The prediction of study-emergent suicidal ideation in bipolar disorder: a pilot study using ecological momentary assessment data.

High frequency data collection in a participant's usual environment [ecological momentary assessment (EMA)] offers a promising approach for understanding, and potentially intervening on, precursors of clinically important states and behaviors in psychiatric disorders such as suicide (1–3). An estimated 25–50% of those with bipolar disorder will attempt suicide in their lifetime (4), and evidence suggests that suicide attempts of individuals with bipolar disorder are also associated with greater lethality or suicide potential compared to unipolar depression (5). Suicidal ideation is a reliable risk factor for death by suicide and suicide attempt incidence across psychiatric disorders (6). However, the temporal course of precursors to suicidal ideation in individuals with bipolar disorder is poorly understood (7). In particular, it is unclear what symptoms or states may place persons at elevated risk for suicidal ideation and the earliest that meaningful changes in these precursors may be detected. Although some surges in suicidal ideation may be impulsive and without precedent, it is likely that, for many patients with bipolar disorder, changes in mood states or comorbidities predict later emergence of suicidal ideation (8). A goal of some recent studies employing EMA data collection methodology is to intensively assess changes in potential risk factors in psychiatric disorders to determine when and how accurately they predict suicidal ideation (9, 10). Statistical prediction models are well suited for application to intensively sampled risk factors obtained from EMA data (11–13) because of their utility in discovering patterns of temporal variation antecedent to and predictive of clinical outcomes. An important criterion for evaluating EMA-based prediction models is robust sensitivity and specificity of predictions across heterogeneous samples. Moreover, prediction models should ideally identify risk states (e.g., mood changes) in advance of the observed outcome (e.g., suicidal ideation) for the application of timely preventative interventions. The greater temporal resolution of EMA data and associated prediction models should also provide for greater accuracy and/or earlier detection than would data obtainable from routine intervals, such as in clinic visits or typical longitudinal clinical research studies. There have been a number of studies utilizing EMA data for the prediction of suicidal behaviors over the short term (9, 14–16); however, none have examined prediction of risk in individuals with bipolar disorder. Prediction of suicide risk in patients with bipolar disorder may be particularly challenging; long-term prospective studies indicate substantial heterogeneity in both symptoms (17, 18) and precursors to exacerbations (19, 20). However, negative affect appears to be a common precursor to symptom worsening in mood disorders (21), providing a rationale for using EMA negative affect ratings in statistical prediction models to detect early signs of suicide risk within bipolar disorder specifically. In the present report, we evaluated EMA-derived negative affect from a pilot study of individuals with inter-episode bipolar disorder across an eight-week assessment period. Applying statistical prediction models, we sought to use EMA data to prospectively predict study-emergent suicidal ideation prior to in-person assessments. Specifically, we employed functional linear models (FLMs) (22, 23) which utilize intensively sampled longitudinal data to generate empirically derived, updatable predictions. We hypothesized that use of individual daily affect ratings as predictors in FLMs would provide more accurate individualized predictions of suicidal ideation compared to data from more infrequently scheduled in-person assessments alone.