ImportanceGiven that suicide rates have been increasing over the past decade and the demand for mental health care is at an all-time high, targeted prevention efforts are needed to identify individuals seeking to initiate mental health outpatient services who are at high risk for suicide. Suicide prediction models have been developed using outpatient mental health encounters, but their performance among intake appointments has not been directly examined.ObjectiveTo assess the performance of a predictive model of suicide attempts among individuals seeking to initiate an episode of outpatient mental health care.Design, Setting, and ParticipantsThis prognostic study tested the performance of a previously developed machine learning model designed to predict suicide attempts within 90 days of any mental health outpatient visit. All mental health intake appointments scheduled between January 1, 2012, and April 1, 2022, at Kaiser Permanente Northern California, a large integrated health care delivery system serving over 4.5 million patients, were included. Data were extracted and analyzed from August 9, 2022, to July 31, 2023.Main Outcome and MeasuresSuicide attempts (including completed suicides) within 90 days of the appointment, determined by diagnostic codes and government databases. All predictors were extracted from electronic health records.ResultsThe study included 1 623 232 scheduled appointments from 835 616 unique patients. There were 2800 scheduled appointments (0.17%) followed by a suicide attempt within 90 days. The mean (SD) age across appointments was 39.7 (15.8) years, and most appointments were for women (1 103 184 [68.0%]). The model had an area under the receiver operating characteristic curve of 0.77 (95% CI, 0.76-0.78), an area under the precision-recall curve of 0.02 (95% CI, 0.02-0.02), an expected calibration error of 0.0012 (95% CI, 0.0011-0.0013), and sensitivities of 37.2% (95% CI, 35.5%-38.9%) and 18.8% (95% CI, 17.3%-20.2%) at specificities of 95% and 99%, respectively. The 10% of appointments at the highest risk level accounted for 48.8% (95% CI, 47.0%-50.6%) of the appointments followed by a suicide attempt.Conclusions and RelevanceIn this prognostic study involving mental health intakes, a previously developed machine learning model of suicide attempts showed good overall classification performance. Implementation research is needed to determine appropriate thresholds and interventions for applying the model in an intake setting to target high-risk cases in a manner that is acceptable to patients and clinicians.

中文翻译：

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验证用于预测心理健康摄入样本中自杀企图的多变量模型

重要性鉴于过去十年自杀率一直在上升，而且对心理健康护理的需求空前高涨，因此需要采取有针对性的预防工作，以确定寻求启动心理健康门诊服务的自杀高危人群。自杀预测模型是利用门诊心理健康遭遇开发的，但尚未直接检查其在入院预约中的表现。目的评估自杀未遂预测模型在寻求启动门诊心理健康护理的个人中的表现。设计，设置和参与者这项预后研究测试了先前开发的机器学习模型的性能，该模型旨在预测任何心理健康门诊就诊后 90 天内的自杀企图。 2012 年 1 月 1 日至 2022 年 4 月 1 日期间安排在北加州 Kaiser Permanente（一个为超过 450 万名患者提供服务的大型综合医疗保健服务系统）进行的所有心理健康预约均包含在内。数据提取和分析时间为2022年8月9日至2023年7月31日。主要结果和措施预约后90天内的自杀企图（包括完成的自杀），由诊断代码和政府数据库确定。所有预测因素均从电子健康记录中提取。结果该研究包括来自 835 616 名独特患者的 1 623 232 次预约。有 2800 次预约（0.17%）在 90 天内企图自杀。各预约的平均 (SD) 年龄为 39.7 (15.8) 岁，大多数预约是女性 (1 103 184 [68.0%])。该模型的受试者工作特征曲线下面积为 0.77（95% CI，0.76-0.78），精确回忆曲线下面积为 0.02（95% CI，0.02-0.02），预期校准误差为 0.0012（ 95% CI, 0.0011-0.0013)，敏感性分别为 37.2% (95% CI, 35.5%-38.9%) 和 18.8% (95% CI, 17.3%-20.2%)，特异性为 95% 和 99%。最高风险水平的 10% 预约占自杀未遂预约的 48.8%（95% CI，47.0%-50.6%）。结论和相关性在这项涉及心理健康摄入量的预后研究中，之前开发的机器学习自杀未遂模型表现出良好的整体分类性能。需要实施研究来确定适当的阈值和干预措施，以便以患者和临床医生可接受的方式在摄入环境中应用该模型来针对高风险病例。