Sanz-Gomez Sergio, Alacreu-Crespo Adrián, Gourgechon-Buot Elia, Perea-Gonzalez Maria Isabel, Ordoñez-Carrasco Jorge Luis, Courtet Philippe, Giner Lucas
Department of Psychiatry, https://ror.org/03yxnpp24Universidad de Sevilla, Sevilla, Spain.
Department of Psychology and Sociology, https://ror.org/012a91z28Universidad de Zaragoza, Zaragoza, Spain.
Eur Psychiatry. 2025 Aug 11;68(1):e114. doi: 10.1192/j.eurpsy.2025.10070.
The significant heterogeneity among individuals who die by suicide complicates prevention, suggesting that a "one-size-fits-all" approach is insufficient. It is crucial to identify distinct subgroups for targeted strategies. This study aims to characterize suicide profiles based on trait impulsivity and related factors.
Data from the FRieNDS project ( Risk Factors in Suicide Deaths), a psychological autopsy study of 408 suicide deaths, were used. After determining the optimal number of clusters via stability analysis through agglomerative nesting, a final cluster analysis was performed on 391 valid suicide deaths (defined as cases with no missing data on the variables used for clustering) using -means on a lower-dimensional representation of the data encoded by an autoencoder. Key clustering variables included sex, impulsivity (Barratt Impulsivity Scale-11), aggression, intent to die, previous history of suicide attempts, history of substance abuse, psychotic and affective disorders, and the presence of a depressive episode at the time of death.
We identified three clusters: (1) Impulsive-aggressive (29.8%), characterized by high rates of Cluster B disorders, substance abuse, more stressful events, and low lethal intent; (2) depressive prior attempters (24.5%), which comprised mostly women and showed greater behavioural changes before death; and (3) non-impulsive/aggressive (45.7%), a group with no clear psychopathological profile, less healthcare contact, and minimal communicated intent to die, despite having few prior attempts.
Our study identified three suicide clusters with varying impulsivity levels, highlighting the need for tailored interventions and community-level research for better suicide prevention strategies.
自杀死亡个体之间存在显著的异质性,这使得预防工作变得复杂,表明“一刀切”的方法并不充分。识别不同的亚组以制定针对性策略至关重要。本研究旨在根据特质冲动性及相关因素对自杀概况进行特征描述。
使用了FRieNDS项目(自杀死亡风险因素)的数据,这是一项对408例自杀死亡进行的心理解剖研究。通过凝聚嵌套稳定性分析确定聚类的最佳数量后,对391例有效自杀死亡病例(定义为聚类所用变量无缺失数据的病例)进行最终聚类分析,使用自动编码器对数据进行低维表示后采用均值法。关键聚类变量包括性别、冲动性(巴拉特冲动性量表-11)、攻击性、死亡意图、既往自杀未遂史、药物滥用史、精神疾病和情感障碍以及死亡时是否存在抑郁发作。
我们识别出三个聚类:(1)冲动-攻击性(29.8%),其特征为B类障碍、药物滥用发生率高、压力事件更多且致死意图低;(2)抑郁型既往未遂者(24.5%),主要由女性组成,且在死亡前表现出更大的行为变化;(3)非冲动/攻击性(45.7%),这一组没有明确的精神病理特征,医疗接触较少,尽管既往未遂次数少,但传达的死亡意图极小。
我们的研究识别出三个冲动性水平不同的自杀聚类,强调了需要进行针对性干预以及社区层面的研究以制定更好的自杀预防策略。