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Development of a probability calculator for psychosis risk in children, adolescents, and young adults

Published online by Cambridge University Press:  12 January 2021

Tyler M. Moore*
Affiliation:
Department of Psychiatry, Brain Behavior Laboratory, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104, USA Lifespan Brain Institute, Penn Medicine and Children's Hospital of Philadelphia, Philadelphia, PA19104, USA
Monica E. Calkins
Affiliation:
Department of Psychiatry, Brain Behavior Laboratory, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104, USA Lifespan Brain Institute, Penn Medicine and Children's Hospital of Philadelphia, Philadelphia, PA19104, USA
Adon F. G. Rosen
Affiliation:
Department of Psychiatry, Brain Behavior Laboratory, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104, USA
Ellyn R. Butler
Affiliation:
Department of Psychiatry, Brain Behavior Laboratory, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104, USA
Kosha Ruparel
Affiliation:
Department of Psychiatry, Brain Behavior Laboratory, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104, USA Lifespan Brain Institute, Penn Medicine and Children's Hospital of Philadelphia, Philadelphia, PA19104, USA
Paolo Fusar-Poli
Affiliation:
Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK OASIS service, South London and Maudsley NHS Foundation Trust, London, UK Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
Nikolaos Koutsouleris
Affiliation:
Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University Munich, Germany
Philip McGuire
Affiliation:
Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
Tyrone D. Cannon
Affiliation:
Departments of Psychology and Psychiatry, Yale University, New Haven, CT06520, USA
Ruben C. Gur
Affiliation:
Department of Psychiatry, Brain Behavior Laboratory, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104, USA Lifespan Brain Institute, Penn Medicine and Children's Hospital of Philadelphia, Philadelphia, PA19104, USA
Raquel E. Gur
Affiliation:
Department of Psychiatry, Brain Behavior Laboratory, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104, USA Lifespan Brain Institute, Penn Medicine and Children's Hospital of Philadelphia, Philadelphia, PA19104, USA
*
Author for correspondence: Tyler M. Moore, E-mail: tymoore@pennmedicine.upenn.edu

Abstract

Background

Assessment of risks of illnesses has been an important part of medicine for decades. We now have hundreds of ‘risk calculators’ for illnesses, including brain disorders, and these calculators are continually improving as more diverse measures are collected on larger samples.

Methods

We first replicated an existing psychosis risk calculator and then used our own sample to develop a similar calculator for use in recruiting ‘psychosis risk’ enriched community samples. We assessed 632 participants age 8–21 (52% female; 48% Black) from a community sample with longitudinal data on neurocognitive, clinical, medical, and environmental variables. We used this information to predict psychosis spectrum (PS) status in the future. We selected variables based on lasso, random forest, and statistical inference relief; and predicted future PS using ridge regression, random forest, and support vector machines.

Results

Cross-validated prediction diagnostics were obtained by building and testing models in randomly selected sub-samples of the data, resulting in a distribution of the diagnostics; we report the mean. The strongest predictors of later PS status were the Children's Global Assessment Scale; delusions of predicting the future or having one's thoughts/actions controlled; and the percent married in one's neighborhood. Random forest followed by ridge regression was most accurate, with a cross-validated area under the curve (AUC) of 0.67. Adjustment of the model including only six variables reached an AUC of 0.70.

Conclusions

Results support the potential application of risk calculators for screening and identification of at-risk community youth in prospective investigations of developmental trajectories of the PS.

Type
Original Article
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press

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Footnotes

*

These authors contributed equally to this work.

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