Finding a disease’s genetic basis can help guide therapy and lead to new medicines, but these findings frequently come about years after the illness has taken a toll on a patient’s life. In a recent study, researchers at Children’s Hospital of Philadelphia (CHOP) employed artificial intelligence and machine learning to scan through medical data and clinical notes in order to correlate symptoms with certain hereditary epilepsies. Their study’s findings may greatly shorten the time it takes to diagnose and treat patients.
Their findings were published in the journal Genetics in Medicine.
Numerous treatments are being developed to target the genes causing the more than 100 epilepsies that have been linked to a single gene mutation. It might take a while for genetic testing to validate a specific genetic epilepsy, though. For instance, signs of one of the most prevalent hereditary epilepsies, Dravet Syndrome, can be seen as early as 6 to 9 months of age, yet the average age of diagnosis is 4.2 years. Due to ongoing concerns about cost and accessibility, researchers must devise strategies for accelerating diagnosis times and expanding diagnosis availability.
Previous research conducted by the Epilepsy Genetics Initiative (ENGIN) at CHOP—one of the nation’s largest programs for epilepsy genetics, having evaluated over 5,000 individuals—has shown that standardised data from Electronic Medical Records can be used to study clinical data at huge scales and more accurately predict the onset of epilepsy based on symptoms rather than relying only on a confirmed genetic diagnosis. Using these previously established methods as a foundation, the goal of this investigation was to find early clinical characteristics that would point to a genetic epilepsy diagnosis.
We wanted to determine whether the type of information captures in electronic medical records prior to genetic testing could provide clinicians with clues for a later diagnosis,
In this instance, we found that a wide range of genetic epilepsies have key clinical features that present prior to genetic testing and diagnosis.
Peter D. Galer
From 4,572,783 clinical notes belonging to 32,112 individuals with childhood epilepsy—1,925 of whom had known or suspected genetic epilepsies—the researchers extracted 89 million timestamped clinical annotations using Natural Language Processing, an AI-driven standard technique for processing clinical information from text in EMRs. A median of 3.6 years passed before the diagnoses were verified by a genetic test in 47,774 age-dependent correlations of clinical characteristics with hereditary epilepsies that the researchers discovered. The cohort yielded a total of 710 genetic aetiologies, and within that group, neurodevelopmental abnormalities between 6 and 9 months of age raised the probability of a subsequent genetic diagnosis fivefold.
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By examining a very large dataset of individuals with childhood epilepsies, we believe that our results could be used prospectively for new diagnose. Since most clinicians use Electronic Medical Records, we believe this system could be widely adapted and utilized even in patient populations where genetic testing is not immediately available after symptom onset.
In the era of precision medicine, quicker, more accurate prognoses could make an enormous difference in the lives of individuals living with genetic epilepsies.
Ingo Helbig, MD
Source: Children’s Hospital of Philadelphia – News
Journal Reference: Galer, Peter D., et al. “Clinical Signatures of Genetic Epilepsies Precede Diagnosis in Electronic Medical Records of 32,000 Individuals.” Genetics in Medicine, 2024, p. 101211, DOI: https://doi.org/10.1016/j.gim.2024.101211.
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