Mental Health Informatics and Data Science Hub, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles.
Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles.
JAMA Netw Open. 2024 Oct 1;7(10):e2442091. doi: 10.1001/jamanetworkopen.2024.42091.
Suicide is a leading cause of death among young people. Accurate detection of self-injurious thoughts and behaviors (SITB) underpins equity in youth suicide prevention.
To compare methods of detecting SITB using structured electronic health information and measure algorithmic performance across demographics.
DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study used medical records among youths aged 6 to 17 years with at least 1 mental health-related emergency department (ED) visit in 2017 to 2019 to an academic health system in Southern California serving 787 000 unique individuals each year. Analyses were conducted between January and September 2023.
Multiexpert electronic health record review ascertained the presence of SITB using the Columbia Classification Algorithm of Suicide Assessment. Random forest classifiers with nested cross-validation were developed using (1) International Statistical Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes for nonfatal suicide attempt and self-harm and chief concern and (2) all available structured data, including diagnoses, medications, and laboratory tests.
Detection performance was assessed overall and stratified by age group, sex, and race and ethnicity.
The sample comprised 2702 unique youths with an MH-related ED visit (1384 youths who identified as female [51.2%]; 131 Asian [4.8%], 266 Black [9.8%], 719 Hispanic [26.6%], 1319 White [48.8%], and 233 other race [8.6%]; median [IQR] age, 14 [12-16] years), including 898 children and 1804 adolescents. Approximately half of visits were related to SITB (1286 visits [47.6%]). Sensitivity of SITB detection using only codes and chief concern varied by age group and increased until age 15 years (6-9 years: 59.3% [95% CI, 48.5%-69.5%]; 10-12 years: 69.0% [95% CI, 63.8%-73.9%]; 13-15 years: 88.4% [95% CI, 85.1%-91.2%]; 16-17 years: 83.1% [95% CI, 79.1%-86.6%]), while specificity remained constant. The area under the receiver operating characteristic curve (AUROC) was lower among preadolescents (0.841 [95% CI, 0.815-0.867]) and male (0.869 [95% CI, 0.848-0.890]), Black (0.859 [95% CI, 0.813-0.905]), and Hispanic (0.861 [95% CI, 0.831-0.891]) youths compared with adolescents (0.925 [95% CI, 0.912-0.938]), female youths (0.923 [95% CI, 0.909-0.937]), and youths of other races and ethnicities (eg, White: 0.901 [95% CI, 0.884-0.918]). Augmented classification (ie, using all available structured data) outperformed classification with codes and chief concern alone (AUROC, 0.975 [95% CI, 0.968-0.980] vs 0.894 [95% CI, 0.882-0.905]; P < .001).
In this study, diagnostic codes and chief concern underestimated SITB prevalence, particularly among minoritized youths. These results suggest that priority on algorithmic fairness in suicide prevention strategies must extend to accurate detection of youths with suicide-related emergencies.
自杀是年轻人死亡的主要原因。准确检测自我伤害思想和行为(SITB)是青少年自杀预防公平的基础。
比较使用结构化电子健康信息检测 SITB 的方法,并衡量不同人口统计学特征下的算法性能。
设计、地点和参与者:本横断面研究使用了 2017 年至 2019 年期间在南加州一个学术健康系统至少有 1 次与心理健康相关的急诊就诊的 6 至 17 岁青少年的医疗记录。分析于 2023 年 1 月至 9 月进行。
多专家电子健康记录审查使用哥伦比亚自杀评估分类算法确定 SITB 的存在。使用(1)非致命性自杀企图和自伤的国际疾病分类,第十次修订版,临床修正(ICD-10-CM)代码和主要关注点,以及(2)所有可用的结构化数据,包括诊断、药物和实验室测试,开发了嵌套交叉验证的随机森林分类器。
总体评估了检测性能,并按年龄组、性别和种族和民族进行分层。
样本包括 2702 名有心理健康相关急诊就诊的独特青少年(1384 名女性[51.2%];131 名亚裔[4.8%]、266 名非裔[9.8%]、719 名西班牙裔[26.6%]、1319 名白人[48.8%]和 233 名其他种族[8.6%];中位数[IQR]年龄为 14 [12-16] 岁),包括 898 名儿童和 1804 名青少年。大约一半的就诊与 SITB 有关(1286 次就诊[47.6%])。仅使用代码和主要关注点检测 SITB 的敏感性随年龄组而变化,直到 15 岁时增加(6-9 岁:59.3%[95%CI,48.5%-69.5%];10-12 岁:69.0%[95%CI,63.8%-73.9%];13-15 岁:88.4%[95%CI,85.1%-91.2%];16-17 岁:83.1%[95%CI,79.1%-86.6%]),而特异性保持不变。在青少年前(0.841[95%CI,0.815-0.867])和男性(0.869[95%CI,0.848-0.890])、非裔(0.859[95%CI,0.813-0.905])和西班牙裔(0.861[95%CI,0.831-0.891])青少年中,接受者操作特征曲线(AUROC)的 AUC 较低,而在青少年(0.925[95%CI,0.912-0.938])、女性青少年(0.923[95%CI,0.909-0.937])和其他种族和族裔的青少年(例如,白人:0.901[95%CI,0.884-0.918])中较高。增强分类(即使用所有可用的结构化数据)优于仅使用代码和主要关注点的分类(AUROC,0.975[95%CI,0.968-0.980]与 0.894[95%CI,0.882-0.905];P < .001)。
在这项研究中,诊断代码和主要关注点低估了 SITB 的患病率,尤其是在少数族裔青少年中。这些结果表明,在自杀预防策略中,算法公平性的优先级必须扩展到对与自杀相关的紧急情况的青少年进行准确检测。