CHOP Researchers Harness Medical Record Data to Improve Epilepsy Care

The Helbig Lab harnesses electronic medical record data to improve care for children with genetic epilepsies.

By Lauren Ingeno

When Ingo Helbig, MD, began working in the United States in 2015, he was struck by the "ubiquity and richness" of what he found in the electronic medical records (EMR) of patients with epileptic seizures. While notes from a single patient visit provided a brief snapshot of the overall clinical picture, when the researcher generated a plot of the patient's clinical diagnoses over two decades, a much more interesting story emerged.

"This brief pilot experiment was so impressive to me that I started to focus my research group on making information from the EMR usable as a tool to better understand genetic epilepsies," wrote Dr. Helbig, a pediatric neurologist in the Division of Neurology at Children's Hospital of Philadelphia and a core faculty member of the Epilepsy NeuroGenetics Initiative (ENGIN) Frontier Program, in a blog post.

With new funding from the National Institutes of Health, the Helbig Lab at CHOP will be investigating how to better use medical record and genetic data in order to understand the genetic underpinnings of pediatric epilepsies and neurodevelopmental disorders. The research will build the foundation for natural history studies of rare diseases over the next five years.

Defining a disease's "natural history" — its natural trajectory throughout a patient's lifetime, in the absence of treatment — is critical for identifying and interpretating interventions that may or may not alter its course.

However, some diseases are so rare that their natural histories have yet to be described —including up to a third of epilepsy cases that are caused by a genetic predisposition. That's partly because there are hundreds of genes associated with pediatric epilepsy, among others that have yet to be discovered.

"If you have a novel drug or gene therapy approach, how do you meaningfully measure its efficacy in a clinical trial?" said Julie Xian, a data scientist in the Helbig Lab, and an incoming MD/PhD student at the Johns Hopkins Medical Scientist Training Program. "You first need a baseline understanding of the natural disease history, in order to then assess whether the treatment strategy reduces symptoms and to what degree it alters the disease trajectory,"

While the availability of large amounts of clinical data is opening up new possibilities for tracking and understanding the progression of epilepsy, as well as other neurological diseases, big data is also difficult to work with, and it is only as resourceful as the scientists who know what to do with it.

To overcome this challenge, Dr. Helbig and his research team have spent years developing new frameworks to handle and extrapolate patterns from large amounts of EMR data. It's a field he's named, "EMR genomics."

The Helbig Lab's two NIH-funded research projects have an overall goal of refining these frameworks to improve clinical care for patients with childhood epilepsies: The first will aim to link clinical presentations with specific genetic etiologies; while the second will delineate longitudinal trajectories and outcomes in genetic epilepsies.

"Having a standardized language and framework allows us to map and harmonize heterogeneous clinical data found across medical records, past studies in the literature, and institutions at scale," Xian said.

Xian, who joined Dr. Helbig's laboratory while she was an undergraduate at the University of Pennsylvania in 2019, and a team of researchers have already trialed the approach by studying neurodevelopmental disorders caused by variants in the STXBP1 gene, which is among the most common etiologies for genetic epilepsies and neurodevelopmental disorders. Their study, published in the journal Brain in 2021, included nearly 20,000 clinical markers across 534 patients with STXBP1-related disorders.

In order to find clinical patterns within the vast clinical diversity of the disease, the team expanded on its work on the Human Phenotype Ontology (HPO) — a dictionary of more than 13,000 phenotypic terms with defined relationships that enables harmonization of heterogeneous clinical data at scale. This framework enabled the team to discover emerging genotype-phenotype correlations that have previously been unrecognized in a disorder with such a perplexing range of phenotypes, and it challenged past studies on STXBP1-related disorders. The research team also reported that seizure frequency in the first year of life is dynamic within this condition, and the efficacy of anti-seizure medications is dependent on a patient's age and seizure type.

Research findings from the two new NIH projects will build upon that study to better stratify subgroups of genetic epilepsies. This work will build a scalable framework that will ultimately help to diagnose and treat many other rare disorders, the researchers said.

"By looking at phenotypic data at scale, it adds another dimension, and we'll be able to paint a more comprehensive picture of these conditions," Xian said. "Dr. Helbig and his team are pioneering an entirely new way of approaching phenotype science, and I can't wait to see where this takes us in the future."