Development of clinical prediction rule for diagnosis of autistic spectrum disorder in children

Abstract

Purpose: This study aims to develop a clinical prediction rule for the diagnosis of autistic spectrum disorder (ASD) in children.

Design/methodology/approach: This population-based study was carried out in children aged 2 to 5 years who were suspected of having ASD. Data regarding demographics, risk factors, histories taken from caregivers and clinical observation of ASD symptoms were recorded before specialists assessed patients using standardized diagnostic tools. The predictors were analyzed by multivariate logistic regression analysis and developed into a predictive model.

Findings: An ASD diagnosis was rendered in 74.8 per cent of 139 participants. The clinical prediction rule consisted of five predictors, namely, delayed speech for their age, history of rarely making eye contact or looking at faces, history of not showing off toys or favorite things, not following clinician's eye direction and low frequency of social interaction with the clinician or the caregiver. At four or more predictors, sensitivity was 100 per cent for predicting a diagnosis of ASD, with a positive likelihood ratio of 16.62.

Originality/value: This practical clinical prediction rule would help general practitioners to initially diagnose ASD in routine clinical practice.

Keywords: Autism; Autistic; Autistic spectrum disorder; Diagnosis; Prediction.