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School nurses play a crucial role in the prompt, appropriate response to epilepsy-related seizure emergencies among students in the school setting. Two intranasal benzodiazepine rescue therapies are now approved and offer potential benefits of being easy to use and socially acceptable. In July 2021, a survey was sent to 49,314 US school nurses to assess knowledge, perceptions, and practice with seizure rescue therapy. Responses were received from 866 (1.8% response rate). Of respondents, 45.7% had used rectal diazepam gel; 9.3%, midazolam nasal spray; and 6.0%, diazepam nasal spray. The majority (58.7%) had not delegated authority to administer rescue therapy, with state/local regulations and lack of willingness of school personnel being the most common barriers to delegation (37.7% and 20.1%, respectively). Additional training of nurses and school staff and progress on delegation policies may help optimize appropriate use of intranasal rescue therapy for seizures and enhance care of students with epilepsy in schools.

We develop an integrative Bayesian predictive modeling framework that identifies individual pathological brain states based on the selection of fluoro-deoxyglucose positron emission tomography (PET) imaging biomarkers and evaluates the association of those states with a clinical outcome. We consider data from a study on temporal lobe epilepsy (TLE) patients who subsequently underwent anterior temporal lobe resection. Our modeling framework looks at the observed profiles of regional glucose metabolism in PET as the phenotypic manifestation of a latent individual pathologic state, which is assumed to vary across the population. The modeling strategy we adopt allows the identification of patient subgroups characterized by latent pathologies differentially associated to the clinical outcome of interest. It also identifies imaging biomarkers characterizing the pathological states of the subjects. In the data application, we identify a subgroup of TLE patients at high risk for post-surgical seizure recurrence after anterior temporal lobe resection, together with a set of discriminatory brain regions that can be used to distinguish the latent subgroups. We show that the proposed method achieves high cross-validated accuracy in predicting post-surgical seizure recurrence.