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Predicting onset of major depression in general practice attendees in Europe: extending the application of the predictD risk algorithm from 12 to 24 months

Published online by Cambridge University Press:  04 January 2013

M. King*
Affiliation:
Mental Health Sciences Unit, Faculty of Brain Sciences, University College London Medical School, London, UK
C. Bottomley
Affiliation:
MRC Tropical Epidemiology Group, London School of Hygiene and Tropical Medicine, London, UK
J. Bellón-Saameño
Affiliation:
El Palo Health Centre, Department of Preventive Medicine, Malaga, Spain
F. Torres-Gonzalez
Affiliation:
Centro de Investigación Biomedica en Red de Salud Mental (CIBERSAM), Departmental Section of Psychiatry and Psychological Medicine, University of Granada, Granada, Spain
I. Švab
Affiliation:
Department of Family Medicine, University of Ljubljana, Ljubljana, Slovenia
D. Rotar
Affiliation:
Department of Family Medicine, University of Ljubljana, Ljubljana, Slovenia
M. Xavier
Affiliation:
Faculdade Ciências Médicas, University of Lisbon, Lisbon, Portugal
I. Nazareth
Affiliation:
MRC Tropical Epidemiology Group, London School of Hygiene and Tropical Medicine, London, UK Medical Research Council General Practice Research Framework, London, UK
*
*Address for correspondence: M. King, M.D. Ph.D., Director of Mental Health Sciences, Faculty of Brain Sciences, University College London Medical School, Charles Bell House, 67–73 Riding House Street, London W1W 7EH, UK. (Email: michael.king@ucl.ac.uk)

Abstract

Background

PredictD is a risk algorithm that was developed to predict risk of onset of major depression over 12 months in general practice attendees in Europe and validated in a similar population in Chile. It was the first risk algorithm to be developed in the field of mental disorders. Our objective was to extend predictD as an algorithm to detect people at risk of major depression over 24 months.

Method

Participants were 4190 adult attendees to general practices in the UK, Spain, Slovenia and Portugal, who were not depressed at baseline and were followed up for 24 months. The original predictD risk algorithm for onset of DSM-IV major depression had already been developed in data arising from the first 12 months of follow-up. In this analysis we fitted predictD to the longer period of follow-up, first by examining only the second year (12–24 months) and then the whole period of follow-up (0–24 months).

Results

The instrument performed well for prediction of major depression from 12 to 24 months [c-index 0.728, 95% confidence interval (CI) 0.675–0.781], or over the whole 24 months (c-index 0.783, 95% CI 0.757–0.809).

Conclusions

The predictD risk algorithm for major depression is accurate over 24 months, extending it current use of prediction over 12 months. This strengthens its use in prevention efforts in general medical settings.

Type
Original Articles
Copyright
Copyright © Cambridge University Press 2013 

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