Analyses to reveal trajectories and early markers of imminent shifts in suicidal states

The National Institute of Mental Health (NIMH) awarded The Ohio State University a $19.5 million Cooperative Agreement on August 18, 2025, under the Mental Health Research Grants program (CFDA 93.242) to conduct a large-scale longitudinal study titled "Analyses to Reveal Trajectories and Early Markers of Imminent Shifts in Suicidal States" (IMPACT Study). The primary deliverable is a richly phenotyped dataset and predictive models identifying early warning markers and risk trajectories for suicide. The project will recruit approximately 13,500 participants aged 18–55 across varying suicide risk levels, collecting six weeks of intensive data through self-report assessments, ecological momentary assessment (EMA), cognitive and affective behavioral tasks, and passive sensing technologies. The research will generate clinically actionable strategies to identify individuals at risk of meaningful increases in suicide risk, identify behavioral markers most sensitive to detecting transitions between suicide risk states, and predict timing of risk escalation to inform clinical care decisions.

Sub-awardees including Indiana University (recruitment and consent services at the IU Psychotic Disorders Program), University of Utah (dynamical systems modeling and data analysis support), University of Michigan's Prechter Bipolar Research program (EMA data collection facilitation from a high-risk bipolar cohort), and the African American Male Wellness Agency (community recruitment outreach and consultation) will provide specialized research support, recruitment coordination, and analytical expertise across the project's four-year performance period extending through August 17, 2029.

Abstract

Research shows that nearly half of suicide decedents visit a health care provider in the weeks prior to their death. Yet, existing suicide screening approaches in healthcare settings fail to identify most patients who go on to engage in suicidal behavior (SB) or DIE by suicide. We propose a large-scale study to develop clinically actionable strategies to identify which individuals will experience clinically meaningful increases in suicide risk, what tools or behavioral markers are most sensitive to detecting transitions between suicide risk states, and when these shifts are likely to occur to inform clinical care. The proposed study builds on our team's extensive expertise in large-scale recruitment, intensive longitudinal assessment, behavioral phenotyping, clinical signatures of suicide risk, and dynamic and computationally intensive modeling approaches to risk prediction. Approximately 13,500 participants between the ages of 18 and 55 will be recruited, representing varying levels of risk for suicide. Most participants will be recruited nationally with postcard mailings to targeted ZIP-code representative areas (n=8- 10,000), and include those with a primary health care visit in the past 3 months (lowest risk category). Additional, higher-risk participants (n=4200) will be recruited based upon recent health care visits, including individuals from depression clinics, ptsd programs, psychosis and risk for psychosis clinics, sleep clinics, pain clinics, and clinics for underinsured people, to include a range of moderate and high risk groups for suicide risk. We will collect six total weeks of self-report assessments, with ecological momentary assessment, cognitive and affective behavioral tasks, and passive sensing data to develop a richly phenotyped sample for modeling variable trajectories of suicide risk over time (aim 1). We will leverage this data to predict temporal patterns that signal transitions from low to high-risk states, and vice versa, using innovative and computationally intensive dynamical systems analyses (aim 2). We will identify individual characteristics that modulate these transitions and identify the minimum data required for robust model estimates (aim 3). With the completion of this project, a novel computational approach to signaling an individual patient's current probability for transitioning to a higher or lower suicide risk state within clinically relevant timeframes (days to weeks) will be available, along with a rich cross-diagnostic dataset of cognitive, affective, and behavioral data from a nationwide sample.