Predicting Uncertain Multi-Dimensional Adulthood Outcomes From Childhood and Adolescent Data in People Referred to Autism Services

Gordon Forbes; Catherine Lord; Rebecca Elias; Andrew Pickles

doi:10.3389/fpsyg.2021.594462

Predicting Uncertain Multi-Dimensional Adulthood Outcomes From Childhood and Adolescent Data in People Referred to Autism Services

Gordon Forbes^*, Catherine Lord, Rebecca Elias, Andrew Pickles

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

4 Citations (Scopus)

Abstract

Introduction: Autism spectrum disorder is a highly heterogeneous diagnosis. When a child is referred to autism services or receives a diagnosis of autism spectrum disorder it is not known what their potential adult outcomes could be. We consider the challenge of making predictions of an individual child’s long-term multi-facetted adult outcome, focussing on which aspects are predictable and which are not. Methods: We used data from 123 adults participating in the Autism Early Diagnosis Cohort. Participants were recruited from age 2 and followed up repeatedly through childhood and adolescence to adulthood. We predicted 14 adult outcome measures including cognitive, behavioral and well-being measures. Continuous outcomes were modeled using lasso regression and ordinal outcomes were modeled using proportional odds regression. Optimism corrected predictive performance was calculated using cross-validation or bootstrap. We also illustrated the prediction of an overall composite formed by weighting outcome measures by priorities elicited from parents. Results: We found good predictive performance from age 9 for verbal and non-verbal IQ, and daily living skills. Predictions for symptom severity, hyperactivity and irritability improved with inclusion of behavioral data collected in adolescence but remained modest. For other outcomes covering well-being, depression, and positive and negative affect we found no ability to predict adult outcomes at any age. Predictions of composites based on parental priorities differed in magnitude and precision depending on which parts of the adult outcome were given more weight. Conclusion: Verbal and non-verbal IQ, and daily living skills can be predicted well from assessments made in childhood. For other adult outcomes, it is challenging to make meaningful predictions from assessments made in childhood and adolescence using the measures employed in this study. Future work should replicate and validate the present findings in different samples, investigate whether the availability of different measures in childhood and adolescence can improve predictions, and consider systematic differences in priorities.

Original language	English
Article number	594462
Journal	Frontiers in Psychology
Volume	12
DOIs	https://doi.org/10.3389/fpsyg.2021.594462
Publication status	Published - 9 Feb 2021

Keywords

adult outcomes
autism spectrum disorder
childhood
early diagnosis cohort
prediction

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10.3389/fpsyg.2021.594462

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@article{0f7bc4c8d7b74400b972d1dc0fc80447,

title = "Predicting Uncertain Multi-Dimensional Adulthood Outcomes From Childhood and Adolescent Data in People Referred to Autism Services",

abstract = "Introduction: Autism spectrum disorder is a highly heterogeneous diagnosis. When a child is referred to autism services or receives a diagnosis of autism spectrum disorder it is not known what their potential adult outcomes could be. We consider the challenge of making predictions of an individual child{\textquoteright}s long-term multi-facetted adult outcome, focussing on which aspects are predictable and which are not. Methods: We used data from 123 adults participating in the Autism Early Diagnosis Cohort. Participants were recruited from age 2 and followed up repeatedly through childhood and adolescence to adulthood. We predicted 14 adult outcome measures including cognitive, behavioral and well-being measures. Continuous outcomes were modeled using lasso regression and ordinal outcomes were modeled using proportional odds regression. Optimism corrected predictive performance was calculated using cross-validation or bootstrap. We also illustrated the prediction of an overall composite formed by weighting outcome measures by priorities elicited from parents. Results: We found good predictive performance from age 9 for verbal and non-verbal IQ, and daily living skills. Predictions for symptom severity, hyperactivity and irritability improved with inclusion of behavioral data collected in adolescence but remained modest. For other outcomes covering well-being, depression, and positive and negative affect we found no ability to predict adult outcomes at any age. Predictions of composites based on parental priorities differed in magnitude and precision depending on which parts of the adult outcome were given more weight. Conclusion: Verbal and non-verbal IQ, and daily living skills can be predicted well from assessments made in childhood. For other adult outcomes, it is challenging to make meaningful predictions from assessments made in childhood and adolescence using the measures employed in this study. Future work should replicate and validate the present findings in different samples, investigate whether the availability of different measures in childhood and adolescence can improve predictions, and consider systematic differences in priorities.",

keywords = "adult outcomes, autism spectrum disorder, childhood, early diagnosis cohort, prediction",

author = "Gordon Forbes and Catherine Lord and Rebecca Elias and Andrew Pickles",

note = "Funding Information: We would like to thank the participants and families for the time they have given to this project, Marisela Huerta, Lauren Pepa, Niki Bahri, Katherine Byrne, Gabrielle Gunin, Kyle Frost, James McCauley, and Allison Megale for their assistance in adult data collection, and Emily Simonoff, Vicky Slonims, Issy Yorke, and the parents of the IAMHealth Advisory panel. Funding. GF and AP were partially supported by the NIHR NF-SI-0617-10120. AP was supported by the Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King?s College London. The views expressed are those of the authors and not necessarily those of the UK NHS, NIHR or the Department of Health and Social Care. Eliciting of priorities from parents was supported by NIHR grant NIHR RP-PG-1211-20016. The EDX study was funded by National Institute of Child Health and Development (NICHD), R01HD081199 (PI: CL) and the National Institute of Mental Health, R01MH081873 (PI: CL). Funding Information: GF and AP were partially supported by the NIHR NF-SI-0617-10120. AP was supported by the Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King{\textquoteright}s College London. The views expressed are those of the authors and not necessarily those of the UK NHS, NIHR or the Department of Health and Social Care. Eliciting of priorities from parents was supported by NIHR grant NIHR RP-PG-1211-20016. The EDX study was funded by National Institute of Child Health and Development (NICHD), R01HD081199 (PI: CL) and the National Institute of Mental Health, R01MH081873 (PI: CL). Publisher Copyright: {\textcopyright} Copyright {\textcopyright} 2021 Forbes, Lord, Elias and Pickles. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.",

year = "2021",

month = feb,

day = "9",

doi = "10.3389/fpsyg.2021.594462",

language = "English",

volume = "12",

journal = "Frontiers in Psychology",

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T1 - Predicting Uncertain Multi-Dimensional Adulthood Outcomes From Childhood and Adolescent Data in People Referred to Autism Services

AU - Forbes, Gordon

AU - Lord, Catherine

AU - Elias, Rebecca

AU - Pickles, Andrew

N1 - Funding Information: We would like to thank the participants and families for the time they have given to this project, Marisela Huerta, Lauren Pepa, Niki Bahri, Katherine Byrne, Gabrielle Gunin, Kyle Frost, James McCauley, and Allison Megale for their assistance in adult data collection, and Emily Simonoff, Vicky Slonims, Issy Yorke, and the parents of the IAMHealth Advisory panel. Funding. GF and AP were partially supported by the NIHR NF-SI-0617-10120. AP was supported by the Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King?s College London. The views expressed are those of the authors and not necessarily those of the UK NHS, NIHR or the Department of Health and Social Care. Eliciting of priorities from parents was supported by NIHR grant NIHR RP-PG-1211-20016. The EDX study was funded by National Institute of Child Health and Development (NICHD), R01HD081199 (PI: CL) and the National Institute of Mental Health, R01MH081873 (PI: CL). Funding Information: GF and AP were partially supported by the NIHR NF-SI-0617-10120. AP was supported by the Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. The views expressed are those of the authors and not necessarily those of the UK NHS, NIHR or the Department of Health and Social Care. Eliciting of priorities from parents was supported by NIHR grant NIHR RP-PG-1211-20016. The EDX study was funded by National Institute of Child Health and Development (NICHD), R01HD081199 (PI: CL) and the National Institute of Mental Health, R01MH081873 (PI: CL). Publisher Copyright: © Copyright © 2021 Forbes, Lord, Elias and Pickles. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2021/2/9

Y1 - 2021/2/9

N2 - Introduction: Autism spectrum disorder is a highly heterogeneous diagnosis. When a child is referred to autism services or receives a diagnosis of autism spectrum disorder it is not known what their potential adult outcomes could be. We consider the challenge of making predictions of an individual child’s long-term multi-facetted adult outcome, focussing on which aspects are predictable and which are not. Methods: We used data from 123 adults participating in the Autism Early Diagnosis Cohort. Participants were recruited from age 2 and followed up repeatedly through childhood and adolescence to adulthood. We predicted 14 adult outcome measures including cognitive, behavioral and well-being measures. Continuous outcomes were modeled using lasso regression and ordinal outcomes were modeled using proportional odds regression. Optimism corrected predictive performance was calculated using cross-validation or bootstrap. We also illustrated the prediction of an overall composite formed by weighting outcome measures by priorities elicited from parents. Results: We found good predictive performance from age 9 for verbal and non-verbal IQ, and daily living skills. Predictions for symptom severity, hyperactivity and irritability improved with inclusion of behavioral data collected in adolescence but remained modest. For other outcomes covering well-being, depression, and positive and negative affect we found no ability to predict adult outcomes at any age. Predictions of composites based on parental priorities differed in magnitude and precision depending on which parts of the adult outcome were given more weight. Conclusion: Verbal and non-verbal IQ, and daily living skills can be predicted well from assessments made in childhood. For other adult outcomes, it is challenging to make meaningful predictions from assessments made in childhood and adolescence using the measures employed in this study. Future work should replicate and validate the present findings in different samples, investigate whether the availability of different measures in childhood and adolescence can improve predictions, and consider systematic differences in priorities.

AB - Introduction: Autism spectrum disorder is a highly heterogeneous diagnosis. When a child is referred to autism services or receives a diagnosis of autism spectrum disorder it is not known what their potential adult outcomes could be. We consider the challenge of making predictions of an individual child’s long-term multi-facetted adult outcome, focussing on which aspects are predictable and which are not. Methods: We used data from 123 adults participating in the Autism Early Diagnosis Cohort. Participants were recruited from age 2 and followed up repeatedly through childhood and adolescence to adulthood. We predicted 14 adult outcome measures including cognitive, behavioral and well-being measures. Continuous outcomes were modeled using lasso regression and ordinal outcomes were modeled using proportional odds regression. Optimism corrected predictive performance was calculated using cross-validation or bootstrap. We also illustrated the prediction of an overall composite formed by weighting outcome measures by priorities elicited from parents. Results: We found good predictive performance from age 9 for verbal and non-verbal IQ, and daily living skills. Predictions for symptom severity, hyperactivity and irritability improved with inclusion of behavioral data collected in adolescence but remained modest. For other outcomes covering well-being, depression, and positive and negative affect we found no ability to predict adult outcomes at any age. Predictions of composites based on parental priorities differed in magnitude and precision depending on which parts of the adult outcome were given more weight. Conclusion: Verbal and non-verbal IQ, and daily living skills can be predicted well from assessments made in childhood. For other adult outcomes, it is challenging to make meaningful predictions from assessments made in childhood and adolescence using the measures employed in this study. Future work should replicate and validate the present findings in different samples, investigate whether the availability of different measures in childhood and adolescence can improve predictions, and consider systematic differences in priorities.

KW - adult outcomes

KW - autism spectrum disorder

KW - childhood

KW - early diagnosis cohort

KW - prediction

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U2 - 10.3389/fpsyg.2021.594462

DO - 10.3389/fpsyg.2021.594462

M3 - Article

AN - SCOPUS:85101213747

SN - 1664-1078

VL - 12

JO - Frontiers in Psychology

JF - Frontiers in Psychology

M1 - 594462

ER -

Predicting Uncertain Multi-Dimensional Adulthood Outcomes From Childhood and Adolescent Data in People Referred to Autism Services

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Keywords

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