Clinical Correlates of Suicidal Ideation in Adults With Obsessive-Compulsive Disorder: An Interpretable Machine Learning Study
Year: 2026
Type: preprint
Study design: Observational Study
Abstract: Introduction Depression accounts for much of the association between obsessive-compulsive disorder and suicidal ideation in clinical samples, but whether other features carry independent information is unclear. We examined what non-depression features reveal about suicidal ideation. Methods Adults with obsessive-compulsive disorder ( N = 231) completed measures of quality of life, anxiety, physical symptoms, obsessive-compulsive symptoms and traits, and personality. Elastic net, random forest, and explainable boosting machines identified suicidal ideation (Beck Depression Inventory-II item 9) without depression measures in nested cross-validation. Secondary analyses compared these features with depression severity. Results Sixty-six participants (28.6%) endorsed suicidal ideation; 61 of these (92.4%) reported thoughts without intent to act. All models showed similar discrimination (area under the curve = .77–.78), as did depression severity alone (.77). Adding the features to depression severity yielded a small estimated gain (difference = .02, 95% confidence interval: −.03 to .06), and flexible models offered no reliable advantage. Most features, including trait anxiety and obsessive-compulsive symptom severity, were associated with suicidal ideation on their own, but these associations attenuated under mutual adjustment. Lower quality of life remained the clearest correlate. Conclusion Non-depression features identified suicidal ideation about as well as depression severity, consistent with substantial shared, distress-related information. Clinicians should ask about suicidal ideation directly, whatever a patient’s depressive symptoms, and consider overall distress in case formulation.
Source: medRxiv
Institution: Yale University
Language: English
Subfield: Clinical Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7163338156