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.

项目概要 创伤后应激障碍 (PTSD) 具有复杂的共存医疗状况（合并症） 并且与高自杀风险相关，特别是对于退伍军人来说，这是导致死亡的主要原因。 创伤后应激障碍 (PTSD) 药物治疗严重缺乏进展，如标签外药物使用的增加就表明了这一点 药物治疗和多种药物治疗（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给创伤后应激障碍（PTSD）管理带来了危机。此外，PTSD 及其主要 合并症 [创伤性脑损伤 (TBI) 和自杀] 在电子医疗中通常没有记录 记录（电子病历）。由于疾病的频繁变化，疾病结果的可预测性也很差。 药物治疗和多种改变合并症。我们的长期目标是改善诊断、 通过增强 EHR 来实现 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，我们将分析退伍军人的电子病历和行政索赔数据 管理 (VA) 和非 VA 数据库，共同覆盖 > 200 万 PTSD 和 > 200 万 TBI 患者。 具体来说，我们的目标是：(1) 识别 EHR 中未发现的 PTSD、TBI 和自残行为（使用机器学习） 有或没有自然语言处理）来指导卫生服务的改进。 (2) 预测 通过新的疾病轨迹模型来解释 VA 人群的 PTSD 临床过程，该模型解释了 随时间变化的治疗方法和偏差 (3) 比较 PTSD 精神药物单一疗法的有效性， 联合用药和心理治疗来指导治疗选择，以改善患者的预后。经过 为了增强和验证我们团队开发的机器学习方法，我们将估算未记录的 来自两个数据集的 PTSD、TBI 和自残，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分析来模拟疾病轨迹，重点关注自残， 物质滥用和创伤后应激障碍 (PTSD) 中的精神病住院治疗。通过本地控制方法创新，我们 将比较有和没有共病 TBI 的退伍军人患 PTSD 的风险。最后我们将进行最大规模的表演 超过 100 种单一疗法和多药疗法的 PTSD 治疗效果比较研究（迄今为止） 治疗方案加上心理治疗干预。这些研究将为以下风险提供高质量的证据： 住院治疗、药物滥用和自杀行为/自残。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口。