Establishment of an objective index for the diagnosis of attention deficit/hyperactivity disorder by the continuous performance test "MOGRAZ".

Attention deficit/hyperactivity disorder Continuous performance test MOGRAZ Multiple logistic regression analysis Prediction model

Journal

Brain & development
ISSN: 1872-7131
Titre abrégé: Brain Dev
Pays: Netherlands
ID NLM: 7909235

Informations de publication

Date de publication:
Nov 2022
Historique:
received: 02 07 2021
revised: 08 07 2022
accepted: 08 07 2022
pubmed: 26 7 2022
medline: 11 11 2022
entrez: 25 7 2022
Statut: ppublish

Résumé

The diagnosis of attention deficit/hyperactivity disorder (AD/HD) in Japan is mainly based on information obtained from caregivers. There is therefore a need to establish an objectivity index that can be easily used in clinical practice. The purpose of the study was to create a predictive model for the diagnosis of AD/HD using the MOGRAZ, a visual continuous performance test developed in Japan. We collected data from an AD/HD group and a non-AD/HD group. The AD/HD group included 75 children with predominantly inattentive type AD/HD and 48 with combined type AD/HD who were aged 6 to 12 years and diagnosed at our department. The non-AD/HD group included 153 Japanese children aged 6 to 11 years enrolled in regular classes at a public elementary school. In both groups, multiple logistic regression analysis was performed using the results of MOGRAZ, age, and sex as parameters, and algorithms for a predictive diagnostic model of AD/HD were created. The area under the receiver operating characteristic curve (ROC-AUC) between the predominantly inattentive type AD/HD subgroup and non-AD/HD group was 0.884 (95% confidence interval: 0.837-0.932), and the ROC-AUC between the combined type AD/HD subgroup and non-AD/HD group was 0.914 (95% CI: 0.869-0.959). The prediction model using the MOGRAZ score allowed us to create an objectivity index to determine the diagnosis of AD/HD that can be easily used in clinical practice. We plan additional verification of this prediction model with additional participants.

Identifiants

pubmed: 35879141
pii: S0387-7604(22)00115-2
doi: 10.1016/j.braindev.2022.07.002
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

664-671

Informations de copyright

Copyright © 2022 The Japanese Society of Child Neurology. Published by Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Tsunehiko Kurokami (T)

Department of Psychosocial Medicine, National Center for Child Health and Development, Tokyo, Japan; Department of Pediatrics and Developmental Biology, Tokyo Medical and Dental University, Tokyo, Japan. Electronic address: kurokami-t@ncchd.go.jp.

Hisao Kobayashi (H)

Emeritus Professor, Saitama University, Saitama, Japan.

Moe Nakajima (M)

Department of Psychosocial Medicine, National Center for Child Health and Development, Tokyo, Japan.

Masashi Mikami (M)

Biostatistics Unit, National Center for Child Health and Development, Tokyo, Japan.

Tatsuya Koeda (T)

Department of Psychosocial Medicine, National Center for Child Health and Development, Tokyo, Japan.

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