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1 Section of General Internal Medicine, Department of Medicine, The University of Chicago, Chicago, Illinois
2 Section of Advanced Pediatrics, Department of Pediatrics, The University of Chicago, Chicago, Illinois
3 Section of Child Psychiatry, Department of Psychiatry, The University of Chicago, Chicago, Illinois
4 Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, Chicago, Illiinois
5 Department of Mental Health, Johns Hopkins School of Public Health, Baltimore, Maryland
6 Section of Hospital Medicine, Department of Medicine, The University of Chicago, Chicago, Illinois
CORRESPONDING AUTHOR: Benjamin W. Van Voorhees, MD, MPH, Section of General Internal Medicine, Department of Medicine, The University of Chicago, 5841 S Maryland, MC 2007, Chicago, IL 60637, bvanvoor{at}medicine.bsd.uchicago.edu
PURPOSE A risk prediction index, similar to those used for other disorders, such as cardiovascular disease, would facilitate depression prevention by identifying those who would benefit most from preventative measures in primary care settings.
METHODS The National Longitudinal Study of Adolescent Health enrolled a representative sample of US adolescents and included a baseline survey in 1995 and a 1-year follow-up survey in 1996 (n = 4,791). We used baseline risk factors (social and cognitive vulnerability and mood) to predict onset of a depressive episode at 1-year follow-up (eg, future risk of episode) and used boosted classification and regression trees to develop a prediction index, The Chicago Adolescent Depression Risk Assessment, suitable for a personal computer or hand-held device. True and false positives and negatives were determined based on concordance and discordance, respectively, between the prediction-category–based index and actual classification-category–based 1-year follow-up outcome. We evaluated the performance of the index for the entire sample and with several depressive episode outcomes using the standard Center for Epidemiologic Studies Depression (CES-D) scale cutoffs.
RESULTS The optimal prediction model (including depressed mood and social vulnerability) was a 20-item model with an area under the receiver operating characteristics curve of 0.80 (95% CI, 0.714–0.870), a sensitivity of 75%, and a specificity of 76.5%. For depressive episode, the positive predictive values in the highest risk group (level 4) was from 13.75% for a depressive episode to 63.57% for CES-D score of greater than 16 (mild to moderate depressed mood or above) at follow-up. Conversely, the negative predictive value of being in the lowest 2 levels (0 or 1) was 99.38% for a depressive episode and 89.19% for a CES-D score of greater than 16.
CONCLUSIONS Our model predicts a depressive episode and other depressive outcomes at 1-year follow-up. Positive and negative predictive values could enable primary care physicians and families to intervene on adolescents at highest risk.
Key Words: Depression • depressive disorder • risk assessment • behavioral medicine • primary health care
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