Seizure Frequency Process and Outcome Quality Measures: Quality Improvement in Neurology

Cognitive screening informs referrals for neuropsychological evaluation in children with epilepsy

OBJECTIVE

This study aimed to evaluate the effectiveness of current neuropsychology referral methods for children with epilepsy and develop data-informed recommendations for use of performance-based cognitive screening measures to improve these processes.

METHODS

Children with epilepsy who had been referred to neuropsychology (n = 51) or had never been referred (n = 34) completed four brief tablet-based screening tests from the National Institutes of Health Toolbox Cognition Battery along with a comprehensive neuropsychological test battery. Demographics, medical information, and parent questionnaires were gathered.

RESULTS

Mean performance on the neuropsychological test battery was worse in the referral group (p = .008, d = .52), but percentage of patients who presented with cognitive impairment (at least two scores 1.5 SD below the mean) did not differ. Demographics did not predict performance on the comprehensive neurocognitive battery (p = .46,R change 2 $$ {R}_{\mathrm{change}}^2 $$  = .06). Medical variables added some predictive value (p = .004,R change 2 $$ {R}_{\mathrm{change}}^2 $$  = .25). Parent questionnaires added minimal value (p = .066,R change 2 $$ {R}_{\mathrm{change}}^2 $$  = .05) beyond the previous variables. Performance on the cognitive screening battery added significant predictive value (p < .001,R change 2 $$ {R}_{\mathrm{change}}^2 $$  = .31) above demographics, medical variables, and parent questionnaires, explaining 31% additional variance in performance on the comprehensive neuropsychological battery. Stepwise analysis suggested that only three screening tests, totaling 15 min of administration time, were necessary. A cutoff score of .70 SD below the mean on any of those screening tests had high sensitivity (.90) while maintaining specificity > .50. A cutoff score of 1 SD below the mean provided better balance of sensitivity (.74) and specificity (.70).

SIGNIFICANCE

Brief and easy to administer performance-based cognitive screening may add value and reduce bias when making decisions about neuropsychology referrals for children with epilepsy. An ideal clinical model could include neuropsychology consultation with chart review, clinical interview, questionnaires, and brief cognitive screening to inform referrals for more comprehensive evaluation. In settings where this is not possible, cognitive screening may be a useful and minimally resource-intensive method for informing referral decisions.

Learning Health Systems and Improvement Science in Neurology

Although the quality movement in healthcare in the United States has been maturing for the last several decades, neurology remains a frontier of work related to learning healthcare systems (LHS) and the science of improvement. This review describes the use of LHS models in neurology and the use of Improvement Science to advance position system changes and improve care. LHSs are broadly understandable, widely supported, and have a developing yet proven track record. However, there are distinct challenges at multiple levels in successful implementation, as well as nuances related to variability in practice patterns and institutions. This review outlines these hurdles and approaches to addressing them. There are examples of effective work currently being conducted in this emerging field, with an emphasis on two subspecialties that have been the primary early adopters of these models and methodology within neurology: stroke and epilepsy. As LHS models take shape in neurology subspecialties, there will be an ongoing need for collaboration and iterative change to support continuous improvement in systems of care and improve outcomes for patients with neurologic disease.

Implementing Guidelines and Measures in Epilepsy Care

ABSTRACT

People with epilepsy must receive up-to-date, high-quality care that aligns with current understanding of basic disease mechanisms, improved diagnostic testing, and evolving medical and surgical treatments. Varying progress has been made in identifying, measuring, and mitigating epilepsy care gaps. Epilepsy guidelines and quality measures should be developed using rigorous processes informed by systematic reviews of best evidence in conjunction with prioritization of need. Epilepsy measures help operationalize guidelines and practice parameters. Most epilepsy quality indicators are process-based metrics defined by delivering care to the patient. Systematic and reliable tracking and documentation of seizure frequency using consistent language is required as a patient-reported outcome within individuals over time and across populations. Emerging literature has demonstrated gaps in epilepsy care, perhaps highlighting limitations in the dissemination and implementation of guidelines and quality measures in clinical practice. Quality improvement methods applied to clinical data registries and learning health systems may afford new opportunities to iteratively, collaboratively, and feasibly disseminate guidelines and quality measures, measure epilepsy care quality, allow for the testing of interventions to mitigate identified care gaps, and, ultimately, improve care for patients with epilepsy.

Collaborative Care to Improve Quality of Life for Anxiety and Depression in Posttraumatic Epilepsy (CoCarePTE): Protocol for a Randomized Hybrid Effectiveness-Implementation Trial

BACKGROUND

Anxiety and depression in people with epilepsy are common and associated with poor outcomes; yet, they often go untreated due to poor mental health specialist access. Collaborative care is an integrated care model with a strong evidence base in primary care and medical settings, but it has not been evaluated in neurology clinics. Evaluating implementation outcomes when translating evidence-based interventions to new clinical settings to inform future scaling and incorporation into real-world practice is important.

OBJECTIVE

The Collaborative Care for Posttraumatic Epilepsy (CoCarePTE) trial aims to evaluate the effectiveness (improvement in emotional quality of life) and implementation of a collaborative care intervention for people with anxiety or depressive symptoms and posttraumatic epilepsy.

METHODS

CoCarePTE is a 2-site, randomized, single-blind, hybrid type 1 effectiveness-implementation trial that will randomize 60 adults to receive either neurology-based collaborative care or usual care. Adults receiving neurological care at participating centers with anxiety or depressive symptoms and a history of at least mild traumatic brain injury before epilepsy onset will be enrolled. The collaborative care intervention is a 24-week stepped-care model with video or telephone calls every 2 weeks by a care manager for measurement-based anxiety and depression care, seizure care monitoring, and brief therapy intervention delivery. This is supplemented by antidepressant prescribing recommendations by psychiatrists for neurologists via case conferences and care manager-facilitated team communication. In step 2 of the intervention, individuals with <50% symptom reduction by 10 weeks will receive an added 8-session remote cognitive behavioral therapy program. The study is powered to detect a moderate improvement in emotional quality of life. As a hybrid type 1 trial, effectiveness is the primary focus, with the primary outcome being a change in emotional quality of life at 6 months in the intervention group compared to control. Secondary effectiveness outcomes are 6-month changes in depression, anxiety, and overall quality of life. Implementation outcomes, including fidelity, acceptability, feasibility, and appropriateness, are evaluated before implementation and at 3 months. The primary effectiveness analysis will compare changes in emotional quality of life scores from baseline to 6 months between the intervention and control arms using multiple linear regression modeling, adjusting for study site and using an intent-to-treat approach.

RESULTS

Enrollment commenced in 2023, with modifications in the inclusion and exclusion made after the first 6 enrollees due to slow recruitment. Enrollment is expected to continue at least into early 2025.

CONCLUSIONS

The CoCarePTE trial is novel in its use of a hybrid effectiveness-implementation design to evaluate an evidence-based mental health intervention in epilepsy, and by incorporating seizure care into a collaborative care model. If a significant improvement in emotional quality of life is found in the intervention group compared to usual care, this would support next step scaling or clinical implementation.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05353452; https://www.clinicaltrials.gov/study/NCT05353452.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/59329.

Current Role of Brivaracetam in the Management of Epilepsy in Adults and Children: A Systematic Review

Epilepsy, a neurological condition, has a devastating effect on the quality of life (QoL) of patients if left untreated. Brivaracetam (BRV), a third-generation antiepileptic drug (AED), acts by modulating synaptic vesicle proteins, making it a valuable addition to the arsenal of drugs for epilepsy management. This study aims to assess the efficacy, safety, and reasons for switching from prior AEDs to BRV in patients with epilepsy. A systematic electronic search was performed in PubMed and Google Scholar for English-language articles published from 1 June 2013 to 2 June 2023 on the safety, efficacy, and behavioral adverse effects (BAEs) of BRV when used as monotherapy, add-on therapy, and after switching from prior AEDs (switch therapy; along with reasons for switching to BRV from prior AEDs in adult and pediatric populations), irrespective of the route of administration. A qualitative assessment was conducted using the Joanna Briggs Institute (JBI) tool. A qualitative synthesis of the data was performed. Sixty-one articles involving a total of 15,186 patients with epilepsy were included for qualitative synthesis. In adults, seizure reduction was reported in 31.4%-72.0%, 4.4%-82.1%, and 6.8%-54.3% of patients; seizure freedom in 12.10%-25.6%, 2.0%-80%, and 6.5%-30.6% of patients; and a responder rate of ≥50% in 30.8%, 21.9%-83.8%, and 16.7%-69.1% of patients with monotherapy, add-on therapy, and after switch therapy, respectively. In the pediatric population, seizure reduction was reported in 39.1%‍-‍62.5% and 21%-59% of patients, seizure freedom in 4.4%-37.5% and 12% of patients, and a responder rate of ≥50% in 19.7%-65% and 21%-45.2% of patients with add-on therapy and after switch therapy, respectively. BAEs such as irritability, mood changes, emotional lability, aggression, and agitation were reported in adults for all types of therapies, while anger was reported with only monotherapy and add-on therapy, hyperactivity with add-on therapy, and agitation with monotherapy and add-on therapy with BRV. In the pediatric population, irritability and aggression were reported with add-on and switch therapies, while emotional lability was reported with only switch therapy with BRV. The reasons for switching to BRV from previous AEDs were lack of efficacy and treatment-related adverse effects (AEs). BRV has a favorable efficacy and safety profile. The drug reduces seizure frequency, provides seizure freedom, and achieves a ≥50% responder rate in adult and pediatric patients with add-on therapy and after switching to BRV from other AEDs. However, there is limited evidence supporting its use as monotherapy.

Extracting seizure control metrics from clinic notes of patients with epilepsy: A natural language processing approach

OBJECTIVES

Monitoring seizure control metrics is key to clinical care of patients with epilepsy. Manually abstracting these metrics from unstructured text in electronic health records (EHR) is laborious. We aimed to abstract the date of last seizure and seizure frequency from clinical notes of patients with epilepsy using natural language processing (NLP).

METHODS

We extracted seizure control metrics from notes of patients seen in epilepsy clinics from two hospitals in Boston. Extraction was performed with the pretrained model RoBERTa_for_seizureFrequency_QA, for both date of last seizure and seizure frequency, combined with regular expressions. We designed the algorithm to categorize the timing of last seizure ("today", "1-6 days ago", "1-4 weeks ago", "more than 1-3 months ago", "more than 3-6 months ago", "more than 6-12 months ago", "more than 1-2 years ago", "more than 2 years ago") and seizure frequency ("innumerable", "multiple", "daily", "weekly", "monthly", "once per year", "less than once per year"). Our ground truth consisted of structured questionnaires filled out by physicians. Model performance was measured using the areas under the receiving operating characteristic curve (AUROC) and precision recall curve (AUPRC) for categorical labels, and median absolute error (MAE) for ordinal labels, with 95 % confidence intervals (CI) estimated via bootstrapping.

RESULTS

Our cohort included 1773 adult patients with a total of 5658 visits with reported seizure control metrics, seen in epilepsy clinics between December 2018 and May 2022. The cohort average age was 42 years old, the majority were female (57 %), White (81 %) and non-Hispanic (85 %). The models achieved an MAE (95 % CI) for date of last seizure of 4 (4.00-4.86) weeks, and for seizure frequency of 0.02 (0.02-0.02) seizures per day.

CONCLUSIONS

Our NLP approach demonstrates that the extraction of seizure control metrics from EHR is feasible allowing for large-scale EHR research.

Hippocampal sclerosis in women with temporal lobe epilepsy: seizure and pregnancy outcomes

BACKGROUND

Temporal lobe epilepsy with hippocampal sclerosis (TLE-HS) is typically resistant to pharmacological interventions; however, achieving seizure freedom is possible through surgery. Our objective was to focus on the pregnancy and seizure outcomes during pregnancy of women with TLE-HS, and aim to identify predictors of seizure control.

METHODS

The West China Registry of Pregnancy of Women with Epilepsy (WCPR_EPi) was a monocentric prospective cohort study of women with epilepsy (WWE). We screened women with TLE-HS in this database. Their clinical profile, anti-seizure medication (ASM) use, and pregnancy outcomes were extracted from the records of the registry (2010-2023).

RESULTS

Out of 2320 WWE followed up, 47 pregnancies in women with TLE-HS were identified and analyzed. Seizure exacerbation occurred in 40.4% of pregnancies, and seizure freedom was present in 34.0% of these during pregnancy. Factors associated with seizure exacerbation during pregnancy was ASM non-adherence (odds ratio [OR] =7.00, 95% confidence interval [CI] 1.43-34.07, P=0.016). The surgery group showed a significantly higher seizure freedom rate (OR = 6.87, 95% CI 1.02-46.23, P=0.016) and lower rate of induced labor (0.0% vs 26.5%, P=0.047) compared to the medically-treated group alone. Caesarean section was chosen in 77.1% of cases due to seizure concerns, with comparable in epilepsy-related (n=20) and obstetric causes (n=24). No major congenital malformations were reported.

CONCLUSIONS

Surgical treatment before pregnancy appears to offer a higher chance of seizure freedom compared to medication alone. Most of women with TLE-HS can deliver healthy offspring regardless of suboptimal seizure control and unwarranted concerns.

About 1.5 million community-dwelling US adults with active epilepsy reported uncontrolled seizures in the past 12 months, and seizure control varied by annual family income-National Health Interview Survey, United States 2021 and 2022

Uncontrolled seizures among people with epilepsy increase risk of adverse health and social outcomes including increased risk of death. Previous population-based studies have reported suboptimal seizure control and disparities in seizure control among U.S. adults with active epilepsy (self-reported doctor-diagnosed epilepsy and taking anti-seizure medicine or with ≥ 1 seizures in the past 12 months) by annual family income. This brief is based upon data from the 2021 and 2022 National Health Interview Survey (NHIS) to provide updated national estimates of the percentages of adults with active epilepsy with and without seizure control (0 seizures in past 12 months) vs. ≥ 1) by anti-seizure medication use and by annual family income. Annual family income was operationalized with NHIS poverty-income ratio (PIR) categories (i.e., total family income divided by the US Census Bureau poverty threshold given the family's size and number of children): PIR < 1.0, 1.0 ≤ PIR < 2.0; PIR ≥ 2.0. Among the 1.1 % of US adults with active epilepsy in 2021/2022 (estimated population about 2.9 million), 49.2 % (∼1.4 million) were taking antiseizure medication and reported no seizures (seizure control), 36.2 % (∼1.1 million) were taking antiseizure medication and reported ≥ 1 seizures (uncontrolled seizures), and 14.7 % (∼400,000) were not taking antiseizure medication and had ≥ 1 seizures (uncontrolled seizures). The prevalence of seizure control among those with active epilepsy varied substantially by annual family income, with a larger percentage of adults with PIR ≥ 2.0 reporting seizure control compared with those with PIR < 1.0. Opportunities for intervention include improving provider awareness of epilepsy treatment guidelines, enhancing access and referral to specialty care, providing epilepsy self-management supports, and addressing unmet social needs of people with epilepsy with uncontrolled seizures, especially those at lowest family income levels.

Development of an International Standard Set of Outcomes and Measurement Methods for Routine Practice for Infants, Children, and Adolescents with Epilepsy: The International Consortium for Health Outcomes Measurement Consensus Recommendations

At present, there is no internationally accepted set of core outcomes or measurement methods for epilepsy clinical practice. The International Consortium for Health Outcomes Measurement (ICHOM) convened an international working group of experts in epilepsy, people with epilepsy, and their representatives to develop minimum sets of standardized outcomes and outcome measurement methods for clinical practice. Using modified Delphi consensus methods with consecutive rounds of online voting over 12 months, a core set of outcomes and corresponding measurement tool packages to capture the outcomes were identified for infants, children, and adolescents with epilepsy. Consensus methods identified 20 core outcomes. In addition to the outcomes identified for the ICHOM Epilepsy adult standard set, behavioral, motor, and cognitive/language development outcomes were voted as essential for all infants and children with epilepsy. The proposed set of outcomes and measurement methods will facilitate the implementation of the use of patient-centered outcomes in daily practice.

Development of an International Standard Set of Outcomes and Measurement Methods for Routine Practice for Adults with Epilepsy: The International Consortium for Health Outcomes Measurement Consensus Recommendations

At present, there is no internationally accepted set of core outcomes or measurement methods for epilepsy clinical practice. Therefore, the International Consortium for Health Outcomes Measurement (ICHOM) convened an international working group of experts in epilepsy, people with epilepsy and their representatives to develop minimum sets of standardized outcomes and outcomes measurement methods for clinical practice that support patient-clinician decision-making and quality improvement. Consensus methods identified 20 core outcomes. Measurement tools were recommended based on their evidence of strong clinical measurement properties, feasibility, and cross-cultural applicability. The essential outcomes included many non-seizure outcomes: anxiety, depression, suicidality, memory and attention, sleep quality, functional status, and the social impact of epilepsy. The proposed set will facilitate the implementation of the use of patient-centered outcomes in daily practice, ensuring holistic care. They also encourage harmonization of outcome measurement, and if widely implemented should reduce the heterogeneity of outcome measurement, accelerate comparative research, and facilitate quality improvement efforts.

Implementing American Academy of Neurology Quality Measures in Antigua Using Quality Improvement Methodology

Background and Objectives

The American Academy of Neurology has developed quality measures related to various neurologic disorders. A gap exists in the implementation of these measures in the different health care systems. To date, there has been no electronic health care record nor implementation of quality measures in Antigua. Therefore, we aimed to increase the percent of patients who have epilepsy quality measures documented using standardized common data elements in the outpatient neurology clinic at Sir Lester Bird Medical Center from 0% to 80% per week by June 1, 2022 and sustain for 6 months.

Methods

We used the Institute for Health care Improvement Model for Improvement methodology. A data use agreement was implemented. Data were displayed using statistical process control charts and the American Society for Quality criteria to determine statistical significance and centerline shifts.

Results

Current and future state process maps were developed to determine areas of opportunity for interventions. Interventions were developed following a "Plan-Do-Study-Act cycle." One intervention was the creation of a RedCap survey and database to be used by health care providers during clinical patient encounters. Because of multiple interventions, we achieved a 100% utilization of the survey for clinical care.

Discussion

Quality improvement (QI) methodology can be used for implementation of quality measures in various settings to improve patient care outcomes without use of significant resources. Implementation of quality measures can increase efficiency in clinical delivery. Similar QI methodology could be implemented in other resource-limited countries of the Caribbean and globally.

Delineating clinical and developmental outcomes in STXBP1-related disorders

STXBP1-related disorders are among the most common genetic epilepsies and neurodevelopmental disorders. However, the longitudinal epilepsy course and developmental end points, have not yet been described in detail, which is a critical prerequisite for clinical trial readiness. Here, we assessed 1281 cumulative patient-years of seizure and developmental histories in 162 individuals with STXBP1-related disorders and established a natural history framework. STXBP1-related disorders are characterized by a dynamic pattern of seizures in the first year of life and high variability in neurodevelopmental trajectories in early childhood. Epilepsy onset differed across seizure types, with 90% cumulative onset for infantile spasms by 6 months and focal-onset seizures by 27 months of life. Epilepsy histories diverged between variant subgroups in the first 2 years of life, when individuals with protein-truncating variants and deletions in STXBP1 (n = 39) were more likely to have infantile spasms between 5 and 6 months followed by seizure remission, while individuals with missense variants (n = 30) had an increased risk for focal seizures and ongoing seizures after the first year. Developmental outcomes were mapped using milestone acquisition data in addition to standardized assessments including the Gross Motor Function Measure-66 Item Set and the Grasping and Visual-Motor Integration subsets of the Peabody Developmental Motor Scales. Quantification of end points revealed high variability during the first 5 years of life, with emerging stratification between clinical subgroups. An earlier epilepsy onset was associated with lower developmental abilities, most prominently when assessing gross motor development and expressive communication. We found that individuals with neonatal seizures or early infantile seizures followed by seizure offset by 12 months of life had more predictable seizure trajectories in early to late childhood compared to individuals with more severe seizure presentations, including individuals with refractory epilepsy throughout the first year. Characterization of anti-seizure medication response revealed age-dependent response over time, with phenobarbital, levetiracetam, topiramate and adrenocorticotropic hormone effective in reducing seizures in the first year of life, while clobazam and the ketogenic diet were effective in long-term seizure management. Virtual clinical trials using seizure frequency as the primary outcome resulted in wide range of trial success probabilities across the age span, with the highest probability in early childhood between 1 year and 3.5 years. In summary, we delineated epilepsy and developmental trajectories in STXBP1-related disorders using standardized measures, providing a foundation to interpret future therapeutic strategies and inform rational trial design.

Long-term epilepsy outcome dynamics revealed by natural language processing of clinic notes

OBJECTIVE

Electronic medical records allow for retrospective clinical research with large patient cohorts. However, epilepsy outcomes are often contained in free text notes that are difficult to mine. We recently developed and validated novel natural language processing (NLP) algorithms to automatically extract key epilepsy outcome measures from clinic notes. In this study, we assessed the feasibility of extracting these measures to study the natural history of epilepsy at our center.

METHODS

We applied our previously validated NLP algorithms to extract seizure freedom, seizure frequency, and date of most recent seizure from outpatient visits at our epilepsy center from 2010 to 2022. We examined the dynamics of seizure outcomes over time using Markov model-based probability and Kaplan-Meier analyses.

RESULTS

Performance of our algorithms on classifying seizure freedom was comparable to that of human reviewers (algorithm F1 = .88 vs. human annotatorκ = .86). We extracted seizure outcome data from 55 630 clinic notes from 9510 unique patients written by 53 unique authors. Of these, 30% were classified as seizure-free since the last visit, 48% of non-seizure-free visits contained a quantifiable seizure frequency, and 47% of all visits contained the date of most recent seizure occurrence. Among patients with at least five visits, the probabilities of seizure freedom at the next visit ranged from 12% to 80% in patients having seizures or seizure-free at the prior three visits, respectively. Only 25% of patients who were seizure-free for 6 months remained seizure-free after 10 years.

SIGNIFICANCE

Our findings demonstrate that epilepsy outcome measures can be extracted accurately from unstructured clinical note text using NLP. At our tertiary center, the disease course often followed a remitting and relapsing pattern. This method represents a powerful new tool for clinical research with many potential uses and extensions to other clinical questions.

The impact of the time to last seizure before admission to the epilepsy monitoring unit (EMU) on epilepsy classifications

INTRODUCTION AND BACKGROUND

The impact of the timing of the last seizure (TTLS) prior to admission to the epilepsy monitoring unit (EMU) on epilepsy classification is unclear for which we conducted this study.

METHODS

We reviewed patients with epilepsy admitted to EMU between January 2021 and April 2022 and identified TTLS before EMU admission. We considered EMU yield as whether; it confirmed epilepsy classification, added new knowledge to the classification, or failed to classify epilepsy.

RESULTS

We studied 156 patients. There were 72 (46%) men, with a mean age of 30. TTLS was divided according to a one- or three-month cutoff. We confirmed the pre-EMU epilepsy classification in 52 (33%) patients, learned new findings on epilepsy classification in 80 (51%) patients, and failed to classify epilepsy in 24 (15%) patients. Patients with "confirmed epilepsy classifications" reported seizures sooner to EMU admission than other groups (0.7 vs. 2.3 months, p-value = 0.02, 95% CI; -1.8, -1.3). Also, the odds of confirming epilepsy classification were more than two times in patients with TTLS within a month compared to those with TTLS of more than a month (OR = 2.4, p-value = 0.04, 95% CI; 1.1, 5.9). The odds were also higher when the 3-month TTLS cutoff was considered (OR = 6.2, p-value = 0.002, 95% CI; 1.6, 40.2). Confirming epilepsy classification was also associated with earlier seizures recorded at one- or three-month cutoff (OR = 2.1 and OR = 2.3, respectively, p-value = 0.05). We did not observe similar findings when we modified the classification or failed to reach a classification.

CONCLUSIONS

The timing of the last seizure before EMU admission appeared to influence the yield of EMU and enhanced the confirmation of epilepsy classifications. Such findings can improve the utilization of EMU in the presurgical evaluation of patients with epilepsy.

Delineating clinical and developmental outcomes in STXBP1-related disorders

STXBP1-related disorders are among the most common genetic epilepsies and neurodevelopmental disorders. However, the longitudinal epilepsy course and developmental endpoints have not yet been described in detail, which is a critical prerequisite for clinical trial readiness. Here, we assessed 1,281 cumulative patient-years of seizure and developmental histories in 162 individuals with STXBP1-related disorders and established a natural history framework. STXBP1-related disorders are characterized by a dynamic pattern of seizures in the first year of life and high variability in neurodevelopmental trajectories in early childhood. Epilepsy onset differed across seizure types, with 90% cumulative onset for infantile spasms by 6 months and focal-onset seizures by 27 months of life. Epilepsy histories diverged between variant subgroups in the first 2 years of life, when individuals with protein-truncating variants and deletions in STXBP1 (n=39) were more likely to have infantile spasms between 5 and 6 months followed by seizure remission, while individuals with missense variants (n=30) had an increased risk for focal seizures and ongoing seizures after the first year. Developmental outcomes were mapped using milestone acquisition data in addition to standardized assessments including the Gross Motor Function Measure-66 Item Set and the Grasping and Visual-Motor Integration subsets of the Peabody Developmental Motor Scales. Quantification of endpoints revealed high variability during the first five years of life, with emerging stratification between clinical subgroups, most prominently between individuals with and without infantile spasms. We found that individuals with neonatal seizures or early infantile seizures followed by seizure offset by 12 months of life had more predictable seizure trajectories in early to late childhood than compared to individuals with more severe seizure presentations, including individuals with refractory epilepsy throughout the first year. Characterization of anti-seizure medication response revealed age-dependent response over time, with phenobarbital, levetiracetam, topiramate, and adrenocorticotropic hormone effective in reducing seizures in the first year of life, while clobazam and the ketogenic diet were effective in long-term seizure management. Virtual clinical trials using seizure frequency as the primary outcome resulted in wide range of trial success probabilities across the age span, with the highest probability in early childhood between 1 year and 3.5 years. In summary, we delineated epilepsy and developmental trajectories in STXBP1-related disorders using standardized measures, providing a foundation to interpret future therapeutic strategies and inform rational trial design.

Development of a core outcome set for quality of life for adults with drug-resistant epilepsy: A multistakeholder Delphi consensus study

OBJECTIVE

In 2017, the American Academy of Neurology (AAN) convened the AAN Quality Measurement Set working group to define the improvement and maintenance of quality of life (QOL) as a key outcome measure in epilepsy clinical practice. A core outcome set (COS), defined as an accepted, standardized set of outcomes that should be minimally measured and reported in an area of health care research and practice, has not previously been defined for QOL in adult epilepsy.

METHODS

A cross-sectional Delphi consensus study was employed to attain consensus from patients and caregivers on the QOL outcomes that should be minimally measured and reported in epilepsy clinical practice. Candidate items were compiled from QOL scales recommended by the AAN 2017 Quality Measurement Set. Inclusion criteria to participate in the Delphi study were adults with drug-resistant epilepsy diagnosed by a physician, no prior diagnosis of psychogenic nonepileptic seizures or a cognitive and/or developmental disability, or caregivers of patients meeting these criteria.

RESULTS

A total of 109 people satisfied inclusion/exclusion criteria and took part in Delphi Round 1 (patients, n = 95, 87.2%; caregivers, n = 14, 12.8%), and 55 people from Round 1 completed Round 2 (patients, n = 43, 78.2%; caregivers, n = 12, 21.8%). One hundred three people took part in the final consensus round. Consensus was attained by patients/caregivers on a set of 36 outcomes that should minimally be included in the QOL COS. Of these, 32 of the 36 outcomes (88.8%) pertained to areas outside of seizure frequency and severity.

SIGNIFICANCE

Using patient-centered Delphi methodology, this study defines the first COS for QOL measurement in clinical practice for adults with drug-resistant epilepsy. This set highlights the diversity of factors beyond seizure frequency and severity that impact QOL in epilepsy.

Development of a natural language processing algorithm to extract seizure types and frequencies from the electronic health record

OBJECTIVE

To develop a natural language processing (NLP) algorithm to abstract seizure types and frequencies from electronic health records (EHR).

BACKGROUND

Seizure frequency measurement is an epilepsy quality metric. Yet, abstraction of seizure frequency from the EHR is laborious. We present an NLP algorithm to extract seizure data from unstructured text of clinic notes. Algorithm performance was assessed at two epilepsy centers.

METHODS

We developed a rules-based NLP algorithm to recognize terms related to seizures and frequency within the text of an outpatient encounter. Algorithm output (e.g. number of seizures of a particular type within a time interval) was compared to seizure data manually annotated by two expert reviewers ("gold standard"). The algorithm was developed from 150 clinic notes from institution #1 (development set), then tested on a separate set of 219 notes from institution #1 (internal test set) with 248 unique seizure frequency elements. The algorithm was separately applied to 100 notes from institution #2 (external test set) with 124 unique seizure frequency elements. Algorithm performance was measured by recall (sensitivity), precision (positive predictive value), and F1 score (geometric mean of precision and recall).

RESULTS

In the internal test set, the algorithm demonstrated 70% recall (173/248), 95% precision (173/182), and 0.82 F1 score compared to manual review. Algorithm performance in the external test set was lower with 22% recall (27/124), 73% precision (27/37), and 0.40 F1 score.

CONCLUSIONS

These results suggest NLP extraction of seizure types and frequencies is feasible, though not without challenges in generalizability for large-scale implementation.