Machine learning-based prediction of suicide risk: A long-term OCD cohort follow-up.

BACKGROUND

Obsessive Compulsive Disorder (OCD) is associated with an increased suicide risk in comparison with general population. Among the suicide risk factors we can highlight psychiatric comorbidities, childhood trauma, obsessive-compulsive severity and a combination of other risk factors related to social and family environment. This study aims to develop and validate a reliable machine learning algorithm to predict suicide risk in OCD patients, based on a combination of clinical and sociodemographic variables.

METHOD: A cohort of 199 OCD patients was followed for an average of 17.8 years (follow-up time range of between 2-28 years) in an OCD-specialized unit. Suicide-related behaviors were documented in the medical records of each participant at the time of occurrence. For the present study, a specialized psychiatrist systematically reviewed all clinical records to extract detailed information on the type of suicide-related behaviors experienced by each participant. Clinical data, including comorbidities, Y-BOCS and CTQ, were collected and used to train supervised machine learning models.

RESULTS

The best-performing model included three predictive variables: family history of suicide, affective comorbidities, and substance use. This model achieved a sensitivity of 71.4%, specificity of 74.4%, F1-score=62.7% and area under ROC curve of 0.80, demonstrating moderate predictive capability. OCD severity and childhood trauma did not significantly enhance prediction performance.

CONCLUSION

This study highlights the potential of supervised machine learning in identifying suicide risk in OCD patients based on commonly-collected clinical variables. The presence of any of the three predictors that conform the best-performing model (family history of suicide, affective comorbidities, and substance use) is sufficient to detect the presence of suicide-behavior risk. Those predictors are routinely assessed in clinical practice, making this model a feasible instrument for early risk detection. Further research with larger and more diverse cohorts is needed to refine predictive accuracy and integrate additional biomarkers for improved suicide risk stratification.