Development and validation of prediction algorithms for major depressive episode in the general population

JOURNAL OF AFFECTIVE DISORDERS

卷:151

Article

Wang, Jian Li^1,2  Manuel, Douglas³  Williams, Jeanne⁴  Schmitz, Norbert⁵  Gilmour, Heather⁶  Patten, Scott^4,7  MacQueen, Glenda⁷  Birney, Arden⁴ 

[1] Univ Calgary, Fac Med, Dept Psychiat, Calgary, AB T2N 4Z6, Canada

[2] Univ Calgary, Fac Med, Dept Community Hlth Sci, Calgary, AB T2N 4Z6, Canada

[3] Univ Ottawa, Fac Med, Dept Clin Epidemiol, Ottawa, ON K1N 6N5, Canada

[4] Univ Calgary, Fac Med, Dept Community Hlth Sci, Calgary, AB T2N 1N4, Canada

[5] McGill Univ, Fac Med, Dept Psychiat, Montreal, PQ H3A 0G4, Canada

[6] STAT Canada, Hlth Anal Div, Ottawa, ON K1A 0T6, Canada

[7] Univ Calgary, Fac Med, Dept Psychiat, Calgary, AB T2N 1N4, Canada

关键词: Prediction algorithms;    Major depression;    Sex-specific;    Population based;   

DOI  :  10.1016/j.jad.2013.05.045

来源: Elsevier

【 摘 要 】

Background: To develop and validate sex specific prediction algorithms for 4-year risk of major depressive episode (MDE) using data from a population based longitudinal cohort. Methods: Household residents from 10 provinces were randomly recruited and interviewed by Statistics Canada. 10,601 participants who were aged 18 years and older and who did not meet the criteria for MDE in the 12 months prior to a baseline interview in 2000/01 were included in algorithm development; data horn 7902 participants who were aged 18 and older and who were free of MDE in 2004/05 were used for validation. Validation was also conducted in sub-populations that are of practice and policy importance. MDE was assessed using the World Health Organization's Composite International Diagnostic Interview (CIDI)-Short Form for Major Depression (CIDI-SFMD). Results: In the training data, the C statistics for algorithms in men was 0.7953 and was 0.7667 for algorithm in women. The algorithms had good predictive power and calibrated well in the development and validation data. Limitations: The data relied on self-report. MDE was assessed with CIDI-SFMD. It was not feasible to validate the algorithms in different populations from different countries. Conclusions: More studies are needed to further validate and refine these algorithms. However, the ability of a small number of easily assessed variables to predict MDE risk indicates that algorithms are a promising strategy for identifying individuals in need of enhanced monitoring and preventive interventions. Ultimately, application of algorithms may lead to increased personalization of treatment, and better clinical outcomes. (C) 2013 The Authors. Published by Elsevier B.V. All rights reserved

【 授权许可】

Free