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- Title
Predictive Value of Early Autism Detection Models Based on Electronic Health Record Data Collected Before Age 1 Year.
- Authors
Engelhard, Matthew M.; Henao, Ricardo; Berchuck, Samuel I.; Chen, Junya; Eichner, Brian; Herkert, Darby; Kollins, Scott H.; Olson, Andrew; Perrin, Eliana M.; Rogers, Ursula; Sullivan, Connor; Zhu, YiQin; Sapiro, Guillermo; Dawson, Geraldine
- Abstract
Key Points: Question: Can autism be detected from routine electronic health records (EHRs) with clinically meaningful accuracy before age 1 year? Findings: In this diagnostic study of 45 080 children, the accuracy of EHR-based early autism detection models at age 30 days was competitive with caregiver surveys collected at ages 18 to 24 months. Model accuracy improved further by age 1 year. Meaning: These findings suggest that EHR-based autism detection could be integrated with caregiver surveys to improve the accuracy of early autism screening. This diagnostic study evaluates the accuracy of early autism detection models based on electronic health record data collected before age 1 year. Importance: Autism detection early in childhood is critical to ensure that autistic children and their families have access to early behavioral support. Early correlates of autism documented in electronic health records (EHRs) during routine care could allow passive, predictive model-based monitoring to improve the accuracy of early detection. Objective: To quantify the predictive value of early autism detection models based on EHR data collected before age 1 year. Design, Setting, and Participants: This retrospective diagnostic study used EHR data from children seen within the Duke University Health System before age 30 days between January 2006 and December 2020. These data were used to train and evaluate L2-regularized Cox proportional hazards models predicting later autism diagnosis based on data collected from birth up to the time of prediction (ages 30-360 days). Statistical analyses were performed between August 1, 2020, and April 1, 2022. Main Outcomes and Measures: Prediction performance was quantified in terms of sensitivity, specificity, and positive predictive value (PPV) at clinically relevant model operating thresholds. Results: Data from 45 080 children, including 924 (1.5%) meeting autism criteria, were included in this study. Model-based autism detection at age 30 days achieved 45.5% sensitivity and 23.0% PPV at 90.0% specificity. Detection by age 360 days achieved 59.8% sensitivity and 17.6% PPV at 81.5% specificity and 38.8% sensitivity and 31.0% PPV at 94.3% specificity. Conclusions and Relevance: In this diagnostic study of an autism screening test, EHR-based autism detection achieved clinically meaningful accuracy by age 30 days, improving by age 1 year. This automated approach could be integrated with caregiver surveys to improve the accuracy of early autism screening.
- Subjects
DIAGNOSIS of autism; PREDICTIVE tests; ACADEMIC medical centers; RETROSPECTIVE studies; MEDICAL screening; AUTOMATION; CHILD psychopathology; RESEARCH funding; ELECTRONIC health records; PREDICTION models; SENSITIVITY &; specificity (Statistics); RECEIVER operating characteristic curves; EARLY diagnosis; PROPORTIONAL hazards models
- Publication
JAMA Network Open, 2023, Vol 6, Issue 1, pe2254303
- ISSN
2574-3805
- Publication type
Article
- DOI
10.1001/jamanetworkopen.2022.54303