A clinical prediction rule for detecting major depressive disorder in primary care: the PREDICT-NL study.

BACKGROUND Major depressive disorder often remains unrecognized in primary care. OBJECTIVE Development of a clinical prediction rule using easily obtainable predictors for major depressive disorder in primary care patients. METHODS A total of 1046 subjects, aged 18-65 years, were included from seven large general practices in the center of The Netherlands. All subjects were recruited in the general practice waiting room, irrespective of their presenting complaint. Major depressive disorder according to Diagnostic and Statistical Manual of Mental Disorders, Fourth Text Revision edition criteria was assessed with the Composite International Diagnostic Interview. Candidate predictors were gender, age, educational level, being single, number of presented complaints, presence of non-somatic complaints, whether a diagnosis was assigned, consultation rate in past 12 months, presentation of depressive complaints or prescription of antidepressants in past 12 months, number of life events in past 6 months and any history of depression. RESULTS The first multivariable logistic regression model including only predictors that require no confronting depression-related questions had a reasonable degree of discrimination (area under the receiver operating characteristic curve or concordance-statistic (c-statistic) = 0.71; 95% Confidence Interval (CI): 0.67-0.76). Addition of three simple though more depression-related predictors, number of life events and history of depression, significantly increased the c-statistic to 0.80 (95% CI: 0.76-0.83). After transforming this second model to an easily to use risk score, the lowest risk category (sum score < 5) showed a 1% risk of depression, which increased to 49% in the highest category (sum score > or = 30). CONCLUSION A clinical prediction rule allows GPs to identify patients-irrespective of their complaints-in whom diagnostic workup for major depressive disorder is indicated.