Leveraging EHR data to evaluate key treatment decisions to prevent suicide-related behaviors

利用 EHR 数据评估关键治疗决策，以预防自杀相关行为

基本信息

批准号：
10311082
负责人：
RONALD C KESSLER
金额：
$ 75.04万
依托单位：
HARVARD MEDICAL SCHOOL
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-01-01 至 2023-10-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10311082
关键词：
Acute Address Ambulatory Care Area Behavior Behavior Therapy Case Management Cessation of life Characteristics Child Rearing Clinic Clinical Code Combined Modality Therapy Criminal Justice Data Data Sources Databases Death Records Decision Making Electronic Health Record Emergency Department patient Employment Equilibrium Feeling suicidal Goals Health Personnel Homelessness Hospitalization Individual Inpatients Intake Integrated Health Care Systems Link Marital Status Measurement Mental disorders Mentally Ill Persons Methods Modeling Modernization Natural Language Processing Observational Study Outcome Outpatients Patients Pattern Performance Pharmacotherapy Population Precision therapeutics Prevalence Primary Care Physician Probability Psychiatrist Psychotherapy Public Health Randomized Reaction Records Reporting Research Research Design Resources Risk Sampling Statistical Methods Structure Suicide Suicide attempt Suicide prevention System Trauma Validation Veterans Health Administration Visit Work base care providers case-by-case basis clinical practice collaborative care common treatment comparative treatment completed suicide design follow-up high risk ideation indexing machine learning method optimal treatments personalized medicine primary outcome prospective reducing suicide response secondary outcome social health determinants success suicidal behavior suicidal morbidity suicidal patient suicide rate treatment effect treatment planning treatment trial unethical virtual

项目摘要

PROJECT SUMMARY/ABSTRACT Key focus: Research leveraging EHR data (PAR-18-929) to prevent suicide-related behaviors (SRBs) Objectives: To use augmented Veterans Health Administration (VHA) EHR data to develop Personalized Treatment Rules (PTRs) to help guide clinicians in making key treatment decisions for mentally ill patients aimed at reducing SRBs over the next 12 months. Specific aims: We will focus on two decisions: the decision of primary care physicians on how to treat patients coming to them for help with common mental disorders (CMD; “the PCP study”); and the decision of VHA Suicide Prevention Coordinators on whether to hospitalize patients who just made nonfatal suicide attempts or treat them as outpatients (“the SPC study”). Both are recognized as critical decisions, with no globally optimal treatment path for either and little guidance on how to decide among the treatment options. Research design: We will use a prospective observational design. The PCP study will be based on EHR data for the roughly 583,000 incident PCP visits of VHA patients for help with a CMD in 2010-2016. An incident visit will be defined as where the patient had not received other CMD treatment in the prior 12 months. The five broad PCP treatment options are pharmacotherapy, referral to psychotherapy, pharmacotherapy plus psychotherapy, pharmacotherapy plus measurement based collaborative care, and referral to a psychiatrist. The outcomes will be either an SRB (the primary outcome, either suicide death or administratively-recorded nonfatal suicide attempt) over the next 12 months or psychiatric hospitalization with suicidality over the same follow-up period (the secondary outcome). These outcomes occurred after 12,292 2010-2016 incident visits. The SPC study will be based on the 67,196 2010-2016 VHA Suicide Behavior Reports completed after a nonfatal VHA outpatient suicide attempt. Roughly half of these cases were hospitalized and the others treated as outpatients. A repeat SRB occurred over the next 12 months for 19,829 of these cases. Methods: A best-practice method of balancing baseline covariates will be used to adjust for nonrandom assignment across treatment options. Baseline covariates will include: prior EHR data; EHR data available for the focal treatment decision, including information abstracted from clinical notes with natural language processing; small-area geocode data for patient addresses; individual-level data from the LexisNexis Social Determinants of Health Database on patient finances, employment, marital status, and criminal justice involvement; and information about prior practice patterns of treating clinicians and practices-resources of treatment settings. A cutting-edge ensemble machine learning method will be used to analyze these weighted data to develop PTRs. Cross-validation in the 2010-2016 data and validation in 2017-2018 data (not available until the third year of the study) will be used to estimate out-of-sample performance of the PTRs.

项目摘要/摘要关键重点：利用EHR数据（PAR-18-929）的研究以防止自杀相关行为（SRB）目标：使用增强退伍军人卫生管理局（VHA）EHR数据开发个性化治疗规则（PTR），以帮助指导临床医生为精神病患者做出关键的治疗决策旨在在接下来的12个月内减少SRB。具体目的：我们将重点介绍两个决定：初级保健医生关于如何治疗患者的决定向他们寻求共同精神疾病的帮助（CMD；“ PCP研究”）；和VHA的决定预防预防协调员是为了使刚刚进行非致命自杀企图的患者住院还是将它们视为门诊病人（“ SPC研究”）。两者都被认为是关键决策，没有全球最佳的决定关于如何在治疗方案中决定的治疗路径，几乎没有指导。研究设计：我们将使用前瞻性观察设计。 PCP研究将基于EHR数据对于VHA患者大约583,000例PCP访问，在2010 - 2016年间接受CMD的帮助。事件访问将定义为患者在前12个月内未接受其他CMD治疗的地方。五 PCP广泛的治疗选择是药物治疗，转诊至心理治疗，药物治疗Plus 心理治疗，药物治疗以及基于测量的协作护理以及转诊至精神科医生。结果要么是SRB（主要结果，自杀死亡或行政录制在接下来的12个月中，非致命自杀企图）或自杀的精神病医院随访期（次要结果）。这些结果发生在2010-2016 2016年的12292年发生事件访问之后。 SPC研究将基于67,196 2010-2016 VHA自杀行为报告，非致命VHA门诊自杀企图。这些病例中的大约一半是住院的，其他病例也接受了治疗作为门诊病人。在接下来的12个月中，发生了重复的SRB，其中有19,829个病例发生。方法：平衡基线协变量的最佳实践方法将用于调整非机车跨治疗方案分配。基线协变量将包括：先前的EHR数据； EHR数据可用于焦点治疗决策，包括用自然语言从临床笔记中抽象的信息加工;用于患者地址的小区域地理码数据；来自Lexisnexis社会的个人级别数据卫生数据库的决定因素关于患者财务，就业，婚姻状况和刑事司法参与；以及有关治疗临床医生和实践资源的先前实践模式的信息治疗环境。尖端的合奏机器学习方法将用于分析这些加权数据开发PTR。 2017 - 2018年2017 - 2018年数据的跨验证数据和验证（不可用直到研究的第三年）将用于估计PTR的样本外部表现。