Development and Validation of an International Risk Prediction Algorithm for Episodes of Major Depression in General Practice Attendees The PredictD Study
Development and Validation of an International Risk Prediction Algorithm for Episodes of Major Depression in General Practice Attendees The PredictD Study
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DOI：
10.1001/archpsyc.65.12.1368
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发表时间：
2008-12-01
影响因子：
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通讯作者：
Nazareth, Irwin
中科院分区：
文献类型：
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作者：
King, Michael;Walker, Carl;Nazareth, Irwin
内容：预防抑郁症的策略是阻碍了缺乏证据的联合预测效果的已知riskfactors.Objectives：要开发一个风险算法的发病major depression.Design：队列的成年全科医生参加者随访6个月和12个月。我们测量了39个已知的危险因素，采用逐步logistic回归建立了一个抑郁症发病的危险模型。我们纠正过拟合模型，并测试它在外部population.Setting：一般做法，在6个欧洲国家和智利。参与者：在欧洲和智利，10 045与会者被招募2003年4月至2005年2月。该算法是在5216名欧洲与会者中开发的，他们在招募时没有抑郁症，并且有抑郁状态的随访数据。它是在1732例在智利谁没有抑郁症在recruitment.Main Outcome Measure：DSM-IV major depression.Results：66%的人接近参加，其中89.5%再次参加在6个月和85.9%，在12个月。风险算法中的10个因素中有9个是年龄，性别，达到的教育水平，终身抑郁症筛查结果，心理困难家族史，简表12的身体健康和心理健康子量表评分，无支持的有偿或无偿工作困难，以及歧视经历。国家是第十个因素。该算法在各国的平均C指数为0.790（95%置信区间[ CI]，0.767-0.813）。欧洲与会者中抑郁症的预测对数几率差异的效应量为1.28（95% CI，1.17-1.40）。在智利与会者的应用程序的算法导致C指数为0.710（95%CI，0.670-0.749）。结论：这第一个风险算法的功能，以及类似的风险算法的心血管事件，并可能是有用的抑郁症的预防。
Context: Strategies for prevention of depression are hindered by lack of evidence about the combined predictive effect of known risk factors.Objectives: To develop a risk algorithm for onset of major depression.Design: Cohort of adult general practice attendees followed up at 6 and 12 months. We measured 39 known risk factors to construct a risk model for onset of major depression using stepwise logistic regression. We corrected the model for overfitting and tested it in an external population.Setting: General practices in 6 European countries and in Chile.Participants: In Europe and Chile, 10 045 attendees were recruited April 2003 to February 2005. The algorithm was developed in 5216 European attendees who were not depressed at recruitment and had follow-up data on depression status. It was tested in 1732 patients in Chile who were not depressed at recruitment.Main Outcome Measure: DSM-IV major depression.Results: Sixty-six percent of people approached participated, of whom 89.5% participated again at 6 months and 85.9%, at 12 months. Nine of the 10 factors in the risk algorithm were age, sex, educational level achieved, results of lifetime screen for depression, family history of psychological difficulties, physical health and mental health subscale scores on the Short Form 12, unsupported difficulties in paid or unpaid work, and experiences of discrimination. Country was the tenth factor. The algorithm's average C index across countries was 0.790 ( 95% confidence interval [ CI], 0.767-0.813). Effect size for difference in predicted log odds of depression between European attendees who became depressed and those who did not was 1.28 ( 95% CI, 1.17-1.40). Application of the algorithm in Chilean attendees resulted in a C index of 0.710 ( 95% CI, 0.670-0.749).Conclusion: This first risk algorithm for onset of major depression functions as well as similar risk algorithms for cardiovascular events and may be useful in prevention of depression.