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.

项目概要/摘要重点关注：利用 EHR 数据 (PAR-18-929) 预防自杀相关行为 (SRB) 的研究目标：使用增强型退伍军人健康管理局 (VHA) EHR 数据来开发个性化治疗规则 (PTR) 帮助指导信徒为精神病患者做出关键治疗决策旨在在未来 12 个月内减少 SRB。具体目标：我们将重点关注两个决定：初级保健医生关于如何治疗患者的决定向他们寻求常见精神障碍的帮助（CMD；“PCP 研究”）以及 VHA 的决定；自杀预防协调员决定是否将刚刚进行过非致命性自杀企图的患者住院或将他们视为门诊患者（“SPC 研究”），两者都被认为是关键决策，没有全局最优。两者的治疗路径以及如何决定治疗方案的指导很少。研究设计：我们将采用前瞻性观察设计，以 EHR 数据为基础。 2010 年至 2016 年期间，VHA 患者因 CMD 寻求 PCP 就诊事件约 583,000 次。将被定义为患者在过去 12 个月内未接受过其他 CMD 治疗。广泛的 PCP 治疗选择包括药物治疗、转诊心理治疗、药物治疗加心理治疗、药物治疗加上基于测量的协作护理以及转介心理治疗。结果将是 SRB（主要结果，自杀死亡或行政记录的死亡）在接下来的 12 个月内（非致命性自杀未遂）或在同一时间内因自杀而住院治疗随访期（次要结果）。这些结果发生在 2010-2016 年 12,292 次事件访问之后。 SPC 研究将基于 67,196 份 2010-2016 年 VHA 自杀行为报告。非致命性 VHA 门诊自杀未遂病例中，大约有一半被住院治疗，其余则接受治疗。在接下来的 12 个月内，其中 19,829 例门诊患者再次接受 SRB。方法：平衡基线协变量的最佳实践方法将用于调整非随机跨治疗方案的分配基线协变量将包括：先前的 EHR 数据；重点治疗决策，包括用自然语言从临床记录中提取的信息处理来自 LexisNexis Social 的患者地址的小区域地理编码数据；关于患者财务、就业、婚姻状况和刑事司法的健康数据库的决定因素参与；以及有关先前治疗实践模式和实践资源的信息将使用尖端的集成机器学习方法来分析这些加权。 2010-2016 年数据的交叉验证和 2017-2018 年数据的验证（不可用）直到研究的第三年）将用于估计 PTR 的样本外表现。