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Developing and validating a prediction model of adolescent major depressive disorder in the offspring of depressed parents
Author(s) -
Stephens Alice,
Allardyce Judith,
Weavers Bryony,
Len Jessica,
Jones Rhys Bevan,
Powell Victoria,
Eyre Olga,
Potter Robert,
Price Valentina Escott,
Osborn David,
Thapar Anita,
Collishaw Stephan,
Thapar Ajay,
Heron Jon,
Rice Frances
Publication year - 2023
Publication title -
journal of child psychology and psychiatry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.652
H-Index - 211
eISSN - 1469-7610
pISSN - 0021-9630
DOI - 10.1111/jcpp.13704
Subject(s) - major depressive disorder , depression (economics) , interquartile range , psychology , psychological intervention , statistic , risk factor , clinical psychology , cohort , psychiatry , medicine , statistics , mood , mathematics , economics , macroeconomics
Background Parental depression is common and is a major risk factor for depression in adolescents. Early identification of adolescents at elevated risk of developing major depressive disorder (MDD) in this group could improve early access to preventive interventions. Methods Using longitudinal data from 337 adolescents at high familial risk of depression, we developed a risk prediction model for adolescent MDD. The model was externally validated in an independent cohort of 1,384 adolescents at high familial risk. We assessed predictors at baseline and MDD at follow‐up (a median of 2–3 years later). We compared the risk prediction model to a simple comparison model based on screening for depressive symptoms. Decision curve analysis was used to identify which model‐predicted risk score thresholds were associated with the greatest clinical benefit. Results The MDD risk prediction model discriminated between those adolescents who did and did not develop MDD in the development ( C ‐statistic = .783, IQR (interquartile range) = .779, .778) and the validation samples ( C ‐statistic = .722, IQR = −.694, .741). Calibration in the validation sample was good to excellent (calibration intercept = .011, C ‐slope = .851). The MDD risk prediction model was superior to the simple comparison model where discrimination was no better than chance ( C ‐statistic = .544, IQR = .536, .572). Decision curve analysis found that the highest clinical utility was at the lowest risk score thresholds (0.01–0.05). Conclusions The developed risk prediction model successfully discriminated adolescents who developed MDD from those who did not. In practice, this model could be further developed with user involvement into a tool to target individuals for low‐intensity, selective preventive intervention.