Suppr超能文献

一项针对在急诊科接受治疗的精神健康障碍患者短期非致命性自杀和自我伤害事件预测因素的队列研究。

A cohort study of predictors of short-term nonfatal suicidal and self-harm events among individuals with mental health disorders treated in the emergency department.

作者信息

Marcus Steven C, Cullen Sara Wiesel, Schmutte Timothy, Xie Ming, Liu Tony, Ungar Lyle H, Cardamone Nicholas C, Williams Nathaniel J, Olfson Mark

机构信息

School of Social Policy & Practice, University of Pennsylvania, Philadelphia, PA, USA.

School of Social Policy & Practice, University of Pennsylvania, Philadelphia, PA, USA.

出版信息

J Psychiatr Res. 2025 Jun;186:458-468. doi: 10.1016/j.jpsychires.2025.04.035. Epub 2025 Apr 22.

Abstract

BACKGROUND

Patients presenting to emergency departments (EDs) for mental health problems have an elevated short-term risk of repeat ED visits, subsequent hospitalization, and suicide.

OBJECTIVE

Use health records to identify predictors of nonfatal suicidal or self-harm events following emergency department visits of individuals with mental health disorders.

METHODS

Electronic health record data from 2015 to 2022 were used to identify ED visits with mental health diagnoses for individuals 10 and older; extract 408 potential predictors including demographic, historical and baseline clinical characteristics from structured and unstructured data; and subsequent suicidal and self-harm events. We constructed a series of progressively additive logistic regression and gradient tree boosting (GTB) models to evaluate how groups of clinical features influenced likelihood of a nonfatal event 180-days after an ED visit.

RESULTS

Records identified 2,445,597 ED episodes and 176,000 subsequent suicidal or self-harm events within 180-days. Individuals experiencing an event relative to those without an event were less likely to be > 65 (3.6 % vs 16.7 %; h = 0.46), more likely to be male (55.7 % vs 40.8 %; h = 0.30) and covered by Medicaid (61.5 % vs 42.2 %, h = 0.39). The final model with 408 clinical features resulted in an AUC of 0.851 (logistic regression) and 0.863 (GTB). Diagnoses of bulimia nervosa (OR = 1.84, p < .0001) and cutting (OR = 2.62, p < .0001) were most highly associated with any subsequent event and suicidal self-harm, respectively.

CONCLUSION

Machine learning algorithms effectively predicted nonfatal suicide-related events within six months following ED visits among individuals with mental health disorders highlighting the importance of suicide symptom focused assessment and prevention efforts during routine emergency mental healthcare, particularly for patients with bulimia nervosa.

摘要

背景

因心理健康问题前往急诊科就诊的患者在短期内再次前往急诊科、随后住院以及自杀的风险会升高。

目的

利用健康记录确定心理健康障碍患者在急诊科就诊后非致命性自杀或自我伤害事件的预测因素。

方法

使用2015年至2022年的电子健康记录数据来识别10岁及以上有心理健康诊断的急诊科就诊情况;从结构化和非结构化数据中提取408个潜在预测因素,包括人口统计学、病史和基线临床特征;以及随后的自杀和自我伤害事件。我们构建了一系列逐步累加的逻辑回归和梯度树提升(GTB)模型,以评估临床特征组如何影响急诊科就诊180天后非致命事件的可能性。

结果

记录识别出2445597次急诊科就诊事件以及180天内176000次随后的自杀或自我伤害事件。与未发生事件的个体相比,发生事件的个体年龄大于65岁的可能性较小(3.6%对16.7%;h=0.46),男性的可能性更大(55.7%对40.8%;h=0.30),且由医疗补助计划承保的可能性更大(61.5%对42.2%,h=0.39)。包含408个临床特征的最终模型的曲线下面积(AUC)为0.851(逻辑回归)和0.863(GTB)。神经性贪食症诊断(OR=1.84,p<.0001)和割伤(OR=2.62,p<.0001)分别与任何随后事件和自杀性自我伤害的关联性最高。

结论

机器学习算法有效地预测了心理健康障碍患者在急诊科就诊后六个月内的非致命性自杀相关事件,突出了在常规紧急心理医疗保健期间,尤其是对神经性贪食症患者,以自杀症状为重点的评估和预防工作的重要性。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验