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 from 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.
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 from 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.
Authors: JianLi Wang; Scott B Patten; Raymond W Lam; Mark Attridge; Kendall Ho; Norbert Schmitz; Alain Marchand; Bonnie M Lashewicz Journal: JMIR Res Protoc Date: 2016-11-15
Authors: Ania Syrowatka; Aude Motulsky; Siyana Kurteva; James A Hanley; William G Dixon; Ari N Meguerditchian; Robyn Tamblyn Journal: Breast Cancer Res Treat Date: 2017-05-28 Impact factor: 4.872
Authors: JianLi Wang; Raymond W Lam; Kendall Ho; Mark Attridge; Bonnie M Lashewicz; Scott B Patten; Alain Marchand; Alice Aiken; Norbert Schmitz; Sarika Gundu; Nitika Rewari; David Hodgins; Andrew Bulloch; Zul Merali Journal: J Med Internet Res Date: 2016-06-06 Impact factor: 5.428