PDF(Pubmed)
Abstract：
UNASSIGNED: Scalable PTSD screening strategies must be brief, accurate and capable of administration by a non-specialized workforce.
UNASSIGNED: We used PTSD as determined by the structured clinical interview as our gold standard and considered predictors sets of (a) Posttraumatic Stress Checklist-5 (PCL-5), (b) Primary Care PTSD Screen for the DSM-5 (PC-PTSD) and, (c) PCL-5 and PC-PTSD questions to identify the optimal items for PTSD screening for public sector settings in Kenya. A logistic regression model using LASSO was fit by minimizing the average squared error in the validation data. Area under the receiver operating characteristic curve (AUROC) measured discrimination performance.
UNASSIGNED: Penalized regression analysis suggested a screening tool that sums the Likert scale values of two PCL-5 questions-intrusive thoughts of the stressful experience (#1) and insomnia (#21). This had an AUROC of 0.85 (using hold-out test data) for predicting PTSD as evaluated by the MINI, which outperformed the PC-PTSD. The AUROC was similar in subgroups defined by age, sex, and number of categories of trauma experienced (all AUROCs>0.83) except those with no trauma history- AUROC was 0.78.
UNASSIGNED: In some East African settings, a 2-item PTSD screening tool may outperform longer screeners and is easily scaled by a non-specialist workforce.
UNASSIGNED: We used PTSD as determined by the structured clinical interview as our gold standard and considered predictors sets of (a) Posttraumatic Stress Checklist-5 (PCL-5), (b) Primary Care PTSD Screen for the DSM-5 (PC-PTSD) and, (c) PCL-5 and PC-PTSD questions to identify the optimal items for PTSD screening for public sector settings in Kenya. A logistic regression model using LASSO was fit by minimizing the average squared error in the validation data. Area under the receiver operating characteristic curve (AUROC) measured discrimination performance.
UNASSIGNED: Penalized regression analysis suggested a screening tool that sums the Likert scale values of two PCL-5 questions-intrusive thoughts of the stressful experience (#1) and insomnia (#21). This had an AUROC of 0.85 (using hold-out test data) for predicting PTSD as evaluated by the MINI, which outperformed the PC-PTSD. The AUROC was similar in subgroups defined by age, sex, and number of categories of trauma experienced (all AUROCs>0.83) except those with no trauma history- AUROC was 0.78.
UNASSIGNED: In some East African settings, a 2-item PTSD screening tool may outperform longer screeners and is easily scaled by a non-specialist workforce.
摘要：
■可扩展的PTSD筛查策略必须简短，准确，能够由非专业劳动力管理。
■我们使用由结构化临床访谈确定的PTSD作为我们的黄金标准，并考虑了（a）创伤后应激清单5（PCL-5）的预测因素集，（b）DSM-5（PC-PTSD）的初级保健PTSD屏幕，（c）PCL-5和PC-PTSD问题，以确定肯尼亚公共部门环境中PTSD筛查的最佳项目。通过最小化验证数据中的平均平方误差来拟合使用LASSO的逻辑回归模型。接收器工作特性曲线下面积(AUROC)测量辨别性能。
■惩罚回归分析提出了一种筛选工具，该工具将两个PCL-5问题的李克特量表值求和-对压力经历（＃1）和失眠（＃21）的侵入性想法。根据MINI的评估，预测PTSD的AUROC为0.85（使用固定测试数据），优于PC-PTSD。AUROC在按年龄定义的亚组中相似，性别,除了没有创伤史的患者，经历的创伤类别数量（所有AUROC>0.83）-AUROC为0.78。
■在某些东非环境中，2个项目的PTSD筛查工具可能优于更长的筛查人员，并且很容易由非专业人员进行缩放。
■我们使用由结构化临床访谈确定的PTSD作为我们的黄金标准，并考虑了（a）创伤后应激清单5（PCL-5）的预测因素集，（b）DSM-5（PC-PTSD）的初级保健PTSD屏幕，（c）PCL-5和PC-PTSD问题，以确定肯尼亚公共部门环境中PTSD筛查的最佳项目。通过最小化验证数据中的平均平方误差来拟合使用LASSO的逻辑回归模型。接收器工作特性曲线下面积(AUROC)测量辨别性能。
■惩罚回归分析提出了一种筛选工具，该工具将两个PCL-5问题的李克特量表值求和-对压力经历（＃1）和失眠（＃21）的侵入性想法。根据MINI的评估，预测PTSD的AUROC为0.85（使用固定测试数据），优于PC-PTSD。AUROC在按年龄定义的亚组中相似，性别,除了没有创伤史的患者，经历的创伤类别数量（所有AUROC>0.83）-AUROC为0.78。
■在某些东非环境中，2个项目的PTSD筛查工具可能优于更长的筛查人员，并且很容易由非专业人员进行缩放。
