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

Published online by Cambridge University Press:  06 January 2011

M. King*
Affiliation:
Department of Mental Health Sciences, UCL Medical School, UK
C. Bottomley
Affiliation:
Department of Primary Care and Population Health, UCL Medical School, UK
J. A. Bellón-Saameño
Affiliation:
El Palo Health Centre, Department of Preventive Medicine, University of Malaga, Spain
F. Torres-Gonzalez
Affiliation:
Department of Psychiatry, University of Granada, Spain
I. Švab
Affiliation:
Department of Family Medicine, University of Ljubljana, Slovenia
J. Rifel
Affiliation:
Department of Family Medicine, University of Ljubljana, Slovenia
H.-I. Maaroos
Affiliation:
Faculty of Medicine, University of Tartu, Estonia
A. Aluoja
Affiliation:
Faculty of Medicine, University of Tartu, Estonia
M. I. Geerlings
Affiliation:
University Medical Center, Utrecht, The Netherlands
M. Xavier
Affiliation:
Faculdade Ciências Médicas, University of Lisbon, Portugal
I. Carraça
Affiliation:
Encarnação Health Centre, Portugal
B. Vicente
Affiliation:
Departamento de Psiquiatría y Salud Mental, Universidad de Concepción, Chile
S. Saldivia
Affiliation:
Departamento de Psiquiatría y Salud Mental, Universidad de Concepción, Chile
I. Nazareth
Affiliation:
Department of Primary Care and Population Health, UCL Medical School, UK Medical Research Council General Practice Research Framework, UK
*
*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: m.king@medsch.ucl.ac.uk)

Abstract

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.

Type
Original Articles
Copyright
Copyright © Cambridge University Press 2011

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