自杀意念的亚组划分：一项针对精神科住院患者的生态瞬时评估研究
Subgrouping suicidal ideations: an ecological momentary assessment study in psychiatric inpatients.
作者信息
Homan Stephanie, Roman Zachary, Ries Anja, Santhanam Prabhakaran, Michel Sofia, Bertram Anna-Marie, Klee Nina, Berther Carlo, Blaser Sarina, Gabi Marion, Homan Philipp, Scheerer Hanne, Colla Michael, Vetter Stefan, Olbrich Sebastian, Seifritz Erich, Galatzer-Levy Isaac, Kowatsch Tobias, Scholz Urte, Kleim Birgit
机构信息
Department of Adult Psychiatry and Psychotherapy, Psychiatric University Clinic Zurich and University of Zurich, Zurich, Switzerland.
Experimental Psychopathology and Psychotherapy, Department of Psychology, University of Zurich, Zurich, Switzerland.
出版信息
BMC Psychiatry. 2025 May 8;25(1):469. doi: 10.1186/s12888-025-06861-w.
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.
TRIAL REGISTRATION
10DL12_183251.
背景
自杀意念（SI）是自杀未遂最强的预测因素之一，但可靠的自杀风险预测模型仍然稀缺。一个关键挑战是自杀意念会随时间波动，这可能反映出不同的亚组，而这些亚组可能为自杀风险预测提供重要见解。本研究旨在在以往对自杀意念轨迹求平均值的方法基础上，采用一种考虑自杀意念时间特性的方法。
方法
首先，我们将纵向聚类应用于51名精神科患者（61%为女性，平均年龄 = 35.26，标准差 = 12.54）在28天内每天进行5次评估的关于自杀意念的生态瞬时评估（EMA）数据。我们使用KmlShape算法，该算法将原始自杀意念得分和测量时间点指数作为输入。其次，我们将每个识别出的亚组与已确定的自杀意念临床风险因素进行回归分析，这些因素包括自杀想法和行为史、绝望感、抑郁症诊断、焦虑症诊断以及虐待史。
结果
识别出四个具有独特自杀意念模式的不同亚组：（1）“高自杀意念，中等变异性”（平均得分高，变异性中等，最高得分高）；（2）“最低自杀意念，最低变异性”（平均得分最低，变异性最低，最高得分最低）；（3）“低自杀意念，中等变异性”（平均得分低，变异性中等，最高得分高）；（4）“最高自杀意念，最高变异性”（平均得分最高，变异性最高，最高得分最高）。此外，这些亚组与临床特征显著相关。例如，自杀意念最不严重的亚组（“最低自杀意念，最低变异性”）绝望感水平最低（β = -0.95，95%置信区间 = -1.04，-0.86），而自杀意念最严重的亚组（“最高自杀意念，最高变异性”）绝望感水平最高（β = 0.84，95%置信区间 = 0.72，0.95）。
结论
将纵向聚类应用于有自杀意念患者的生态瞬时评估数据，能够识别出具有更清晰临床特征的明确且不同的自杀意念亚组。这种方法是深入理解自杀意念的关键一步，也是加强预测和预防工作的基础。
试验注册
10DL12_183251。
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