A Nomogram for Predicting ADHD and ASD in Child and Adolescent Mental Health Services (CAMHS).
J Clin Med
; 13(8)2024 Apr 19.
Article
en En
| MEDLINE
| ID: mdl-38673670
ABSTRACT
Objectives:
To enhance the early detection of Attention Deficit/Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) by leveraging clinical variables collected at child and adolescent mental health services (CAMHS).Methods:
This study included children diagnosed with ADHD and/or ASD (n = 857). Three logistic regression models were developed to predict the presence of ADHD, its subtypes, and ASD. The analysis began with univariate logistic regression, followed by a multicollinearity diagnostic. A backward logistic regression selection strategy was then employed to retain variables with p < 0.05. Ethical approval was obtained from the local ethics committee. The models' internal validity was evaluated based on their calibration and discriminative abilities.Results:
The study produced models that are well-calibrated and validated for predicting ADHD (incorporating variables such as physical activity, history of bone fractures, and admissions to pediatric/psychiatric services) and ASD (including disability, gender, special education needs, and Axis V diagnoses, among others).Conclusions:
Clinical variables can play a significant role in enhancing the early identification of ADHD and ASD.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Idioma:
En
Revista:
J Clin Med
Año:
2024
Tipo del documento:
Article
País de afiliación:
España