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.
This project aims to develop precision treatment rules for (i) primary care physicians trying to develop a treatment plan for patients seeking treatment for common mental disorders and (ii) suicide prevention coordinators trying to develop a treatment plan for a patient who just made a nonfatal suicide attempt with the goal of developing a plan that will minimize prevalence of suicide-related behaviors (either suicide deaths or nonfatal attempts) over the next 12 months. The work will be carried out in the Veterans Health Administration system, but will have implications for the broader population. The research has the potential to have great public health significance given that suicide-related behaviors (SRBs) are important public health problems, that the decisions we will study occur very often and have important effects on SRBs, that clinicians currently have no clear guidance on how to make these decisions, and that the design we propose to use has a high probability of success because of a number of important advantages over the designs used in previous studies.