A Pilot Study Using Frequent Inpatient Assessments of Suicidal Thinking to Predict Short-Term Postdischarge Suicidal Behavior.
A Pilot Study Using Frequent Inpatient Assessments of Suicidal Thinking to Predict Short-Term Postdischarge Suicidal Behavior.
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一项使用频繁的住院患者自杀想法评估预测短期出院后自杀行为的初步研究。
DOI：
10.1001/jamanetworkopen.2021.0591
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发表时间：
2021-03-01
影响因子：
13.8
通讯作者：
Nock MK
中科院分区：
文献类型：
--
作者：
Wang SB;Coppersmith DDL;Kleiman EM;Bentley KH;Millner AJ;Fortgang R;Mair P;Dempsey W;Huffman JC;Nock MK
通过频繁评估个体自杀想法的水平和变异性，能否提高对精神病住院后自杀企图的预测？在这项对83名成年住院精神病患者的预后研究中，仅使用基线数据时，对产后自杀企图的预测是公平的，在使用住院期间自杀想法的平均水平的模型中得到改善，在包括自杀想法的动态特征的模型中得到进一步改善。这些研究结果表明，自杀想法的实时动态变化数据可以提高对精神病住院后高危期自杀企图的预测。这项预后研究测试了与仅使用基线数据或使用住院期间实时自杀想法的平均水平相比，在精神病住院期间建模实时自杀想法的动态变化是否可以改善出院后自杀企图的预测。从精神病院出院后的几周是自杀企图的最高风险期。通过智能手机提示实时监测自杀念头可能比单一的横断面评估更能指示短期风险。测试与仅使用基线（即入院）数据或使用住院期间实时自杀想法的平均水平相比，对精神病住院期间实时自杀想法的动态变化建模是否可以改善出院后自杀企图的预测。在这项预后研究中，从马萨诸塞州总医院住院精神科招募了83名成年人，在住院期间每天完成4至6次自杀想法的生态瞬时评估调查，以及出院后2周和4周评估自杀企图的简短随访调查。参与者至少完成了3次实时监测调查。入选标准包括因自杀想法和/或行为以及英语流利而住院。数据收集于2016年1月至2018年12月，分析于2020年1月至12月。主要结局为出院后一个月内的自杀企图。在83名参与者中（平均[SD]年龄，38.4 [13.6]岁; 43名[51.8%]男性参与者; 69名[83.1%]白色个体），9名（10.8%）在出院后一个月内尝试自杀。弹性网络模型的平均交叉验证AUC显示，使用基线数据的模型的预测准确性是公平的（曲线下面积[AUC]，0.71;第一至第三四分位数，0.55-0.88），适用于使用住院期间实时自杀想法平均水平的模型（AUC，0.81;第一至第三四分位数，0.67-0.91），最适合使用住院期间实时自杀想法的动态变化模型（AUC，0.89;第一至第三四分位数，0.81-0.97）;这种结果模式适用于其他分类指标（例如，准确性、阳性预测值、Brier评分），并且当使用不同的交叉验证程序时。评估自杀想法的快速波动的特征成为产后自杀企图的最强预测因子。包含缺失百分比的最终一组模型进一步改善了平均值（平均AUC，0.93;第一至第三四分位数，0.90-1.00）和动态特征（平均AUC，0.93;第一至第三四分位数，0.88-1.00）模型。在这项研究中，在住院期间收集有关自杀想法的实时数据显着改善了对住院后自杀企图的短期预测。包括自杀想法随时间的动态变化的模型产生了最好的预测;捕捉自杀想法快速变化的特征是特别强的预测因子。调查未完成也成为一个重要的预测posthospitalization自杀企图。
Can prediction of suicide attempts after psychiatric hospitalization be improved using frequent assessments of the level and variability of an individual’s suicidal thoughts? In this prognostic study of 83 adult psychiatric inpatients, prediction of posthospital suicide attempts was fair when using only baseline data, improved in a model using mean level of suicidal thinking during hospitalization, and improved further in a model including dynamic features of suicidal thoughts. These findings suggest that data on real-time, dynamic changes in suicidal thoughts could improve prediction of suicide attempts during the high-risk period following psychiatric hospitalization. This prognostic study tests whether modeling dynamic changes in real-time suicidal thoughts during psychiatric hospitalization can improve predictions of postdischarge suicide attempts vs using only baseline data or using the mean level of real-time suicidal thoughts during hospitalization. The weeks following discharge from psychiatric hospitalization are the highest-risk period for suicide attempts. Real-time monitoring of suicidal thoughts via smartphone prompts may be more indicative of short-term risk than a single, cross-sectional assessment. To test whether modeling dynamic changes in real-time suicidal thoughts during psychiatric hospitalization can improve predictions of postdischarge suicide attempts vs using only baseline (ie, admission) data or using the mean level of real-time suicidal thoughts during hospitalization. In this prognostic study, 83 adults recruited from the inpatient psychiatric unit at Massachusetts General Hospital completed ecological momentary assessment surveys of suicidal thinking 4 to 6 times per day during hospitalization as well as brief follow-up surveys assessing suicide attempts at 2 and 4 weeks after discharge. Participants completed at least 3 real-time monitoring surveys. Inclusion criteria included hospitalization for suicidal thoughts and/or behaviors and English fluency. Data were collected from January 2016 to December 2018 and analyzed from January to December 2020. The primary outcome was suicide attempt in the month after discharge. Of 83 participants (mean [SD] age, 38.4 [13.6] years; 43 [51.8%] male participants; 69 [83.1%] White individuals), 9 (10.8%) made a suicide attempt in the month after discharge. Mean cross-validated AUC for elastic net models revealed predictive accuracy was fair for the model using baseline data (area under the curve [AUC], 0.71; first to third quartile, 0.55-0.88), good for the model using the mean level of real-time suicidal thoughts during hospitalization (AUC, 0.81; first to third quartile, 0.67-0.91), and best for the model using dynamic changes in real-time suicidal thoughts during hospitalization (AUC, 0.89; first to third quartile, 0.81-0.97); this pattern of results held for other classification metrics (eg, accuracy, positive predictive value, Brier score) and when using different cross-validation procedures. Features assessing rapid fluctuations in suicidal thinking emerged as the strongest predictors of posthospital suicide attempts. A final set of models incorporating percentage missingness further improved both the mean (mean AUC, 0.93; first to third quartile, 0.90-1.00) and dynamic feature (mean AUC, 0.93; first to third quartile, 0.88-1.00) models. In this study, collecting real-time data about suicidal thinking during the course of hospitalization significantly improved short-term prediction of posthospitalization suicide attempts. Models including dynamic changes in suicidal thinking over time yielded the best prediction; features that captured rapid changes in suicidal thoughts were particularly strong predictors. Survey noncompletion also emerged as an important predictor of posthospitalization suicide attempts.
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影响因子：
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作者：
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