Subgrouping suicidal ideations: an ecological momentary assessment study in psychiatric inpatients

Background Suicidal ideation (SI) is one of the strongest predictors of suicide attempts, yet reliable prediction models for suicide risk remain scarce. A key challenge is that SI can fluctuate over time, potentially reflecting different subgroups that may offer important insights for suicide risk prediction. This study aims to build upon previous approaches that averaged SI trajectories by adopting a method that respects the temporal nature of SI. Methods First, we applied longitudinal clustering to ecological momentary assessment (EMA) data on SI, with five daily assessments over 28 days from 51 psychiatric patients (61% female, mean age = 35.26, SD = 12.54). We used the KmlShape algorithm, which takes raw SI scores and the measurement occasion index as input. Second, we regressed each identified subgroup against established clinical risk factors for SI, including a history of suicidal thoughts and behaviors, hopelessness, depression diagnosis, anxiety disorder diagnosis, and history of abuse. Results Four distinct subgroups with unique SI patterns were identified: (1) “High SI, moderate variability” (high mean, medium variability, high maximum); (2) “Lowest SI, lowest variability” (lowest mean, lowest variability, lowest maximum); (3) “Low SI, moderate variability” (low mean, medium variability, high maximum); and (4) “Highest SI, highest variability” (highest mean, highest variability, highest maximum). Furthermore, these subgroups were significantly associated with clinical characteristics. For instance, the subgroup with the least severe SI (“lowest SI, lowest variability”) showed the lowest levels of hopelessness (beta = -0.95, 95% CI = -1.04, -0.86), whereas the subgroup with the most severe SI (“highest SI, highest variability”) exhibited the highest levels of hopelessness (beta = 0.84, 95% CI = 0.72, 0.95). Conclusion Applying longitudinal clustering to EMA data from patients with SI enables the identification of well-defined and distinct SI subgroups with clearer clinical characteristics. This approach is a crucial step toward a deeper understanding of SI and serves as a foundation for enhancing prediction and prevention efforts.