ABSTRACT
Objective: To develop and validate INCLEN Diagnostic Tool for Attention Deficit Hyperactivity Disorder (INDT-ADHD). Design: Diagnostic test evaluation by cross sectional design. Setting: Tertiary care pediatric centers. Participants: 156 children aged 65-117 months. Methods: After randomization, INDT-ADHD and Connor’s 3 Parent Rating Scale (C3PS) were administered, followed by an expert evaluation by DSM-IV-TR diagnostic criteria. Main outcome measures: Psychometric evaluation of diagnostic accuracy, validity (construct, criterion and convergent) and internal consistency. Results: INDT-ADHD had 18 items that quantified symptoms and impairment. Attention deficit hyperactivity disorder was identified in 57, 87 and 116 children by expert evaluation, INDT-ADHD and C3PS, respectively. Psychometric parameters of INDT-ADHD for differentiating attention deficit hyperactivity disorder and normal children were: sensitivity 87.7%, specificity 97.2%, positive predictive value 98.0% and negative predictive value 83.3%, whereas for differentiating from other neuro-developmental disorders were 87.7%, 42.9%, 58.1% and 79.4%, respectively. Internal consistency was 0.91. INDT-ADHD has a 4-factor structure explaining 60.4% of the variance. Convergent validity with Conner’s Parents Rating Scale was moderate (r =0.73, P= 0.001). Conclusions: INDT-ADHD is suitable for diagnosing attention deficit hyperactivity disorder in Indian children between the ages of 6 to 9 years.
ABSTRACT
Objective: To develop and validate INCLEN Diagnostic Tool for Autism Spectrum Disorder (INDT-ASD). Design: Diagnostic test evaluation by cross sectional design Setting: Four tertiary pediatric neurology centers in Delhi and Thiruvanthapuram, India. Methods: Children aged 2-9 years were enrolled in the study. INDT-ASD and Childhood Autism Rating Scale (CARS) were administered in a randomly decided sequence by trained psychologist, followed by an expert evaluation by DSM-IV TR diagnostic criteria (gold standard). Main outcome measures: Psychometric parameters of diagnostic accuracy, validity (construct, criterion and convergent) and internal consistency. Results: 154 children (110 boys, mean age 64.2 mo) were enrolled. The overall diagnostic accuracy (AUC=0.97, 95% CI 0.93, 0.99; P<0.001) and validity (sensitivity 98%, specificity 95%, positive predictive value 91%, negative predictive value 99%) of INDT-ASD for Autism spectrum disorder were high, taking expert diagnosis using DSM-IV-TR as gold standard. The concordance rate between the INDT-ASD and expert diagnosis for ‘ASD group’ was 82.52% [Cohen’s κ=0.89; 95% CI (0.82, 0.97); P=0.001]. The internal consistency of INDT-ASD was 0.96. The convergent validity with CARS (r = 0.73, P= 0.001) and divergent validity with Binet-Kamat Test of intelligence (r = -0.37; P=0.004) were significantly high. INDT-ASD has a 4-factor structure explaining 85.3% of the variance. Conclusion: INDT-ASD has high diagnostic accuracy, adequate content validity, good internal consistency high criterion validity and high to moderate convergent validity and 4-factor construct validity for diagnosis of Autistm spectrum disorder.