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

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

• 批准号: 10088135
• 负责人: Christophe G. Lambert
• 金额: $ 77.62万
• 依托单位: UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-01 至 2022-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10088135
• 关键词: Address; Affect; Benefits and Risks; Bipolar Disorder; Caring; Cause of Death; Characteristics; Clinical; Clinical Research; Code; Complex; Coupled; Data; Data Set; Databases; Decision Making; Detection; Diagnostic; Disease; Disease Management; Disease Outcome; Disease Progression; Disease model; Documentation; Drug Combinations; Effectiveness; Electronic Health Record; Event; Fostering; General Population; Glean; Goals; Health Services; Healthcare; Hospitalization; Interdisciplinary Study; Intervention; Investigation; Label; Longterm Follow-up; Machine Learning; Major Mental Illness; Maps; Mediating; Medical; Mental disorders; Mentally Ill Persons; Methodology; Methods; Military Personnel; Modeling; Natural Language Processing; Observational Study; Outcome; Patient Care; Patient-Focused Outcomes; Patients; Pharmaceutical Preparations; Pharmacological Treatment; Pharmacotherapy; Phenotype; Polypharmacy; Population; Positioning Attribute; Post-Traumatic Stress Disorders; Provider; Psychiatry; Psychotherapy; Regimen; Relative Risks; Reporting; Research Design; Residual state; Risk; Risk Estimate; Safety; Secondary Prevention; Self-Injurious Behavior; Source; Suicide; Symptoms; Time; Traumatic Brain Injury; Traumatic Stress Disorders; Treatment Protocols; Treatment outcome; United States Department of Veterans Affairs; Veterans; analysis pipeline; cohort; comorbidity; comparative; comparative effectiveness study; compare effectiveness; data modeling; effective therapy; experience; health record; high risk; improved; improved outcome; innovation; language processing; multiple drug use; natural language; novel; off-label drug; outcome forecast; prevent; psychosocial; service delivery; sociodemographic factors; stress related disorder; substance misuse; suicidal act; suicidal risk; tertiary prevention; therapy design; time use; treatment choice; treatment comparison

PROJECT SUMMARY Post-traumatic stress disorder (PTSD) has complex profiles of co-occurring medical conditions (comorbidities) and is associated with high risk of suicide, particularly among Veterans, in which it is a leading cause of death. 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). The consequent limited evidence on the relative risks and benefits of treatments creates a crisis in PTSD management. Moreover, PTSD and its major comorbidities [traumatic brain injury (TBI) and suicidality] 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 comorbidities. Our long-term goal is to improve diagnostics, secondary/tertiary prevention, and treatment outcomes of PTSD and its comorbidities via enhanced EHR utilization. To achieve our objectives, we will analyze EHR and administrative claims data from Veterans Administration (VA) and non-VA databases, collectively covering >2M PTSD and >2M TBI patients. Specifically, we aim to: (1) Identify undetected PTSD, TBI, and self-harm from EHRs (using machine learning with and without natural language language processing) to guide health service improvements. (2) Predict PTSD clinical course in the VA population through novel modeling of disease trajectories that account for time-varying treatments and biases (3) 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 machine learning approach developed by our team, we will impute unrecorded PTSD, TBI, and self-harm from both datasets, and characterize factors associated with documentation disparities. We will model diseases trajectories with enhanced latent class analysis, focusing on self-harm, substance misuse, and psychiatric hospitalization in PTSD. With Local Control methodology innovations, we will compare the risk of PTSD in veterans with and without comorbid TBI. Finally, we will perform the largest comparative effectiveness studies (to date) of PTSD treatments on >100 monotherapy and polypharmacy regimens plus psychotherapy interventions. These studies will provide high-quality evidence on the risk of hospitalizations, substance misuse, and suicidal acts/self-harm. Successful completion of these investigations will improve the quality of decision making for providers and patients, and guide improved service delivery to the population of veterans and non-veterans with PTSD/TBI, and/or high risk of suicide.

项目摘要 创伤后应激障碍（PTSD）具有同时发生的医疗状况（合并症）的复杂轮廓 并且与自杀的高风险有关，尤其是在退伍军人中，这是死亡的主要原因。 PTSD药物疗法缺乏急需的进步，如标签外的增加所示 药物和多药（同时使用多种药物）。随之而来的有限证据 治疗的相对风险和益处在PTSD管理中造成了危机。此外，PTSD及其专业 合并症[创伤性脑损伤（TBI）和自杀性]通常在电子健康中仍然没有记载 记录（EHR）。疾病结局的可预测性也很差，因为 药理治疗和多种修饰合并症。我们的长期目标是改善诊断， 通过增强的EHR，PTSD及其合并症的次要/第三级预防以及其合并症的治疗结果 利用率。为了实现我们的目标，我们将分析EHR和退伍军人的行政索赔数据 给药（VA）和非VA数据库，共同覆盖了> 2M PTSD和> 2M TBI患者。 具体而言，我们的目标是：（1）从EHRS中确定未发现的PTSD，TBI和自我伤害（使用机器学习 有或没有自然语言处理），以指导卫生服务改进。 （2）预测 VA人群中的PTSD临床课程通过疾病轨迹的新型建模来解释 时间变化的治疗和偏见（3）比较PTSD精神单位疗法的有效性， 多药和心理治疗，以指导选择改善患者预后的治疗方法。经过 加强和验证我们团队开发的机器学习方法，我们将归为未记录 来自两个数据集的PTSD，TBI和自我伤害，并表征与文档相关的因素 差异。我们将通过增强的潜在类别分析对疾病轨迹进行建模，重点是自我伤害， PTSD中的药物滥用和精神病住院。通过本地控制方法创新，我们 将比较有和没有合并症的退伍军人的PTSD风险。最后，我们将执行最大的 PTSD治疗> 100个单药治疗和多药治疗的比较有效性研究（迄今为止） 方案和心理治疗干预措施。这些研究将提供有关有风险的高质量证据 住院，滥用药物和自杀行为/自我伤害。这些调查成功完成 将提高提供者和患者决策的质量，并指导服务提供的服务提供 具有PTSD/TBI的退伍军人和非退伍军人的人口和/或自杀的高风险。