Algorithm developed by CHOP researchers helps differentiate ADHD from similar conditions

PHILADELPHIA (KYW Newsradio) — Researchers at the Children’s Hospital of Philadelphia developed an algorithm to help define the differences between attention-deficit/hyperactivity disorder (ADHD) symptoms and other disorders.

ADHD affects between about 5% and 8% of school-age children and 2% to 4% of adults, according to CHOP. It comes in three different types, exists on a spectrum, and can be mimicked by other conditions, thus sometimes making it difficult to diagnose.

Complicating diagnoses even more, learning or sleep disorders affect about half of patients with ADHD, which makes it hard to determine whether a patient’s symptoms are caused directly by ADHD or one of the other conditions.

“Mood disorder, Tourette [syndrome], early psychotic conditions or even schizophrenia can sometimes simulate,” said Hakon Hakonarson, director of the Center for Applied Genomics at CHOP and senior author of the study.

“The diagnosis is usually missed and diluted by other conditions, and the diagnosis is not given properly,” he continued.

His team developed an algorithm using existing electronic health records to help distinguish the differences. They performed a case study using more than 51,000 patient records. Of that sample, about 46% of people diagnosed with ADHD had solely the disorder, while 54% had ADHD and at least one other psychiatric condition.

The algorithm is in the early stages of development, but the researchers found it has a positive predictive value of 95% for ADHD, suggesting very accurate testing for prospective diagnoses.

“[We can] utilize the information to try to establish new therapies that can treat the individual with the most relevant medications that address the underlying cause,” Hakonarson added.

Shahiddah Lowe, a Philadelphia-based educator, thinks this tool will allow her to do her job more effectively.

“Depending on whether they are hyperactive or maybe a little inattentive, that gives us an idea of what interventions or accommodations the kid’s going to need in the classroom,” she explained.

Ultimately, researchers believe it will help design more effective methods for therapeutic intervention and more precise clinical trials.