Common terms for rare epilepsies: Synonyms, associated terms, and links to structured vocabularies

Population-based study of rare epilepsy incidence in a US urban population

OBJECTIVE

This study was undertaken to estimate incidence of rare epilepsies and compare with literature.

METHODS

We used electronic health record text search to identify children with 28 rare epilepsies in New York City (2010-2014). We estimated cumulative incidence and compared with literature.

RESULTS

Eight of 28 rare epilepsies had five or more prior estimates, and our measurements were within the published range for all. The most common were infantile epileptic spasms syndrome (1 in 2920 live births), Lennox-Gastaut syndrome (1 in 9690), and seizures associated with tuberous sclerosis complex (1 in 14 300). Fifteen of 28 had fewer than five prior estimates, and of these, we provided additional estimates for early infantile developmental and epileptic encephalopathy (1 in 32 700), epilepsy with myoclonic-atonic seizures (1 in 34 100), Sturge-Weber syndrome plus seizures/epilepsy (1 in 40 900), epilepsy in infancy with migrating focal seizures (1 in 54 500), Aicardi syndrome plus seizures/epilepsy (1 in 71 600), hypothalamic hamartoma with seizures (1 in 225 000), and Rasmussen syndrome (1 in 450 000). Five of 28 rare epilepsies had no prior estimates, and of these, we provided a new estimate for developmental/epileptic encephalopathy with spike-and-wave activation in sleep and/or continuous spikes and waves during sleep (1 in 34 100). Data were limited for the remaining 12 rare epilepsies, which were all genetic epilepsies, including PCDH19, CDKL5, Alpers disease, SCN8A, KCNQ2, SCN2A, GLUT1 deficiency, Phelan-McDermid syndrome, myoclonic epilepsy with ragged-red fibers, dup15q syndrome, ring chromosome 14, and ring chromosome 20.

SIGNIFICANCE

We estimated the incidence of rare epilepsies using population-based electronic health record data and literature review. More research is needed to better estimate the incidence of genetic epilepsies with nonspecific clinical features. Electronic health records may be a valuable data source for studying rare epilepsies and other rare diseases, particularly as genetic testing becomes more widely adopted.

Creating rare epilepsy cohorts using keyword search in electronic health records

OBJECTIVE

Administrative codes to identify people with rare epilepsies in electronic health records are limited. The current study evaluated the use of keyword search as an alternative method for rare epilepsy cohort creation using electronic health records data.

METHODS

Data included clinical notes from encounters with International Classification of Diseases, Ninth Revision (ICD-9) codes for seizures, epilepsy, and/or convulsions during 2010-2014, across six health care systems in New York City. We identified cases with rare epilepsies by searching clinical notes for keywords associated with 33 rare epilepsies. We validated cases via manual chart review. We compared the performance of keyword search to manual chart review using positive predictive value (PPV), sensitivity, and F-score. We selected an initial combination of keywords using the highest F-scores.

RESULTS

Data included clinical notes from 77 924 cases with ICD-9 codes for seizures, epilepsy, and/or convulsions. The all-keyword search method identified 6095 candidates, and manual chart review confirmed that 2068 (34%) had a rare epilepsy. The initial combination method identified 1862 cases with a rare epilepsy, and this method performed as follows: PPV median = .64 (interquartile range [IQR] = .50-.81, range = .20-1.00), sensitivity median = .93 (IQR = .76-1.00, range = .10-1.00), and F-score median = .71 (IQR = .63-.85, range = .18-1.00). Using this method, we identified four cohorts of rare epilepsies with over 100 individuals, including infantile spasms, Lennox-Gastaut syndrome, Rett syndrome, and tuberous sclerosis complex. We identified over 50 individuals with two rare epilepsies that do not have specific ICD-10 codes for cohort creation (epilepsy with myoclonic atonic seizures, Sturge-Weber syndrome).

SIGNIFICANCE

Keyword search is an effective method for cohort creation. These findings can improve identification and surveillance of individuals with rare epilepsies and promote their referral to specialty clinics, clinical research, and support groups.

Multicenter Assessment of Sturge-Weber Syndrome: A Retrospective Study of Variations in Care and Use of Natural History Data

BACKGROUND

We summarize the history of individuals with Sturge-Weber syndrome (SWS) to inform clinical trial design and identify variations in care.

METHODS

We performed retrospective chart review of individuals with SWS from centers in New York City. We characterized data quality using a novel scoring system. For 13 clinical concepts, we evaluated if data were present and if they were of high quality.

RESULTS

We included 26 individuals with SWS (58% female; median age at initial visit 7 years; absolute range 1 month to 56 years]). Twenty-two had nevus flammeus, 13 glaucoma, four homonymous hemianopia, and 15 hemiparesis. Nineteen of 21 had at least one confirmed seizure with a known first seizure date, all before 24 months. Most (18 of 26, 69%) epilepsy was controlled. A plurality (10 of 23, 43%) had either normal cognitive function or mild cognitive delays. Aspirin use varied by site (P = 0.02)-at four sites, use was 0% (zero of three), 0% (zero of four), 80% (four of five), and 64% (nine of 14). Data were present for more than 75% of cases for 11 of 13 clinical concepts (missing: age of diagnosis, age of glaucoma onset). There were gaps in level of detail for motor impairments, glaucoma severity, seizure history, cognition, and medication history.

CONCLUSIONS

Clinical charts have important gaps in the level of detail around core SWS clinical features, limiting value for some natural history studies. Any clinical trial in SWS designed to prevent epilepsy should begin in the first year of life. Variations in use of aspirin suggest de facto clinical equipoise and warrant a comparative effectiveness study.

Pediatric Neurology Research in the Twenty-First Century: Status, Challenges, and Future Directions Post-COVID-19

BACKGROUND

The year 2020 marked a fundamental shift in the pediatric neurology field. An impressive positive trajectory of advances in patient care and research faced sudden global disruptions by the coronavirus disease 2019 pandemic and by an international movement protesting racial, socioeconomic, and health disparities. The disruptions revealed obstacles and fragility within the pediatric neurology research mission. However, renewed commitment offers unique opportunities for the pediatric neurology research community to enhance and prioritize research directions for the coming decades.

METHODS

The Research Committee of the Child Neurology Society evaluated the challenges and opportunities facing the pediatric neurology research field, including reviewing published literature, synthesizing publically available data, and conducting a survey of pediatric neurologists.

RESULTS

We identified three priority domains for the research mission: funding levels, active guidance, and reducing disparities. Funding levels: to increase funding to match the burden of pediatric neurological disease; to tailor funding mechanisms and strategies to support clinical trial efforts unique to pediatric neurology; and to support investigators across their career trajectory. Active guidance: to optimize infrastructure and strategies, to leverage novel therapeutics, enhance data collection, and improve inclusion of children in clinical trials. Reducing disparities: to reduce health disparities in children with neurological disease, to develop proactive measures to enhance workforce diversity and inclusion, and increase avenues to balance work-life obligations for investigators.

CONCLUSIONS

In this uniquely challenging epoch, the pediatric neurology research community has a timely and important mission to re-engage the public and government, advancing the health of children with neurological conditions.

CDC-supported epilepsy surveillance and epidemiologic studies: A review of progress since 1994

To report progress, to identify gaps, and to plan epilepsy surveillance and research activities more effectively, the Centers for Disease Control and Prevention (CDC) Epilepsy Program has summarized findings from selected CDC-supported surveillance and epidemiologic studies about epilepsy from 1994 through 2019. We identified publications supported by CDC funding and publications conducted by the CDC Epilepsy Program alone or with partners. We included only epilepsy surveillance and epidemiologic studies focusing on epilepsy burden, epilepsy-related outcomes, and healthcare utilization. We describe the findings of these studies in the following order: 1)prevalence; 2)incidence; 3)epilepsy-related outcomes by selected demographic characteristics; 4)cysticercosis or neurocysticercosis (NCC); 5)traumatic brain injury (TBI); 6)comorbidity; 7)mortality; 8)access to care; 9)quality of care; and 10) cost. We have characterized these findings in relation to the scope of the first three domains of the 2012 Institute of Medicine report on epilepsy and its relevant first four recommendations. From 1994 through 2019, 76 publications on epilepsy-related epidemiologic and surveillance studies were identified. Over the past 25 years, CDC has expanded community, state, and national surveillance on epilepsy and supported epidemiologic studies by using multiple assessment methods and validated case-ascertainment criteria to identify epilepsy burden, epilepsy-related outcomes, and healthcare utilization in the general population or in population subgroups. Among identified research opportunities, studies on epilepsy incidence and risk factors, mortality, and cost are considered as important surveillance gaps. Other remaining gaps and suggested surveillance strategies are also proposed. Findings from this review may help epilepsy researchers and other stakeholders reference and prioritize future activities for epidemiologic and surveillance studies in epilepsy.

Disruption of the sodium-dependent citrate transporter SLC13A5 in mice causes alterations in brain citrate levels and neuronal network excitability in the hippocampus

In addition to tissues such as liver, the plasma membrane sodium-dependent citrate transporter, NaCT (SLC13A5), is highly expressed in brain neurons, but its function is not understood. Loss-of-function mutations in the human SLC13A5 gene have been associated with severe neonatal encephalopathy and pharmacoresistant seizures. The molecular mechanisms of these neurological alterations are not clear. We performed a detailed examination of a Slc13a5 deletion mouse model including video-EEG monitoring, behavioral tests, and electrophysiologic, proteomic, and metabolomic analyses of brain and cerebrospinal fluid. The experiments revealed an increased propensity for epileptic seizures, proepileptogenic neuronal excitability changes in the hippocampus, and significant citrate alterations in the CSF and brain tissue of Slc13a5 deficient mice, which may underlie the neurological abnormalities. These data demonstrate that SLC13A5 is involved in brain citrate regulation and suggest that abnormalities in this regulation can induce seizures. The present study is the first to (i) establish the Slc13a5-knockout mouse model as a helpful tool to study the neuronal functions of NaCT and characterize the molecular mechanisms by which functional deficiency of this citrate transporter causes epilepsy and impairs neuronal function; (ii) evaluate all hypotheses that have previously been suggested on theoretical grounds to explain the neurological phenotype of SLC13A5 mutations; and (iii) indicate that alterations in brain citrate levels result in neuronal network excitability and increased seizure propensity.

A propositional AI system for supporting epilepsy diagnosis based on the 2017 epilepsy classification: Illustrated by Dravet syndrome

PURPOSE

The 2017 epilepsy and seizure diagnosis framework emphasizes epilepsy syndromes and the etiology-based approach. We developed a propositional artificial intelligence (AI) system based on the above concepts to support physicians in the diagnosis of epilepsy.

METHODS

We analyzed and built ontology knowledge for the classification of seizure patterns, epilepsy, epilepsy syndrome, and etiologies. Protégé ontology tool was applied in this study. In order to enable the system to be close to the inferential thinking of clinical experts, we classified and constructed knowledge of other epilepsy-related knowledge, including comorbidities, epilepsy imitators, epilepsy descriptors, characteristic electroencephalography (EEG) findings, treatments, etc. We used the Ontology Web Language with Description Logic (OWL-DL) and Semantic Web Rule Language (SWRL) to design rules for expressing the relationship between these ontologies.

RESULTS

Dravet syndrome was taken as an illustration for epilepsy syndromes implementation. We designed an interface for the physician to enter the various characteristics of the patients. Clinical data of an 18-year-old boy with epilepsy was applied to the AI system. Through SWRL and reasoning engine Drool's execution, we successfully demonstrate the process of differential diagnosis.

CONCLUSION

We developed a propositional AI system by using the OWL-DL/SWRL approach to deal with the complexity of current epilepsy diagnosis. The experience of this system, centered on the clinical epilepsy syndromes, paves a path to construct an AI system for further complicated epilepsy diagnosis.

Automated detection of sudden unexpected death in epilepsy risk factors in electronic medical records using natural language processing

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

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.