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）具有复杂的共同发生的医疗条件（合并症） 并且与自杀的高风险有关，特别是在退伍军人中，它是死亡的主要原因。 PTSD药物治疗严重缺乏进展，如标签外药物的使用增加所示。 药物和多药疗法（同时使用多种药物）。因此，有限的证据 治疗的相对风险和收益在PTSD管理中产生了危机。此外，PTSD及其主要 合并症[创伤性脑损伤（TBI）和自杀倾向]在电子健康中通常没有记录 记录（EHR）。疾病结果的可预测性也很差，因为疾病结果的变化很频繁。 药物治疗和多种改变合并症。我们的长期目标是改进诊断， 通过增强EHR的PTSD及其合并症的二级/三级预防和治疗结果 利用率为了实现我们的目标，我们将分析来自退伍军人的EHR和行政索赔数据 管理（VA）和非VA数据库，共同涵盖> 2 M PTSD和> 2 M TBI患者。 具体来说，我们的目标是：（1）从EHR中识别未被发现的PTSD，TBI和自我伤害（使用机器学习） 使用和不使用自然语言处理）来指导卫生服务的改进。(2)预测 通过对疾病轨迹的新建模，在VA人群中进行PTSD临床过程， 随时间变化的治疗和偏差（3）比较PTSD精神药物单一治疗的有效性， 综合用药和心理治疗，以指导治疗的选择，改善患者的结果。通过 增强和验证我们团队开发的机器学习方法，我们将把未记录的 PTSD、TBI和来自两个数据集的自我伤害，并表征与文件相关的因素 差距。我们将通过增强的潜在类别分析来模拟疾病轨迹，重点是自我伤害， 物质滥用和创伤后应激障碍的精神病住院治疗。通过本地控制方法的创新，我们 将比较有和没有合并创伤性脑损伤的退伍军人患创伤后应激障碍的风险。最后，我们将执行最大的 PTSD治疗的比较有效性研究（迄今为止）>100单药治疗和多药治疗 方案加心理治疗干预。这些研究将提供高质量的证据， 住院、药物滥用和自杀行为/自我伤害。顺利完成这些调查 将提高提供者和患者的决策质量，并指导改善服务提供， 患有PTSD/TBI和/或高自杀风险的退伍军人和非退伍军人人群。