Predicting Imminent Suicidal Thoughts and Nonfatal Attempts: The Role of Complexity

Abstract

For decades, our ability to predict suicidal thoughts and behaviors (STBs) has been at near-chance levels. The objective of this study was to advance prediction by addressing two major methodological constraints pervasive in past research: (a) the reliance on long follow-ups and (b) the application of simple conceptualizations of risk. Participants were 1,021 high-risk suicidal and/or self-injuring individuals recruited worldwide. Assessments occurred at baseline and 3, 14, and 28 days after baseline using a range of implicit and self-report measures. Retention was high across all time points (> 90%). Risk algorithms were derived and compared with univariate analyses at each follow-up. Results indicated that short-term prediction alone did not improve prediction for attempts, even using commonly cited “warning signs”; however, a small set of factors did provide fair-to-good short-term prediction of ideation. Machine learning produced considerable improvements for both outcomes across follow-ups. Results underscore the importance of complexity in the conceptualization of STBs.

Suicidal Thoughts, Behaviors, and Event-Related Potentials: A Systematic Review and Meta-Analysis

“…Because our suicide-risk meta-analysis may have been driven by differences in SI, our results may suggest that differences in severity of SI can be due to disruptions in neural systems. This suggestion aligns with recent work suggesting that the number of paths leading to SI is indeterminate (Huang, Ribeiro, et al, 2020;Ribeiro et al, 2019). Another possibility is that what has been termed the general factor of psychopathology, or p factor, is driving this lack of differences (Caspi et al, 2014;Lahey et al, 2012).…”

Accurate Diagnosis of Suicide Ideation/Behavior Using Robust Ensemble Machine Learning: A University Student Population in the Middle East and North Africa (MENA) Region

“…Both studies focused on a clinical sample that is different from the target group of the current study. Another study that can be comparable to our study is Ribeiro and colleagues [ 44 ]. Although it showed a good performance (AUC: 0.9), the population was different (adults with a history of self-injury), and it did not focus on important psychological variables that have been shown to be related to suicide, such as positive mental health, post-traumatic stress disorder, and post-traumatic growth.…”

Decomposing implicit associations about life and death improves our understanding of suicidal behavior

“…Furthermore, research using the D-IAT has found that these self-identification tendencies prospectively predict suicide attempts over a six-month follow-up, above and beyond a history of a prior suicide attempt as well as both clinicians' and patients' predictions of engaging in a suicide attempt (Nock et al, 2010). Other studies have found similar prospective prediction results with reduced identification with life/increased identification with death associated with greater risk (Randall, Rowe, Dong, Nock, & Colman, 2013;Tello, Harika-Germaneau, Serra, Jaafari, & Chatard, 2019).…”