自杀念头的分组:精神病医院住院患者的生态瞬间评估研究
Stephanie Homan1,2, Zachary Roman3, Anja Ries4
1Department of Adult Psychiatry and Psychotherapy, Psychiatric University Clinic Zurich and University of Zurich, Zurich, Switzerland. stephanie.homan@psychologie.uzh.ch.
BMC psychiatry
|May 9, 2025
概括
使用纵向聚类在精神病患者中识别明显的自杀念头 (SI) 模式,揭示了具有独特临床特征的子组. 这种时间方法增强了对自杀风险的理解和预测.
科学领域:
- 精神病学和心理健康 精神病学和心理健康
- 数据科学和分析数据科学和分析
- 临床心理学 临床心理学
背景情况:
- 自杀念头 (SI) 是自杀企图的关键预测因素,但缺乏准确的风险预测模型.
- 随着时间的推移,SI波动,这表明可能存在不同的患者子组.
- 以前的模型经常平均SI轨迹,可能会掩盖时间动态.
研究的目的:
- 将纵向聚类应用于生态瞬间评估 (EMA) 数据,以识别不同的SI子组.
- 检查精神病患者中SI模式的时间性质.
- 将已识别的SI子组与已确定的临床风险因素联系起来.
主要方法:
- 利用KmlShape算法对SI上的EMA数据进行纵向聚类.
- 从51名精神病患者收集了28天的每日SI评估.
- 对临床因素回归确定SI子组:自杀行为史,绝望,抑郁,焦虑和滥用史.
主要成果:
- 确定了四个不同的SI子组:"高SI,中度变化"",最低SI,最低变化"",低SI,中度变化"和"最高SI,最高变化".
- 这些子组显示出与临床特征的显著关联.
- "最低SI,最低可变性"子组的绝望度最低,而"最高SI,最高可变性"子组的绝望度最高.
结论:
- EMA数据的纵向聚类有效地识别出具有明确临床特征的不同SI子组.
- 这种时间分析方法对于更深入地了解SI至关重要.
- 这些发现为改进自杀风险预测和预防策略提供了基础.
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