Seizure prediction using real world data – a learning health system realized

Neonatal seizures. Neonatal seizures can lead to serious consequences for newborns, including long-term morbidity and mortality. In high-resource neonatal intensive care units, screening for seizures with CEEG has become commonplace and is considered standard of care. Accurate seizure prediction can help optimize the allocation of CEEG resources and improve care for critically ill neonates. In our recent study, we aimed to develop seizure prediction models using data extracted from standardized EEG reports. Here is a brief overview of our findings using real-world data to predict seizures in neonates.

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Unlocking STXBP1 through Electronic Medical Records

Understanding the EMR. Several weeks ago, I gave a presentation at the STXBP1 Summit conference, the third annual meeting since the first in 2019 – a time when I had just entered the field of neurogenetics. It has been fascinating to follow one of the neurodevelopmental genes with the “fastest growing knowledge,” with the expanded scope of clinical studies and emergence of novel avenues for targeted gene therapies on the horizon. However, one of the many projects our STXBP1 team is currently working on takes a somewhat atypical approach – we aimed to map the natural disease history of STXBP1-related disorders based entirely on reconstructed Electronic Medical Records (EMR). Here are some of the challenges we have had to confront and what we learned searching for meaning in the depth of the EMR. Continue reading