Faculty Bibliography

Seizures present in childhood with infinite diversity. History alone may suffice for diagnosis in some cases; more often additional evidence is needed to clarify events of concern. Electroencephalography (EEG) is a primary methodology used for seizure identification and management. Pediatric and adolescent health care providers are increasingly asked to make decisions about when and how to refer patients for eventual monitoring and must then be able to confidently interpret any resulting report(s). Comprehensive literature review was undertaken to provide a succinct and up-to-date overview aimed at general and subspecialty non-neurologist pediatric and adolescent health care providers to not only convey a solid general understanding of EEG and what it entails for patients and their families, but also foster a deeper understanding of the indications for monitoring-and how to interpret documented findings. In plain language this resultant guide reviews EEG basics, provides a crash course in the various types of EEG available, discusses broad indications for epilepsy monitoring, guides counseling and management for patients and their families both before and after EEG, and ultimately aids in the interpretation of both findings and prognosis. This review should allow both primary and subspecialty non-neurologic pediatric and adolescent health care providers to better identify when and how to best utilize EEG as part of a larger comprehensive clinical approach, distinguishing and managing both epileptic and nonepileptic disorders of concern while fostering communication across providers to facilitate and coordinate better holistic long-term care of pediatric and adolescent patients.

OBJECTIVE:Sudden unexpected death in epilepsy (SUDEP) is an important cause of mortality in epilepsy. However, there is a gap in how often providers counsel patients about SUDEP. One potential solution is to electronically prompt clinicians to provide counseling via automated detection of risk factors in electronic medical records (EMRs). We evaluated (1) the feasibility and generalizability of using regular expressions to identify risk factors in EMRs and (2) barriers to generalizability. METHODS:Data included physician notes for 3000 patients from one medical center (home) and 1000 from five additional centers (away). Through chart review, we identified three SUDEP risk factors: (1) generalized tonic-clonic seizures, (2) refractory epilepsy, and (3) epilepsy surgery candidacy. Regular expressions of risk factors were manually created with home training data, and performance was evaluated with home test and away test data. Performance was evaluated by sensitivity, positive predictive value, and F-measure. Generalizability was defined as an absolute decrease in performance by <0.10 for away versus home test data. To evaluate underlying barriers to generalizability, we identified causes of errors seen more often in away data than home data. To demonstrate how small revisions can improve generalizability, we removed three "boilerplate" standard text phrases from away notes and repeated performance. RESULTS:We observed high performance in home test data (F-measure range = 0.86-0.90), and low to high performance in away test data (F-measure range = 0.53-0.81). After removing three boilerplate phrases, away performance improved (F-measure range = 0.79-0.89) and generalizability was achieved for nearly all measures. The only significant barrier to generalizability was use of boilerplate phrases, causing 104 of 171 errors (61%) in away data. SIGNIFICANCE/CONCLUSIONS:Regular expressions are a feasible and probably a generalizable method to identify variables related to SUDEP risk. Our methods may be implemented to create large patient cohorts for research and to generate electronic prompts for SUDEP counseling.

Identifying individuals with rare epilepsy syndromes in electronic data sources is difficult, in part because of missing codes in the International Classification of Diseases (ICD) system. Our objectives were the following: (1) to describe the representation of rare epilepsies in other medical vocabularies, to identify gaps; and (2) to compile synonyms and associated terms for rare epilepsies, to facilitate text and natural language processing tools for cohort identification and population-based surveillance. We describe the representation of 33 epilepsies in 3 vocabularies: Orphanet, SNOMED-CT, and UMLS-Metathesaurus. We compiled terms via 2 surveys, correspondence with parent advocates, and review of web resources and standard vocabularies. UMLS-Metathesaurus had entries for all 33 epilepsies, Orphanet 32, and SNOMED-CT 25. The vocabularies had redundancies and missing phenotypes. Emerging epilepsies (SCN2A-, SCN8A-, KCNQ2-, SLC13A5-, andSYNGAP-related epilepsies) were underrepresented. Survey and correspondence respondents included 160 providers, 375 caregivers, and 11 advocacy group leaders. Each epilepsy syndrome had a median of 15 (range 6-28) synonyms. Nineteen had associated terms, with a median of 4 (range 1-41). We conclude that medical vocabularies should fill gaps in representation of rare epilepsies to improve their value for epilepsy research. We encourage epilepsy researchers to use this resource to develop tools to identify individuals with rare epilepsies in electronic data sources.