcome; Disease Progression; Disease model; Disparity; Documentation; Drug Combinations; Electronic Health Record; Ethnic Origin; Fostering; Funding; General Population; Goals; Healthcare; Hospitalization; Human Resources; Informatics; International; Intervention; Investigation; Label; Learning; Machine Learning; Major Depressive Disorder; Medical; Mental Depression; Mental Health; Mental disorders; Methods; Military Personnel; Modeling; Outcome; Patient Care; Patient-Focused Outcomes; Patients; Persons; Pharmaceutical Preparations; Pharmacological Treatment; Pharmacotherapy; Phenotype; Polypharmacy; Population; Positioning Attribute; Post-Traumatic Stress Disorders; Prognosis; Protocols documentation; Psychotherapy; Race; Recovery; Regimen; Relative Risks; Research; Research Design; Resources; Risk; Risk Assessment; Schizophrenia; Self-Injurious Behavior; Sleep Disorders; Socioeconomic Factors; Substance Use Disorder; Survival Analysis; Thinking; Time; Traumatic Brain Injury; Treatment Protocols; Treatment outcome; United States Department of Veterans Affairs; Veterans; Veterans Health Administration; causal model; comorbidity; comparative effectiveness; comparative effectiveness study; compare effectiveness; effective therapy; experience; health care disparity; health data; health disparity; health record; high risk; improved; learning strategy; model development; multidisciplinary; multiple drug use; neglect; novel; off-label drug; psychosocial; research study; response; service delivery; sex disparity; sociodemographic factors; success; suicidal; suicidal act; suicide rate; symptomatic improvement; tertiary prevention; therapy design; trauma exposure; treatment choice; treatment comparison

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.

项目概要 创伤后应激障碍 (PTSD) 通常具有复杂的并发医疗状况，并且 与自我伤害的高风险有关，包括自杀，这是死亡的主要原因，特别是 在退伍军人中。 PTSD 药物治疗严重缺乏进展，如增加 使用标签外药物和多种药物（同时使用多种药物），但证据有限 他们的相对风险和收益。此外，创伤后应激障碍（PTSD）以及自杀性和非自杀性自残往往仍然存在 未记录在电子健康记录 (EHR) 中。由于疾病结果的可预测性也很差 药物治疗经常发生变化，并且同时发生多种情况 包括抑郁症、双向情感障碍、精神分裂症、物质使用障碍、创伤性脑损伤和 睡眠障碍。我们的长期目标是改善诊断、二级/三级预防和治疗 通过加强 EHR 的利用来了解 PTSD 及其并发病症的结果。为了实现我们的目标，我们 将分析来自退伍军人健康管理局 (VHA) 和非 VHA 的 EHR 和行政索赔数据 数据库，总共覆盖超过 180 万 PTSD 患者。具体来说，我们的目标是：（1）识别未被发现和 使用机器学习和技术来分析影响 PTSD 结果的未编码的同时发生的心理健康表型 描述其文件中的差异； (2) 创建稳健的模型，考虑偏差和 共同发生的情况，以确定 PTSD 失代偿/恢复的临床轨迹，以应对 随时间变化的治疗； (3) 使用以下方法比较 PTSD 治疗中自残和住院的风险 通过国际网络研究对表型进行编码和估算。我们将比较效果 创伤后应激障碍 (PTSD) 精神治疗单一疗法、多药治疗和心理治疗，以指导治疗选择 改善患者治疗效果。通过增强和验证积极的无标签机器学习方法 由我们的团队开发，我们将估算与以下同时发生的未记录/未发现的心理健康状况 VHA 和非 VHA 数据库中的 PTSD，以及与文档相关的特征因素 差异。我们将通过增强的潜在类别/潜在轨迹分析来模拟疾病轨迹，重点关注 关于创伤后应激障碍 (PTSD) 患者的自残、物质使用障碍和精神病住院治疗。最后，我们将执行 迄今为止针对超过 100 种单一疗法和 PTSD 治疗的最大比较有效性研究 除了心理治疗干预外，还使用因果模型和方法进行多种药物治疗 解决偏见。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，并指导改善向退伍军人和非退伍军人提供的服务 PTSD/TBI，和/或自残/自杀的高风险。