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.

项目总结