A new computational approach to autism screening utilizing digital biomarkers shows promise

Researchers at the University of Chicago have developed a novel computational approach that can reliably predict an eventual diagnosis of autism spectrum disorder (ASD) in young children, without the need for additional blood work or procedures, using only diagnostic codes from past doctor’s visits. The new approach reportedly reduces the number of false-positive ASD diagnoses produced by traditional screening methods by half.

The study, a collaboration between the ZeD Lab and University of Chicago developmental pediatricians Dr. Michael E. Msall, MD, and Dr. Peter J. Smith, MD, was published on October 6 in Science Advances.

Using only sequences of ICD9 and ICD10 (International Classification of Diseases) diagnostic codes generated from past doctor’s visits, which are available for any consenting patient, the researchers were able to leverage known comorbidities of ASD to reliably predict an eventual positive diagnosis.

The researchers’ new algorithm determines an autism comorbid risk score (ACoR), which estimates the risk that a child with a given timeline of diagnoses will eventually receive a confirmed ASD diagnosis. The research applied advances in medical informatics to over 30 million de-identified diagnostic sequences representing over 15,000 distinct ICD codes, originating from the Truven Health Analytics and University of Chicago Medical Center (UCM) databases. The team separated these profiles into positive (i.e., an official ASD diagnosis) and controls. They then applied algorithms that “learned” patterns representative of ASD-positive cohorts compared to ASD-negative cohorts. This research strategy allowed them to find which disease categories contribute to the ACoR and how much each category contributes.

When these risk scores are calculated for an individual patient, the researchers can quantify how far their unique diagnostic timeline deviates from either the positive or control group. When that risk score crosses a threshold, a patient can be flagged as possibly requiring interventions.

By several standard metrics, ACoR outperformed the commonly used questionnaire-based M-CHAT/F screening method, as well as other methods that have made use of comorbidity patterns, including a higher probability that a flagged patient will receive a confirmed ASD diagnosis. Importantly, the researchers were able to flag patients as at-risk more than one year earlier than their actual diagnosis.

Importantly, ACoR performed consistently well for different racial and ethnic groups and even in U.S. counties where diagnostic resources are scarce. “A lot of what we have done is take the data and processes available in better-connected systems and apply them to less well-supported healthcare communities. We know for instance, that African American individuals are frequently diagnosed later, which has an impact on long-term care. This type of technology could overcome some of these structural barriers,” said Smith.

None of this should be taken to mean that the old paradigm for autism screening should be thrown out. Despite their exciting results, the researchers see this new tool as complementary to methods like M-CHAT/F. In fact, when used in tandem with the screening results from M-CHAT/F, ACoR performs even better. There is no substitute for the expertise of a trained specialist or the careful observation of parents, but when 85% of the patients flagged by M-CHAT/F are false positives, objective, data-driven approaches like ACoR, which can be administered even without the patient being present, can fill in the gaps sometimes created by the subjectivity of other measures. The ZeD lab hopes to see their tool widely adopted so that it can reduce the number of false positive patients, who would still need to undergo subsequent screening, and cut down the waiting times for families seeking care for their children.

The study, “Reduced false positives in autism screening via digital biomarkers inferred from deep comorbidity patterns”, was supported by the Defense Advanced Research Projects Agency (DARPA) project number HR00111890043/P00004 . Additional authors include Yi Huang and James van Horne, both of the University of Chicago.

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