Please join us in congratulating Dr. Xiaoxuan Cai who was recently awarded a National Institute of Mental Health (NIMH) grant! This project aims to analyze SPI usage and how it can be optimized for suicide presentations. 

Title: Optimizing Brief Coping Skills Interventions for Suicide Prevention: Leveraging Technology and Novel Statistical Models for Precision Mental Health

Abstract: Suicide is a leading cause of death in the United States. Patients who have recently been discharged from an inpatient psychiatric hospitalization are at particularly elevated risk for suicide. Although safety planning interventions (SPIs) are provided to many patients to alleviate risk, the success of these approaches relies on patients' capability to remember and apply these strategies amidst severe emotional distress. Unfortunately, a large proportion of patients do not use their plans in the post-discharge period, and many that do use their SPIs report that they do not find them to be helpful or go on to make a suicide attempt. The precise reasons for these findings are unclear. A critical knowledge gap lies in understanding the individual-specific triggers, contexts, and timing of suicide risk states that signify SPI use may be clinically indicated, and the conditions that can potentiate or inhibit the efficacy of SPIs. That is, a) whether patients are successful at identifying changes in suicide risk that would necessitate the use of SPIs or b) whether clinicians are recommending the use of these plans in a manner that supports suicide risk reduction, is unclear. To address these critical knowledge gaps, we will follow 240 patients at high-risk for suicide who have completed a SPIs in a naturalistic observational study. We will collect multimethod data from a combination of ecological momentary assessment, wearables, and periodic clinician ratings of risk together with advanced analytic approaches to intensively characterize the contexts within which patients are most likely to use and benefit from their SPIs. We will leverage these data in novel, Bayesian change point detection models to identify clinically dissociable suicide risk states that indicate worsening in momentary risk, even the absence of explicit information about suicidal ideation, to help clinicians refine warning signs to help patients recognize when SPI use is clinically indicated. Additional models will examine whether factors such as inflection points in risk, patient characteristics, and patterns of SPI usage impact the degree to which patients benefit from plan use. Our proposal aligns precisely with the NIH's focus on precision medicine, representing a pioneering application of technological assessment and computational modeling in suicide prevention research. In the short-term, our primary objective is to equip clinicians with the information needed to maximize the impact of SPIs. In the longer-term, our findings can inform the development of digital health strategies that use SPIs to effectively address risk in real-world settings, thereby contributing to a pragmatic, data-driven solution to the public health emergency of suicide prevention. The ramifications of this work are vast and meaningful: improving clinical practice, enhancing the delivery of SPIs, including eventually through digital health strategies, and ultimately, driving down suicide rates. This project bears the potential to effect monumental, life-preserving changes in the realm of suicide prevention, showcasing the transformative power of integrating cutting-edge technology into mental health care. 

Primary Investigator: MELANIE BOZZAY, Assistant Professor, Psychiatry and Behavioral Health, The Ohio State University