Department of Mental Health Sciences, UCL Medical School, UK.
Psychol Med. 2011 Aug;41(8):1625-39. doi: 10.1017/S0033291710002400. Epub 2011 Jan 6.
There are no risk models for the prediction of anxiety that may help in prevention. We aimed to develop a risk algorithm for the onset of generalized anxiety and panic syndromes.
Family practice attendees were recruited between April 2003 and February 2005 and followed over 24 months in the UK, Spain, Portugal and Slovenia (Europe4 countries) and over 6 months in The Netherlands, Estonia and Chile. Our main outcome was generalized anxiety and panic syndromes as measured by the Patient Health Questionnaire. We entered 38 variables into a risk model using stepwise logistic regression in Europe4 data, corrected for over-fitting and tested it in The Netherlands, Estonia and Chile.
There were 4905 attendees in Europe4, 1094 in Estonia, 1221 in The Netherlands and 2825 in Chile. In the algorithm four variables were fixed characteristics (sex, age, lifetime depression screen, family history of psychological difficulties); three current status (Short Form 12 physical health subscale and mental health subscale scores, and unsupported difficulties in paid and/or unpaid work); one concerned country; and one time of follow-up. The overall C-index in Europe4 was 0.752 [95% confidence interval (CI) 0.724-0.780]. The effect size for difference in predicted log odds between developing and not developing anxiety was 0.972 (95% CI 0.837-1.107). The validation of predictA resulted in C-indices of 0.731 (95% CI 0.654-0.809) in Estonia, 0.811 (95% CI 0.736-0.886) in The Netherlands and 0.707 (95% CI 0.671-0.742) in Chile.
PredictA accurately predicts the risk of anxiety syndromes. The algorithm is strikingly similar to the predictD algorithm for major depression, suggesting considerable overlap in the concepts of anxiety and depression.
目前尚无以预防为目的的预测焦虑风险的模型。我们旨在开发预测广泛性焦虑和惊恐综合征发病的风险算法。
在英国、西班牙、葡萄牙和斯洛文尼亚（欧洲 4 国），家庭医生于 2003 年 4 月至 2005 年 2 月间招募参与者，随访 24 个月；在荷兰、爱沙尼亚和智利，随访 6 个月。我们的主要结局为患者健康问卷所测量的广泛性焦虑和惊恐综合征。我们在欧洲 4 国的数据中使用逐步逻辑回归法将 38 个变量纳入风险模型，校正过度拟合，然后在荷兰、爱沙尼亚和智利进行检验。
欧洲 4 国共纳入 4905 名参与者，爱沙尼亚 1094 名，荷兰 1221 名，智利 2825 名。算法中，4 个变量为固定特征（性别、年龄、终生抑郁筛查、心理困难家族史），3 个为当前状态变量（SF-12 生理健康子量表和心理健康子量表评分，以及带薪和/或无薪工作中无法获得支持的困难），1 个为相关国家，1 个为随访时间。欧洲 4 国的整体 C 指数为 0.752（95%可信区间为 0.724～0.780）。发展为焦虑和不发展为焦虑的预测对数优势差异的效应量为 0.972（95%可信区间为 0.837～1.107）。预测 A 的验证得出的 C 指数在爱沙尼亚为 0.731（95%可信区间为 0.654～0.809），荷兰为 0.811（95%可信区间为 0.736～0.886），智利为 0.707（95%可信区间为 0.671～0.742）。
预测 A 可准确预测焦虑综合征风险。该算法与预测抑郁症的 predictD 算法极为相似，提示焦虑和抑郁的概念存在显著重叠。
