Health
Electronic medical records improve early screening for autism
Recent studies show that electronic health record (EHR)-based detection of autism may lead to improved accuracy in early autism screening.
If diagnosed early, children and families are more likely to receive the resources they need to manage autism. Although the American Academy of Pediatrics recommends universal screening for children aged 18 months to her 24 months, the current median age at first diagnosis is her 50 months. This leaves most children with autism diagnosed too late to benefit from early support.
The Infant Autism Modified Checklist with Follow-Up (M-CHAT-F) and its revisions are the most common tools for early autism diagnosis used in children aged 16-30 months. M-CHAT-F has a sensitivity of 39% and a positive predictive value (PPV) of 15%.
Other tools have shown improved sensitivity and PPV, but a need for improved accuracy has been observed with these tools. New approaches are needed to reduce the biases that lead to diagnostic discrepancies that exist in current methods.
EHR data have shown promising results for early and accurate diagnosis. To assess how the inclusion of EHR data might affect early autism screening models, the researchers conducted a diagnostic study from August 1, 2020 to April 2, 2022. Did. Data were collected from the Duke University Health System (DUHS) EHR.
Inclusion criteria included being born between October 1, 2006 and December 1, 2019, having at least one documented encounter with DUHS before age 30 days, and having no DUHS before age 1 year. included at least two recorded encounters with The participant’s demographic information was also collected from her EHR field.
Computable phenotypes were used to identify autism spectrum disorders (ASD) and other neurodevelopmental disorders in participants. To be defined as a case, a patient must have a documented condition code on at least 2 of her separate days, and an encounter with her DUHS clinic, which specializes in neurodevelopmental disorders. was required to have a state code associated with the
The predictive model used the EHR of patients at 30, 60, 90, 180, 270, and 360 days of age. A development set was constructed for model training and hyperparameter tuning, and model performance was analyzed on the test set. Data were randomly split into one of these two sets.
Performance was determined by receiver operating characteristic curves, mean PPV, and concordance index.
The study involved 45,080 children, of whom 924 met criteria for ASD. In addition, her 175 patients with one diagnostic code associated with autism but not meeting the criteria were included in the secondary analysis. Another 10,782 of her participants met criteria for another neurodevelopmental disorder, and 33,374 were used as control participants.
At 30 days, the EHR detection model showed 45.5% sensitivity and 23% PPV at 90% sensitivity. This changed to his PPV of 17.6% with a sensitivity of 59.8% and a sensitivity of 81.5% by day 360. These are meaningful levels of accuracy, making the EHR a potentially valuable tool for accurate early autism screening.
reference
Engelhard MM, Henao R, Berchuck SI, et al. Predictive value of an autism early detection model based on electronic health record data collected before age 1 year. JAMA net opened. 2023;6(2):e2254303.doi:10.1001/jamanetworkopen.2022.54303
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