Individual level prediction of emerging suicide events in the pharmacologic treatment of bipolar disorder

Abstract

AbstractBackgroundPatients with bipolar disorder have a high lifetime risk of suicide. Predicting, preventing and managing suicidal behavior are major goals in clinical practice. Changes in suicidal thoughts and behavior are common in the course of treatment of bipolar disorder.MethodsUsing a dataset from a randomized clinical trial of bipolar disorder treatment (N=98), we tested predictors of future suicidal behavior identified through a review of literature and applied marginal variable selection and machine learning methods. The performance of the models was assessed using the optimism-adjusted C statistic.ResultsNumber of prior hospitalizations, number of prior suicide attempts, current employment status and Hamilton Depression Scale were identified as predictors and a simple logistic regression model was constructed. This model was compared with a model incorporating interactions with treatment group assignment, and more complex variable selection methods (LASSO and Survival Trees). The best performing models had average optimism-adjusted C-statistics of 0.67 (main effects only) and 0.69 (Survival Trees). Incorporating medication group did not improve prediction performance of the models.ConclusionsThese results suggest that models with a few predictors may yield a clinically meaningful way to stratify risk of emerging suicide events in patients who are undergoing pharmacologic treatment for bipolar disorder.Significance StatementThis study aims to find out whether suicide events that occur during the pharmacological treatment of bipolar disorder, a severe psychiatric disorder that is highly associated with suicide behavior, can be predicted. Using existing methods, we developed and compared several predictive models. We showed that these models performed similarly to predictive models of other outcomes, such as treatment efficacy, in unipolar and bipolar depression. This suggests that suicide events during bipolar disorder may be a feasible target for individualized interventions in the future.