Psychological Medicine

Original Articles

An international risk prediction algorithm for the onset of generalized anxiety and panic syndromes in general practice attendees: predictA

M. Kinga1 c1, C. Bottomleya2, J. A. Bellón-Saameñoa3, F. Torres-Gonzaleza4, I. Švaba5, J. Rifela5, H.-I. Maaroosa6, A. Aluojaa6, M. I. Geerlingsa7, M. Xaviera8, I. Carraçaa9, B. Vicentea10, S. Saldiviaa10 and I. Nazaretha2a11

a1 Department of Mental Health Sciences, UCL Medical School, UK

a2 Department of Primary Care and Population Health, UCL Medical School, UK

a3 El Palo Health Centre, Department of Preventive Medicine, University of Malaga, Spain

a4 Department of Psychiatry, University of Granada, Spain

a5 Department of Family Medicine, University of Ljubljana, Slovenia

a6 Faculty of Medicine, University of Tartu, Estonia

a7 University Medical Center, Utrecht, The Netherlands

a8 Faculdade Ciências Médicas, University of Lisbon, Portugal

a9 Encarnação Health Centre, Portugal

a10 Departamento de Psiquiatría y Salud Mental, Universidad de Concepción, Chile

a11 Medical Research Council General Practice Research Framework, UK


Background There are no risk models for the prediction of anxiety that may help in prevention. We aimed to develop a risk algorithm for the onset of generalized anxiety and panic syndromes.

Method Family practice attendees were recruited between April 2003 and February 2005 and followed over 24 months in the UK, Spain, Portugal and Slovenia (Europe4 countries) and over 6 months in The Netherlands, Estonia and Chile. Our main outcome was generalized anxiety and panic syndromes as measured by the Patient Health Questionnaire. We entered 38 variables into a risk model using stepwise logistic regression in Europe4 data, corrected for over-fitting and tested it in The Netherlands, Estonia and Chile.

Results There were 4905 attendees in Europe4, 1094 in Estonia, 1221 in The Netherlands and 2825 in Chile. In the algorithm four variables were fixed characteristics (sex, age, lifetime depression screen, family history of psychological difficulties); three current status (Short Form 12 physical health subscale and mental health subscale scores, and unsupported difficulties in paid and/or unpaid work); one concerned country; and one time of follow-up. The overall C-index in Europe4 was 0.752 [95% confidence interval (CI) 0.724–0.780]. The effect size for difference in predicted log odds between developing and not developing anxiety was 0.972 (95% CI 0.837–1.107). The validation of predictA resulted in C-indices of 0.731 (95% CI 0.654–0.809) in Estonia, 0.811 (95% CI 0.736–0.886) in The Netherlands and 0.707 (95% CI 0.671–0.742) in Chile.

Conclusions PredictA accurately predicts the risk of anxiety syndromes. The algorithm is strikingly similar to the predictD algorithm for major depression, suggesting considerable overlap in the concepts of anxiety and depression.

(Received March 02 2010)

(Revised November 12 2010)

(Accepted November 15 2010)

(Online publication January 06 2011)


c1 Address for correspondence: M. King, M.D., Ph.D., Department of Mental Health Sciences, University College London Medical School, Charles Bell House, 67–73 Riding House Street, London W1W 7EH, UK. (Email: