King's College London

TY - JOUR

T1 - Identifying adolescents at risk for depression: assessment of a global prediction model in the Great Smoky Mountains Study

AU - Caye, Arthur

AU - Marchionatti, Lauro

AU - Pereira, Rivka

AU - Fisher, Helen

AU - Kohrt, Brandon A

AU - Mondelli, Valeria

AU - McGinnis, Ellen

AU - Copeland, William

AU - Kieling, Christian

N1 - Funding Information: The IDEA project was funded by an MQ Brighter Futures grant [ MQBF/1 IDEA ]. Additional support was provided by the UK Medical Research Council [ MC_PC_MR/R019460/1 ] and the Academy of Medical Sciences [ GCRFNG_100281 ] under the Global Challenges Research Fund. This work is also supported by research grants from Brazilian public funding agencies Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) [ 477129/2012–9 and 445828/2014–5 ], Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) [ 62/2014 ], and Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (FAPERGS) [ 17/2551-0001009-4 ]. CK is a Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) researcher and an Academy of Medical Sciences Newton Advanced Fellow. CK and BAK are supported by the U.S. National Institute of Mental Health [ R21MH124072 ]. VM was supported by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London . HLF was part supported by the Economic and Social Research Council (ESRC) Centre for Society and Mental Health at King's College London [ ES/S012567/1 ]. WC has received research support from the National Institute of Mental Health , the National Institute on Drug Abuse , and the Eunice Kennedy Shriver National Institute of Child Health and Human Development . The views hereby expressed are those of the authors and not necessarily those of the funders. Funding Information: AC has acted as a consultant for Knight Therapeutics in the past year. VM has received research funding from Johnson & Johnson . All other authors declare no competing interests. Publisher Copyright: © 2022 The Authors

PY - 2022/11

Y1 - 2022/11

N2 - The Identifying Depression Early in Adolescence Risk Score (IDEA-RS) has been externally assessed in samples from four continents, but North America is lacking. Our aim here was to evaluate the performance of the IDEA-RS in predicting future onset of Major Depressive Disorder (MDD) in an adolescent population-based sample in the United States of America - the Great Smoky Mountains Study (GSMS). We applied the intercept and weights of the original IDEA-RS model developed in Brazil to generate individual probabilities for each participant of the GSMS at age 15 (N = 1029). We then evaluated the performance of such predictions against the diagnosis of MDD at age 19 using simple, case-mix corrected and refitted models. Furthermore, we compared how prioritizing the information provided by parents or by adolescents affected performance. The IDEA-RS exhibited a C-statistic of 0.63 (95% CI 0.53-0.74) to predict MDD in the GSMS when applying uncorrected weights. Case-mix corrected and refitted models enhanced performance to 0.69 and 0.67, respectively. No significant difference was found in performance by prioritizing the reports of adolescents or their parents. The IDEA-RS was able to parse out adolescents at risk for a later onset of depression in the GSMS cohort with above chance discrimination. The IDEA-RS has now showed above-chance performance in five continents.

AB - The Identifying Depression Early in Adolescence Risk Score (IDEA-RS) has been externally assessed in samples from four continents, but North America is lacking. Our aim here was to evaluate the performance of the IDEA-RS in predicting future onset of Major Depressive Disorder (MDD) in an adolescent population-based sample in the United States of America - the Great Smoky Mountains Study (GSMS). We applied the intercept and weights of the original IDEA-RS model developed in Brazil to generate individual probabilities for each participant of the GSMS at age 15 (N = 1029). We then evaluated the performance of such predictions against the diagnosis of MDD at age 19 using simple, case-mix corrected and refitted models. Furthermore, we compared how prioritizing the information provided by parents or by adolescents affected performance. The IDEA-RS exhibited a C-statistic of 0.63 (95% CI 0.53-0.74) to predict MDD in the GSMS when applying uncorrected weights. Case-mix corrected and refitted models enhanced performance to 0.69 and 0.67, respectively. No significant difference was found in performance by prioritizing the reports of adolescents or their parents. The IDEA-RS was able to parse out adolescents at risk for a later onset of depression in the GSMS cohort with above chance discrimination. The IDEA-RS has now showed above-chance performance in five continents.

UR - http://www.scopus.com/inward/record.url?scp=85136597062&partnerID=8YFLogxK

U2 - 10.1016/j.jpsychires.2022.08.017

DO - 10.1016/j.jpsychires.2022.08.017

M3 - Article

VL - 155

SP - 146

EP - 152

JO - Journal of psychiatric research

JF - Journal of psychiatric research

SN - 0022-3956

ER -