Mixed methods examination of warning signs within 24 hours of suicide attempt in hospitalized adults

Suicide is a leading cause of death, and individuals who attempt suicide and receive hospital treatment are at
high risk for suicide within a year. The identification and validation of warning signs (WS) for suicidal behavior –
near-term risk factors– is a national priority. Determining if an individual is at risk now drives high-impact
decisions in acute care settings within emergency departments (e.g., whether to admit a patient) and crisis
lines (e.g., whether to send a mobile crisis team). Yet, there has been little research on ‘when’ individuals are
at near-term risk or WS (i.e., within minutes, hours, a day) for suicide attempts. This clinically- and
theoretically-driven study addresses critical gaps in our understanding of WS for suicide attempts. We seek to
a) discover novel warning signs candidates for suicide attempts, b) validate, and generate the first risk
estimates, for these candidates and WS put forward in recent theoretical formulations, c) compare risk-
estimates of WS to determine if those currently prioritized in risk assessments in acute care settings is
warranted, and d) develop new algorithms to detect linguistic signals of specific WS content in patients'
narrative interviews. We propose a multi-site mixed-methods study that will recruit 400 adults currently
hospitalized for a suicide attempt in two academic medical centers in the Upper Midwest. Subjects will be
asked to tell the narrative story of their attempt in their own words, and also undergo a detailed semi-structured
interview to obtain systematic data about hypothesized WS on the day of the attempt and the day prior. We will
discover potential novel WS candidates using subjects’ narrative stories coded by experts using qualitative
methodology (Aim 1). Next, we will validate a priori and novel candidate WS (Aim 2). Case-crossover
methodology will be used, a within-subjects design that uses subjects as their own control. The semi-structured
interview data are analyzed through comparisons of the presence/intensity of hypothesized WS on the day of
the attempt (high-risk case period) to the day prior (lower risk control period). Finally, we will develop and test
an algorithm to detect linguistic signals of specific WS content (Exploratory Aim 3). Natural language
processing and deep learning models of language will be used to detect WS within the narratives. WS for
suicide attempts are extraordinarily difficult to study due the practical challenge of examining the hours
preceding an act of suicide. The project uses innovative qualitative and quantitative methods to address this
challenge in a rigorous fashion. The study is designed to provide scientifically grounded WS to inform clinical
decision-making, patient/family education, and automated risk identification.

Grant Number: 5R01MH133587-03
NIH Institute/Center: NIH
Principal Investigator: COURTNEY BAGGE