The evolution of antiseizure medication therapy selection in adults: Is artificial intelligence -assisted antiseizure medication selection ready for prime time?

Antiseizure medications (ASMs) are the mainstay of symptomatic epilepsy treatment. The primary goal of pharmacotherapy with ASMs in epilepsy is to achieve complete seizure remission while minimizing therapy-related adverse events. Over the years, more ASMs have been introduced, with approximately 30 now in everyday use. With such a wide variety, much guidance is needed in choosing ASMs for initial therapy, subsequent replacement monotherapy, or adjunctive therapy. The specific ASMs are typically tailored by the patient's related factors, including epilepsy syndrome, age, sex, comorbidities, and ASM characteristics, including the spectrum of efficacy, pharmacokinetic properties, safety, and tolerability. Weighing these key clinical variables requires experience and expertise that may be limited. Furthermore, with this approach, patients may endure multiple trials of ineffective treatments before the most appropriate ASM is found. A more reliable way to predict response to different ASMs is needed so that the most effective and tolerated ASM can be selected. Soon, alternative approaches, such as deep machine learning (ML), could aid the individualized selection of the first and subsequent ASMs. The recognition of epilepsy as a network disorder and the integration of personalized epilepsy networks in future ML platforms can also facilitate the prediction of ASM response. Augmenting the conventional approach with artificial intelligence (AI) opens the door to personalized pharmacotherapy in epilepsy. However, more work is needed before these models are ready for primetime clinical practice.

Association of intracranial abnormalities with the development of epilepsy and drug-resistant epilepsy in patients with Parry-Romberg syndrome.

BACKGROUND: Epilepsy represents an essential component of Parry Romberg syndrome (PRS). This study aimed to identify clinical factors that influence the development of epilepsy and drug-resistant epilepsy (DRE) in PRS. METHODS: We retrospectively reviewed the medical records of eighty patients with PRS. Data including the age of onset for PRS, history of seizures, use and timing of immunotherapy, antiseizure medication use, and EEG and brain imaging findings were reviewed. For comparison with the patients with epilepsy (PRSe+) group, we selected 18 age and sex-matched controls from the patient without epilepsy (PRSe-) cohort using propensity score matching. RESULTS: Eighteen (22.5%) had epilepsy: 12 were female, and the median age was 14.5 years (range = 6-48 years). Eleven patients developed DRE. The median latency between the onset of cutaneous manifestations and diagnosis and timing and use of immunotherapy was similar between the PRSe + and PRSe- groups. Intracranial abnormalities were commonly seen in the PRSe + group (16 vs. 2, p < 0.01). White matter disease and ipsilateral atrophy were common among the PRSe + group. Timing and use of immunotherapy, epileptiform discharges, and brain imaging abnormalities did not differ between those with DRE and without. CONCLUSIONS: The presence and degree of severity of ipsilateral brain abnormalities are risk factors for the development of epilepsy in PRS but not factors in predicting drug resistance. The timing of immunotherapy did not influence the development of PRSe + or DRE. Prospective studies are needed to identify biomarkers for epilepsy and assess the role of immunotherapy on seizure outcomes in PRSe + .