Heterogeneity of resting-state EEG features in juvenile myoclonic epilepsy and controls

Abnormal EEG features are a hallmark of epilepsy, and abnormal frequency and network features are apparent in EEGs from people with idiopathic generalized epilepsy in both ictal and interictal states. Here, we characterize differences in the resting-state EEG of individuals with juvenile myoclonic epilepsy and assess factors influencing the heterogeneity of EEG features. We collected EEG data from 147 participants with juvenile myoclonic epilepsy through the Biology of Juvenile Myoclonic Epilepsy study. Ninety-five control EEGs were acquired from two independent studies [Chowdhury et al. (2014) and EU-AIMS Longitudinal European Autism Project]. We extracted frequency and functional network-based features from 10 to 20 s epochs of resting-state EEG, including relative power spectral density, peak alpha frequency, network topology measures and brain network ictogenicity: a computational measure of the propensity of networks to generate seizure dynamics. We tested for differences between epilepsy and control EEGs using univariate, multivariable and receiver operating curve analysis. In addition, we explored the heterogeneity of EEG features within and between cohorts by testing for associations with potentially influential factors such as age, sex, epoch length and time, as well as testing for associations with clinical phenotypes including anti-seizure medication, and seizure characteristics in the epilepsy cohort. P-values were corrected for multiple comparisons. Univariate analysis showed significant differences in power spectral density in delta (2-5 Hz) (P = 0.0007, hedges' g = 0.55) and low-alpha (6-9 Hz) (P = 2.9 × 10-8, g = 0.80) frequency bands, peak alpha frequency (P = 0.000007, g = 0.66), functional network mean degree (P = 0.0006, g = 0.48) and brain network ictogenicity (P = 0.00006, g = 0.56) between epilepsy and controls. Since age (P = 0.009) and epoch length (P = 1.7 × 10-8) differed between the two groups and were potential confounders, we controlled for these covariates in multivariable analysis where disparities in EEG features between epilepsy and controls remained. Receiver operating curve analysis showed low-alpha power spectral density was optimal at distinguishing epilepsy from controls, with an area under the curve of 0.72. Lower average normalized clustering coefficient and shorter average normalized path length were associated with poorer seizure control in epilepsy patients. To conclude, individuals with juvenile myoclonic epilepsy have increased power of neural oscillatory activity at low-alpha frequencies, and increased brain network ictogenicity compared with controls, supporting evidence from studies in other epilepsies with considerable external validity. In addition, the impact of confounders on different frequency-based and network-based EEG features observed in this study highlights the need for careful consideration and control of these factors in future EEG research in idiopathic generalized epilepsy particularly for their use as biomarkers.

Adult-onset idiopathic dystonia: A national data-linkage study to determine epidemiological, social deprivation, and mortality characteristics

BACKGROUND AND PURPOSE

Accurate epidemiological information is essential for the improved understanding of dystonia syndromes, as well as better provisioning of clinical services and providing context for diagnostic decision-making. Here, we determine epidemiological, social deprivation, and mortality characteristics of adult-onset idiopathic dystonia in the Welsh population.

METHODS

A retrospective population-based cohort study using anonymized electronic health care data in Wales was conducted to identify individuals with dystonia between 1 January 1994 and 31 December 2017. We developed a case-ascertainment algorithm to determine dystonia incidence and prevalence, as well as characterization of the dystonia cohort, based on social deprivation and mortality.

RESULTS

The case-ascertainment algorithm (79% sensitivity) identified 54,966 cases; of these cases, 41,660 had adult-onset idiopathic dystonia (≥20 years). Amongst the adult-onset form, the median age at diagnosis was 41 years, with males significantly older at time of diagnosis compared to females. Prevalence rates ranged from 0.02% in 1994 to 1.2% in 2017. The average annual incidence was 87.7/100,000/year, increasing from 49.9/100,000/year (1994) to 96.21/100,000/year (2017). In 2017, people with dystonia had a similar life expectancy to the Welsh population.

CONCLUSIONS

We have developed a case-ascertainment algorithm, supported by the introduction of a neurologist-reviewed validation cohort, providing a platform for future population-based dystonia studies. We have established robust population-level prevalence and incidence values for adult-onset idiopathic forms of dystonia, with this reflecting increasing clinical recognition and identification of causal genes. Underlying causes of death mirrored those of the general population, including circulatory disorders, respiratory disorders, cancers, and dementia.

Epilepsy Mortality in Wales: 2005-2017

BackgroundEpilepsy is a common, chronic neurological condition that affects 50 million people worldwide. The risk of premature death in people with epilepsy is up to three times higher than for the general population making this disease a significant public health concern. In England, there are around 3,100 deaths associated with epilepsy each year; 49 per cent of these deaths are premature. The mortality of epilepsy in Wales in recent years is currently unknown. Main Aim To ascertain mortality figures for deaths associated with epilepsy in Wales. MethodWe anonymously linked the Annual District Death Extract and the Welsh Demographics Service datasets within the Secure Anonymised Information Linkage Databank. Using ICD-10 codes for epilepsy, we identified all people who died with a mention of epilepsy on their death certificate, date of their death, and age at death between 2005 and 2017. Number of deaths per year were summed for each year. We also calculated the proportion of premature deaths and mean age at death for each year. All-cause mortality figures were collated as comparators. ResultsDuring the study period, there were around 173 deaths associated with epilepsy in Wales each year. The proportion of epilepsy-associated deaths compared with all-cause deaths increased almost two-fold during this time. 54% of all deaths associated with epilepsy occurred under the age of 75 years, compared with 33 per cent of all-cause deaths. The mean age at death for people with epilepsy is 67 years; 11 years younger than all-cause deaths (78 years). ConclusionThe number of deaths associated with epilepsy is increasing every year in Wales. These figures also show that having epilepsy reduces life expectancy. More research is needed into the causes of epilepsy-associated deaths to inform policy and improve outcomes for this patient group.

Association between levodopa and ischemic heart disease

Background Several studies linked the use of levodopa to an increase in homocysteine level, which can lead eventually to ischemic heart disease (IHD) in Parkinson’s disease (PD) patients. There is a lack of large population studies that have investigated the cardiovascular safety of levodopa. Objectives The main objective of the study is to investigate the one-year risk of IHD hospitalisation, all-cardiovascular hospital hospitalisation, and all-cause mortality among users of L-dopa compared with users of Monoamine oxidase B (MAO-B) inhibitors (as a reference). Methods A population-based study evaluated data obtained from the Secure Anonymised Information Linkage (SAIL) Databank of residents in Wales, aged 40 years or older, newly treated with PD medications between 2000 and 2016. The General Practice (GP) database was used to identify the PD diagnostic codes, PD medications, and other medications used by PD patients. Hospital data were used to identify the first hospitalisation event (IHD and other cardiovascular events). A fully adjusted propensity score multivariate Cox regression analysis was used to examine associations between levodopa and the study outcomes. The index date was set at the date of the first PD prescription in the newly diagnosed PD patients. Other variables included gender, comorbidities, social deprivation score and previous medications history were controlled for. Findings There were 5,140 participants on levodopa and 494 on MAO-B inhibitors. L-dopa was not associated with IHD (p=0.561), other cardiovascular events (p=0.233), or all-cause mortality (p=0.334). For IHD, the lack of difference was seen also in the unadjusted model and in the age-only adjusted model. Conclusion This study has shown that L-dopa does not increase the risk of IHD, cardiovascular risk, or all-cause mortality in the newly diagnosed PD patient within one year after the therapy initiation. This could contribute to the safety profile of L-dopa therapy. Future research with a longer follow up period is warranted.

Validating epilepsy diagnoses in routinely collected data

IntroductionPrimary healthcare records are used for studies within large data repositories. One of the limitations of using these routinely collected data for epilepsy research is the possibility of including incorrectly recorded diagnoses. To our knowledge, the accuracy of UK GP diagnosis codes for epilepsy has only partially been validated. Objectives and ApproachWe aimed to validate the accuracy of case ascertainment algorithms in identifying people with epilepsy in routinely collected Welsh healthcare data. A reference population of 150 people with definite epilepsy and 150 people without epilepsy was ascertained from hospital records and linked to records held within the Secure Anonymised Information Linkage (SAIL) databank in Wales. We used three different algorithms to identify the reference population: a) individuals with an epilepsy diagnosis code and two consecutive AED prescription codes; b) individuals with an epilepsy diagnosis code only; c) individuals with two consecutive AED prescription codes only. ResultsWe applied the algorithms to all patients and to adults and children separately. For all patients, combining diagnosis and AED prescription codes had a sensitivity of 84% (95% ci 77–90) and specificity of 98% (95–100) in identifying people with epilepsy; diagnosis codes alone had a sensitivity of 86% (80–91) and a specificity of 97% (92–99); and AED prescription codes alone achieved a sensitivity of 92% (70–83) and a specificity of 73% (65–80). Using AED codes only was more accurate in children, achieving a sensitivity of 88% (75–95) and specificity of 98% (88–100). This can be explained by the widespread use of AEDs for indications other than epilepsy in adults, which is not the case for children. Conclusion/ImplicationsGP epilepsy diagnosis and AED prescription codes can be used to identify people with epilepsy using anonymised healthcare records in Wales. In children using AED prescription codes alone is an accurate way to identify epilepsy cases. These results are generalizable to other studies that use UK primary care records.

Using natural language processing to extract structured epilepsy data from unstructured clinic letters

IntroductionElectronic health records (EHR) are a powerful resource in enabling large-scale healthcare research. EHRs often lack detailed disease-specific information that is collected in free text within clinical settings. This challenge can be addressed by using Natural Language Processing (NLP) to derive and extract detailed clinical information from free text. Objectives and ApproachUsing a training sample of 40 letters, we used the General Architecture for Text Engineering (GATE) framework to build custom rule sets for nine categories of epilepsy information as well as clinic date and date of birth. We used a validation set of 200 clinic letters to compare the results of our algorithm to a separate manual review by a clinician, where we evaluated a “per item” and a “per letter” approach for each category. ResultsThe “per letter” approach identified 1,939 items of information with overall precision, recall and F1-score of 92.7%, 77.7% and 85.6%. Precision and recall for epilepsy specific categories were: diagnosis (85.3%,92.4%),  type (93.7%,83.2%), focal seizure (99.0%,68.3%), generalised seizure (92.5%,57.0%), seizure frequency (92.0%,52.3%), medication (96.1%,94.0%), CT (66.7%,47.1%), MRI (96.6%,51.4%) and EEG (95.8%,40.6%). By combining all items per category, per letter we were able to achieve higher precision, recall and F1-scores of 94.6%, 84.2% and 89.0% across all categories. Conclusion/ImplicationsOur results demonstrate that NLP techniques can be used to accurately extract rich phenotypic details from clinic letters that is often missing from routinely-collected data. Capturing these new data types provides a platform for conducting novel precision neurology research, in addition to potential applicability to other disease areas.

Codifying unstructured data: A Natural Language Processing approach to extract rich data from clinical letters

ABSTRACT ObjectivesElectronic healthcare records (EHR) are the main data sources that facilitate epidemiology research. Routinely collected data such as primary and secondary care are now easily linked to produce novel and high impact research. There are, however, rich data locked in the free text of clinical letters that are not otherwise translated into EHRs. It is highly desirable to be able to extract this information to strengthen the body of information in existing EHRs. The Swansea Collaborative in Analysis of NLP Research (SCANR) group at Swansea University has been established to evaluate the usage of Natural Language Processing platforms for obtaining new clinical data. To use Clix Enrich to extract SNOMED concepts from a variety of clinical free texts and produce EHRs from the extraction process. Approach SNOMED concepts contain common items of interest such as diagnosis, medication and symptoms, as well as contextual concepts such as historical reference and negation. Clix Enrich uses the SNOMED dictionary to encode clinical free text (pre-co-ordinated) and find contextually correct SNOMED concepts (post co-ordinated). We used Clix Enrich to extract meaningful clinical terms from MS and Epilepsy consultant letters, as well as presenting complaint fields from a Welsh Emergency Department (ED). ResultsWe tailored Clix Enrich to extract a wide variety of clinical terms from each source (fourty texts per source) and validated the extraction accuracy with clinical experts in each domain. Clix Enrich was able to accurately extract the correct diagnosis for MS, Epilepsy and ED attendance (100%, 95% and 80%), dosage and frequency of anti-epileptic medication and MS modifying therapy (90%, 100%) and EDDS score (94%). We note a probable source of discrepancy in extraction accuracy between letter sources in the frequency of abbreviated terms, particularly within the presenting complaint field of the ED sample. ConclusionClix Enrich can be used to accurately extract SNOMED concepts from clinical letters. The resulting datasets are readily available to link to existing EHRs, and can be linked to EHRs that adopt the SNOMED coding structure, or backward compatible hierarchies. Clix Enrich comes with out-of-the-box extraction methods but the optimum way to extract the correct information would be to build in custom queries, thus requiring clinical expertise to validate extraction.