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

项目总结/摘要 重点：研究利用EHR数据（PAR-18-929），以防止自杀相关行为（SRB） 目的：使用增强的退伍军人健康管理局（VHA）EHR数据来开发个性化的 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定 旨在未来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 自杀预防协调员关于是否住院的病人谁刚刚作出非致命的自杀企图或 将他们作为门诊病人治疗（“SPC研究”）。两者都被认为是关键决策，没有全局最优决策。 治疗路径的任何一个和很少指导如何决定之间的治疗方案。 研究设计：我们将采用前瞻性观察设计。PCP研究将基于EHR数据 2010-2016年，VHA患者约有583，000次PCP事件就诊，以获得CMD帮助。一次意外访问 定义为患者在过去12个月内未接受其他CMD治疗。五 广泛的PCP治疗选择是药物治疗，转介心理治疗，药物治疗加 心理治疗、药物治疗加基于测量的协作护理，以及转介给精神科医生。 结局将是SRB（主要结局，自杀死亡或管理记录） 在接下来的12个月内发生非致命性自杀企图）或在同一时间内因自杀倾向住院治疗 随访期（次要结局）。这些结果发生在2010-2016年的12，292次事件访问之后。 SPC研究将基于2010-2016年完成的67，196份VHA自杀行为报告， 非致命性VHA门诊自杀企图。这些病例中大约有一半住院治疗，其他人接受治疗 门诊病人在接下来的12个月里，这些病例中有19，829例发生了重复的SRB。 方法：将使用平衡基线协变量的最佳实践方法调整非随机 治疗方案之间的分配。基线协变量将包括：既往EHR数据; EHR数据可用于 重点治疗决策，包括从自然语言临床记录中提取的信息 处理;患者地址的小区域地理编码数据;来自Lenovo Nexis Social的个人级别数据 健康数据库中关于患者财务、就业、婚姻状况和刑事司法的决定因素 参与;和有关治疗临床医生和实践资源的先前实践模式的信息， 治疗设置。一个尖端的集成机器学习方法将被用来分析这些加权 数据来开发PTR。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的样本外性能。

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.