What are we optimizing for in autism screening? Examination of algorithmic changes in the M‐CHAT

Abstract The present study objectives were to examine the performance of the new M‐CHAT‐R algorithm to the original M‐CHAT algorithm. The main purpose was to examine if the algorithmic changes increase identification of children later diagnosed with ASD, and to examine if there is a trade‐off when changing algorithms. We included 54,463 screened cases from the Norwegian Mother and Child Cohort Study. Children were screened using the 23 items of the M‐CHAT at 18 months. Further, the performance of the M‐CHAT‐R algorithm was compared to the M‐CHAT algorithm on the 23‐items. In total, 337 individuals were later diagnosed with ASD. Using M‐CHAT‐R algorithm decreased the number of correctly identified ASD children by 12 compared to M‐CHAT, with no children with ASD screening negative on the M‐CHAT criteria subsequently screening positive utilizing the M‐CHAT‐R algorithm. A nonparametric McNemar's test determined a statistically significant difference in identifying ASD utilizing the M‐CHAT‐R algorithm. The present study examined the application of 20‐item MCHAT‐R scoring criterion to the 23‐item MCHAT. We found that this resulted in decreased sensitivity and increased specificity for identifying children with ASD, which is a trade‐off that needs further investigation in terms of cost‐effectiveness. However, further research is needed to optimize screening for ASD in the early developmental period to increase identification of false negatives.