Developing and validating a prediction model of adolescent major depressive disorder in the offspring of depressed parents

Alice Stephens; Judith Allardyce; Bryony Weavers; Jessica Lennon; Rhys Bevan Jones; Victoria Powell; Olga Eyre; Robert Potter; Valentina Escott-Price; David Osborn; Anita Thapar; Stephan Collishaw; Jon E Heron; Frances  Rice

doi:10.1111/jcpp.13704

Developing and validating a prediction model of adolescent major depressive disorder in the offspring of depressed parents

Alice Stephens
, Judith Allardyce
, Bryony Weavers
, Jessica Lennon
, Rhys Bevan Jones
, Victoria Powell
, Olga Eyre
, Robert Potter
, Valentina Escott-Price
, David Osborn
, Anita Thapar
, Stephan Collishaw
, Jon E Heron
, Frances Rice^*

^*Corresponding author for this work

Research output: Contribution to journal › Article (Academic Journal) › peer-review

9 Citations (Scopus)

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Abstract

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.

Original language	English
Number of pages	9
Journal	Journal of Child Psychology and Psychiatry
Early online date	12 Sept 2022
DOIs	https://doi.org/10.1111/jcpp.13704
Publication status	E-pub ahead of print - 12 Sept 2022

Bibliographical note

Funding Information:
The authors are grateful to the families and general practitioners who participated in and assisted with the EPAD study. The authors thank the assistant psychologists who helped with data collection. Robert Potter and Anita Thapar reviewed all cases in which a child had a diagnosis or subthreshold symptoms in the EPAD study and Valerie Russell provided project administration. The authors are extremely grateful to all the ALSPAC families who took part in this study, the midwives for their help in recruiting them and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses. Data collection for the EPAD study was supported by grant JTA/06 from The Sir Jules Thorn Charitable Trust. This project was also supported by funding from the Medical Research Council (MRC) through grant MR/R004609/1. D.O. is supported by the National Institute for Health Research (NIHR) Biomedical Research Centre (BRC) at the University College London Hospitals (UCLH) and the National Institute for Health Research ARC North Thames. J.A. is supported by the Wellcome Trust (209176/Z/17/Z). This publication is the work of the authors, and J.H. and F.R. will serve as guarantors for the contents of this article. The Wolfson Centre for Young People's Mental Health was established with support from the Wolfson Foundation. The UK Medical Research Council and Wellcome (Grant ref: 217065/Z/19/Z) and the University of Bristol provide core support for ALSPAC. A comprehensive list of grants funding is available on the ALSPAC website. Data collection for the variables included in the ALSPAC data in this research was specifically funded by Wellcome Trust and MRC (076467/Z/05/Z) and NIH (5R01MH07384204). The funders of the study had no role in the study design, data collection, data analysis, data interpretation or writing of the report. The authors have declared that they have no competing or potential conflicts of interest. Key points

Publisher Copyright:
© 2022 The Authors. Journal of Child Psychology and Psychiatry published by John Wiley & Sons Ltd on behalf of Association for Child and Adolescent Mental Health.

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Stephens, A., Allardyce, J., Weavers, B., Lennon, J., Bevan Jones, R., Powell, V., Eyre, O., Potter, R., Escott-Price, V., Osborn, D., Thapar, A., Collishaw, S., Heron, J. E., & Rice, F. (2022). Developing and validating a prediction model of adolescent major depressive disorder in the offspring of depressed parents. Journal of Child Psychology and Psychiatry. Advance online publication. https://doi.org/10.1111/jcpp.13704

@article{c065fa666dc345788ab21d29ca640dc3,

title = "Developing and validating a prediction model of adolescent major depressive disorder in the offspring of depressed parents",

abstract = "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{\textendash}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{\textendash}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. ",

author = "Alice Stephens and Judith Allardyce and Bryony Weavers and Jessica Lennon and \{Bevan Jones\}, Rhys and Victoria Powell and Olga Eyre and Robert Potter and Valentina Escott-Price and David Osborn and Anita Thapar and Stephan Collishaw and Heron, \{Jon E\} and Frances Rice",

note = "Funding Information: The authors are grateful to the families and general practitioners who participated in and assisted with the EPAD study. The authors thank the assistant psychologists who helped with data collection. Robert Potter and Anita Thapar reviewed all cases in which a child had a diagnosis or subthreshold symptoms in the EPAD study and Valerie Russell provided project administration. The authors are extremely grateful to all the ALSPAC families who took part in this study, the midwives for their help in recruiting them and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses. Data collection for the EPAD study was supported by grant JTA/06 from The Sir Jules Thorn Charitable Trust. This project was also supported by funding from the Medical Research Council (MRC) through grant MR/R004609/1. D.O. is supported by the National Institute for Health Research (NIHR) Biomedical Research Centre (BRC) at the University College London Hospitals (UCLH) and the National Institute for Health Research ARC North Thames. J.A. is supported by the Wellcome Trust (209176/Z/17/Z). This publication is the work of the authors, and J.H. and F.R. will serve as guarantors for the contents of this article. The Wolfson Centre for Young People's Mental Health was established with support from the Wolfson Foundation. The UK Medical Research Council and Wellcome (Grant ref: 217065/Z/19/Z) and the University of Bristol provide core support for ALSPAC. A comprehensive list of grants funding is available on the ALSPAC website. Data collection for the variables included in the ALSPAC data in this research was specifically funded by Wellcome Trust and MRC (076467/Z/05/Z) and NIH (5R01MH07384204). The funders of the study had no role in the study design, data collection, data analysis, data interpretation or writing of the report. The authors have declared that they have no competing or potential conflicts of interest. Key points Publisher Copyright: {\textcopyright} 2022 The Authors. Journal of Child Psychology and Psychiatry published by John Wiley \& Sons Ltd on behalf of Association for Child and Adolescent Mental Health.",

year = "2022",

month = sep,

day = "12",

doi = "10.1111/jcpp.13704",

language = "English",

journal = "Journal of Child Psychology and Psychiatry",

issn = "0021-9630",

publisher = "Wiley-Blackwell",

}

Stephens, A, Allardyce, J, Weavers, B, Lennon, J, Bevan Jones, R, Powell, V, Eyre, O, Potter, R, Escott-Price, V, Osborn, D, Thapar, A, Collishaw, S, Heron, JE & Rice, F 2022, 'Developing and validating a prediction model of adolescent major depressive disorder in the offspring of depressed parents', Journal of Child Psychology and Psychiatry. https://doi.org/10.1111/jcpp.13704

TY - JOUR

T1 - Developing and validating a prediction model of adolescent major depressive disorder in the offspring of depressed parents

AU - Stephens, Alice

AU - Allardyce, Judith

AU - Weavers, Bryony

AU - Lennon, Jessica

AU - Bevan Jones, Rhys

AU - Powell, Victoria

AU - Eyre, Olga

AU - Potter, Robert

AU - Escott-Price, Valentina

AU - Osborn, David

AU - Thapar, Anita

AU - Collishaw, Stephan

AU - Heron, Jon E

AU - Rice, Frances

N1 - Funding Information: The authors are grateful to the families and general practitioners who participated in and assisted with the EPAD study. The authors thank the assistant psychologists who helped with data collection. Robert Potter and Anita Thapar reviewed all cases in which a child had a diagnosis or subthreshold symptoms in the EPAD study and Valerie Russell provided project administration. The authors are extremely grateful to all the ALSPAC families who took part in this study, the midwives for their help in recruiting them and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses. Data collection for the EPAD study was supported by grant JTA/06 from The Sir Jules Thorn Charitable Trust. This project was also supported by funding from the Medical Research Council (MRC) through grant MR/R004609/1. D.O. is supported by the National Institute for Health Research (NIHR) Biomedical Research Centre (BRC) at the University College London Hospitals (UCLH) and the National Institute for Health Research ARC North Thames. J.A. is supported by the Wellcome Trust (209176/Z/17/Z). This publication is the work of the authors, and J.H. and F.R. will serve as guarantors for the contents of this article. The Wolfson Centre for Young People's Mental Health was established with support from the Wolfson Foundation. The UK Medical Research Council and Wellcome (Grant ref: 217065/Z/19/Z) and the University of Bristol provide core support for ALSPAC. A comprehensive list of grants funding is available on the ALSPAC website. Data collection for the variables included in the ALSPAC data in this research was specifically funded by Wellcome Trust and MRC (076467/Z/05/Z) and NIH (5R01MH07384204). The funders of the study had no role in the study design, data collection, data analysis, data interpretation or writing of the report. The authors have declared that they have no competing or potential conflicts of interest. Key points Publisher Copyright: © 2022 The Authors. Journal of Child Psychology and Psychiatry published by John Wiley & Sons Ltd on behalf of Association for Child and Adolescent Mental Health.

PY - 2022/9/12

Y1 - 2022/9/12

N2 - 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.

AB - 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.

U2 - 10.1111/jcpp.13704

DO - 10.1111/jcpp.13704

M3 - Article (Academic Journal)

C2 - 36096685

SN - 0021-9630

JO - Journal of Child Psychology and Psychiatry

JF - Journal of Child Psychology and Psychiatry

ER -

Developing and validating a prediction model of adolescent major depressive disorder in the offspring of depressed parents

Abstract

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