Hostname: page-component-8448b6f56d-mp689 Total loading time: 0 Render date: 2024-04-19T06:48:11.070Z Has data issue: false hasContentIssue false

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*
Mental Health Sciences Unit, Faculty of Brain Sciences, University College London Medical School, London, UK
C. Bottomley
MRC Tropical Epidemiology Group, London School of Hygiene and Tropical Medicine, London, UK
J. Bellón-Saameño
El Palo Health Centre, Department of Preventive Medicine, Malaga, Spain
F. Torres-Gonzalez
Centro de Investigación Biomedica en Red de Salud Mental (CIBERSAM), Departmental Section of Psychiatry and Psychological Medicine, University of Granada, Granada, Spain
I. Švab
Department of Family Medicine, University of Ljubljana, Ljubljana, Slovenia
D. Rotar
Department of Family Medicine, University of Ljubljana, Ljubljana, Slovenia
M. Xavier
Faculdade Ciências Médicas, University of Lisbon, Lisbon, Portugal
I. Nazareth
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:



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.


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).


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).


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.

Original Articles
Copyright © Cambridge University Press 2013 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)


Anderson, KM, Wilson, PW, Odell, PM, Kannel, WB (1991). An updated coronary risk profile. A statement for health professionals. Circulation 83, 356362.CrossRefGoogle ScholarPubMed
Cassano, P, Fava, M (2002). Depression and public health: an overview. Journal of Psychosomatic Research 53, 849857.CrossRefGoogle Scholar
Conroy, RM, Pyorala, K, Fitzgerald, AP, Sans, S, Menotti, A, De Backer, G, De Bacquer, D, Ducimetiere, P, Jousilahti, P, Keil, U, Njolstad, I, Oganov, RG, Thomsen, T, Tunstall-Pedoe, H, Tverdal, A, Wedel, H, Whincup, P, Wilhelmsen, L, Graham, IM, and on behalf of the SCORE Project Group (2003). Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. European Heart Journal 24, 9871003.CrossRefGoogle ScholarPubMed
Gandek, B, Ware, JE, Aaronson, NK, Apolone, G, Bjorner, JB, Brazier, JE, Bullinger, M, Kaasa, S, Leplege, A, Prieto, L, Sullivan, M (1998). Cross-validation of item selection and scoring for the SF-12 Health survey in nine countries: results from the IQOLA Project. Journal of Clinical Epidemiology 11, 11711178.CrossRefGoogle Scholar
Goldberg, DP, Huxley, P (1992). Common Mental Disorders: A Bio-Social Model. Routledge: London/New York.Google Scholar
Harrell, FE (2001). Regression Modelling Strategies. Springer: New York.CrossRefGoogle Scholar
Janssen, I, Hanssen, M, Bak, M, Bijl, RV, de Graaf, R, Vollebergh, W, McKenzie, K, van Os, J (2003). Discrimination and delusional ideation. British Journal of Psychiatry 182, 7176.CrossRefGoogle ScholarPubMed
Janssen, KJ, Moons, KG, Kalkman, CJ, Grobbee, DE, Vergouwe, Y (2008). Updating methods improved the performance of a clinical prediction model in new patients. Journal of Clinical Epidemiology 61, 7686.CrossRefGoogle ScholarPubMed
Jenkinson, C, Layte, R, Jenkinson, D, Lawrence, K, Petersen, S, Paice, C, Stradling, J (1997). A shorter form health survey: can the SF-12 replicate results from the SF-36 in longitudinal studies? Journal of Public Health Medicine 19, 179186.CrossRefGoogle ScholarPubMed
Karasek, RA, Theorell, T (1990). Healthy Work: Stress, Productivity, and the Reconstruction of Working Life. Basic Books: New York.Google Scholar
King, M, Walker, C, Levy, G, Bottomley, C, Royston, P, Weich, S, Bellón-Saameño, J, Moreno, B, Svab, I, Rotar, D, Rifel, J, Maaroos, H, Aluoja, A, Kalda, R, Neeleman, J, Geerlings, MI, Xavier, M, Carraça, I, Gonçalves-Pereira, M, Vicente, B, Saldivia, S, Melipillan, R, Torres-Gonzalez, F, Nazareth, I (2008). Development and validation of an international risk prediction algorithm for episodes of major depression in general practice attendees: the PredictD study. Archives of General Psychiatry 65, 13681376.CrossRefGoogle ScholarPubMed
King, M, Weich, S, Torres, F, Svab, I, Maaroos, H, Neeleman, J, Xavier, M, Morris, R, Walker, C, Bellon, JA, Moreno, B, Rotar, D, Rifel, J, Aluoja, A, Kalda, R, Geerlings, MI, Carraca, I, Caldas de Almeida, M, Vicente, B, Saldivia, S, Rioseco, P, Nazareth, I (2006). Prediction of depression in European general practice attendees: the PREDICT study. BMC Public Health 6, 6.CrossRefGoogle ScholarPubMed
Pepe, MS, Janes, H, Longton, G, Leisenring, W, Newcomb, P (2004). Limitations of the odds ratio in gauging the performance of a diagnostic, prognostic, or screening marker. American Journal of Epidemiology 159, 882890.CrossRefGoogle ScholarPubMed
Qureshi, N, Bethea, J, Modell, B, Brennan, P, Papageorgiou, A, Raeburn, S, Hapgood, R, Modell, M (2005). Collecting genetic information in primary care: evaluating a new family history tool. Family Practice 22, 663669.CrossRefGoogle ScholarPubMed
Robins, LN, Wing, J, Wittchen, HU, Helzer, JE, Babor, TF, Burke, J, Farmer, A, Jablenski, A, Pickens, R, Regier, DA (1988). The Composite International Diagnostic Interview. An epidemiologic instrument suitable for use in conjunction with different diagnostic systems and in different cultures. Archives of General Psychiatry 45, 10691077.CrossRefGoogle ScholarPubMed
Steyerberg, EW, Borsboom, GJJM, van Houwelingen, HC, Eijkemans, MJC, Habbema, JD (2004). Validation and updating of predictive logistic regression models: a study on sample size and shrinkage. Statistics in Medicine 23, 25672586.CrossRefGoogle Scholar
Thornicroft, G, Sartorius, N (1993). The course and outcome of depression in different cultures: 10-year follow-up of the WHO Collaborative Study on the Assessment of Depressive Disorders. Psychological Medicine 23, 10231032.CrossRefGoogle Scholar
Weich, S (2001). Risk factors for the common mental disorders in primary care (Ph.D. thesis). University of Cambridge: Cambridge.Google Scholar
WHO (1997). Composite International Diagnostic Interview (CIDI). Version 2.1. World Health Organization: Geneva.Google Scholar
Supplementary material: File

King Supplementary Material


Download King Supplementary Material(File)
File 91.6 KB