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In the past two decades, several screening instruments were developed to detect toddlers who may be autistic both in clinical and unselected samples. Among others, the Quantitative CHecklist for Autism in Toddlers (Q-CHAT) is a quantitative and normally distributed measure of autistic traits that demonstrates good psychometric properties in different settings and cultures. Recently, machine learning (ML) has been applied to behavioral science to improve the classification performance of autism screening and diagnostic tools, but mainly in children, adolescents, and adults. In this study, we used ML to investigate the accuracy and reliability of the Q-CHAT in discriminating young autistic children from those without. Five different ML algorithms (random forest (RF), naïve Bayes (NB), support vector machine (SVM), logistic regression (LR), and K-nearest neighbors (KNN)) were applied to investigate the complete set of Q-CHAT items. Our results showed that ML achieved an overall accuracy of 90%, and the SVM was the most effective, being able to classify autism with 95% accuracy. Furthermore, using the SVM–recursive feature elimination (RFE) approach, we selected a subset of 14 items ensuring 91% accuracy, while 83% accuracy was obtained from the 3 best discriminating items in common to ours and the previously reported Q-CHAT-10. This evidence confirms the high performance and cross-cultural validity of the Q-CHAT, and supports the application of ML to create shorter and faster versions of the instrument, maintaining high classification accuracy, to be used as a quick, easy, and high-performance tool in primary-care settings.

Background: The Quantitative Checklist for Autism in Toddlers (Q-CHAT) is parent-report screening questionnaire for detecting threshold and sub-threshold autistic features in toddlers. The Q-CHAT is a dimensional measure normally distributed in the general population sample and is able to differentiate between a group of children with a diagnosis of autism and unselected toddlers. Objectives: We aim to investigate the psychometric properties, score distribution, and external validity of the Q-CHAT in an Italian clinical sample of young children with autism versus children with developmental delay and typically developing children. Method: N = 126 typically developing children (TD), n = 139 children with autism, and n = 50 children presenting developmental delay (DD) were administered the Q-CHAT. Standardized measures of cognitive functions, language, and behaviors were also obtained. Results: The Q-CHAT scores were normally distributed and demonstrated adequate internal consistency and good item to total score correlations. The mean Q-CHAT score in the autism group was significantly higher than those found in the DD sample and TD children. No difference on the mean Q-CHAT score between DD and TD children was found. The accuracy of the Q-CHAT to discriminate between autism and TD was very good. Two different cut-points (27 and 31, respectively) maximized sensitivity and specificity for autism versus TD and DD, respectively. Finally, higher Q-CHAT scores were correlated with lower language and social communication skills. Conclusions: In clinical settings, the Q-CHAT demonstrated good psychometric properties and external validity to discriminate autism children not just from children with typical development but also from children with developmental delay.

Background In the past decade, several screening instruments have been developed to detect toddlers at risk for autism, both in clinical and unselected samples.We aimed to assess the validity and cross-cultural stability of the Quantitative CHecklist for Autism in Toddlers (Q−CHAT), a quantitative and normally distributed measure of autistic traits, during routine paediatric developmental surveillance in a large Italian community of toddlers. Methods A group of n = 2400 unselected Italian toddlers were screened by paediatricians during routine well−CHild visits between 18 and 24 months,in three Italian regions, representative of the North, Centre and South of Italy. The psychometric properties and factor structure of the Q−CHAT were explored and compared with published literature from Asia and the UK. Results The total Q−CHAT scores in our sample confirmed a normal distribution. Mean Q−CHAT scores across regions were consistent with those reported in UK studies and likewise, a gender effect was found, with boys scoring higher than girls.We also found a remarkable consistency with the three-factor structure of the Asian Q−CHAT. Internal consistency was acceptable for both the Q−CHAT total score and the three factors. Conclusions Our results confirm the dimensional distribution of autistic traits and related symptoms in unselected populations across different cultures. The Q−CHAT is a valid and reliable dimensional screening instrument to be used within a routine paediatric setting during well-child assessments.The main normative data and factor structure replicated in our sample