Development and Validation of an International Risk Prediction Algorithm for Episodes of Major Depression in General Practice Attendees: The PredictD Study | Depressive Disorders | JAMA Psychiatry | JAMA Network
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Original Article
December 1, 2008

Development and Validation of an International Risk Prediction Algorithm for Episodes of Major Depression in General Practice Attendees: The PredictD Study

Author Affiliations

Author Affiliations: Departments of Mental Health Sciences (Drs King and Walker and Mr Levy) and Primary Care and Population Sciences (Drs Bottomley and Nazareth), University College London, Medical Research Council General Practice Research Framework (Mr Levy and Dr Nazareth), and Medical Research Council Clinical Trials Unit (Dr Royston), London, and Health Sciences Research Institute, University of Warwick, Coventry (Dr Weich), England; Department of Preventive Medicine, El Palo Health Centre, Malaga (Dr Bellón-Saameño), and Department of Psychiatry, University of Granada, Granada (Drs Moreno and Torres-Gonzalez), Spain; Department of Family Medicine, University of Ljubljana, Ljubljana, Slovenia (Drs Švab, Rotar, and Rifel); Faculty of Medicine, University of Tartu, Tartu, Estonia (Drs Maaroos, Aluoja, and Kalda); University Medical Center, Utrecht, the Netherlands (Drs Neeleman and Geerlings); Faculdade Ciências Médicas, University of Lisbon (Drs Xavier and Gonçalves-Pereira), and Encarnação Health Centre (Dr Carraça), Lisbon, Portugal; and Departamento de Psiquiatría y Salud Mental, Universidad de Concepción, Concepción, Chile (Drs Vicente and Saldivia and Mr Melipillan).

Arch Gen Psychiatry. 2008;65(12):1368-1376. doi:10.1001/archpsyc.65.12.1368
Abstract

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.

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