Health care utilization and mortality for people with epilepsy during COVID-19: A population study

OBJECTIVE

This study was undertaken to characterize changes in health care utilization and mortality for people with epilepsy (PWE) during the COVID-19 pandemic.

METHODS

We performed a retrospective study using linked, individual-level, population-scale anonymized health data from the Secure Anonymised Information Linkage databank. We identified PWE living in Wales during the study "pandemic period" (January 1, 2020-June 30, 2021) and during a "prepandemic" period (January 1, 2016-December 31, 2019). We compared prepandemic health care utilization, status epilepticus, and mortality rates with corresponding pandemic rates for PWE and people without epilepsy (PWOE). We performed subgroup analyses on children (<18 years old), older people (>65 years old), those with intellectual disability, and those living in the most deprived areas. We used Poisson models to calculate adjusted rate ratios (RRs).

RESULTS

We identified 27 279 PWE who had significantly higher rates of hospital (50.3 visits/1000 patient months), emergency department (55.7), and outpatient attendance (172.4) when compared to PWOE (corresponding figures: 25.7, 25.2, and 87.0) in the prepandemic period. Hospital and epilepsy-related hospital admissions, and emergency department and outpatient attendances all reduced significantly for PWE (and all subgroups) during the pandemic period. RRs [95% confidence intervals (CIs)] for pandemic versus prepandemic periods were .70 [.69-.72], .77 [.73-.81], .78 [.77-.79], and .80 [.79-.81]. The corresponding rates also reduced for PWOE. New epilepsy diagnosis rates decreased during the pandemic compared with the prepandemic period (2.3/100 000/month cf. 3.1/100 000/month, RR = .73, 95% CI = .68-.78). Both all-cause deaths and deaths with epilepsy recorded on the death certificate increased for PWE during the pandemic (RR = 1.07, 95% CI = .997-1.145 and RR = 2.44, 95% CI = 2.12-2.81). When removing COVID deaths, RRs were .88 (95% CI = .81-.95) and 1.29 (95% CI = 1.08-1.53). Status epilepticus rates did not change significantly during the pandemic (RR = .95, 95% CI = .78-1.15).

SIGNIFICANCE

All-cause non-COVID deaths did not increase but non-COVID deaths associated with epilepsy did increase for PWE during the COVID-19 pandemic. The longer term effects of the decrease in new epilepsy diagnoses and health care utilization and increase in deaths associated with epilepsy need further research.

Epilepsy and the risk of COVID-19-related hospitalization and death: A population study

OBJECTIVE

People with epilepsy (PWE) may be at an increased risk of severe COVID-19. It is important to characterize this risk to inform PWE and for future health and care planning. We assessed whether PWE were at higher risk of being hospitalized with, or dying from, COVID-19.

METHODS

We performed a retrospective cohort study using linked, population-scale, anonymized electronic health records from the SAIL (Secure Anonymised Information Linkage) databank. This includes hospital admission and demographic data for the complete Welsh population (3.1 million) and primary care records for 86% of the population. We identified 27 279 PWE living in Wales during the study period (March 1, 2020 to June 30, 2021). Controls were identified using exact 5:1 matching (sex, age, and socioeconomic status). We defined COVID-19 deaths as having International Classification of Diseases, 10th Revision (ICD-10) codes for COVID-19 on death certificates or occurring within 28 days of a positive SARS-CoV-2 polymerase chain reaction (PCR) test. COVID-19 hospitalizations were defined as having a COVID-19 ICD-10 code for the reason for admission or occurring within 28 days of a positive SARS-CoV-2 PCR test. We recorded COVID-19 vaccinations and comorbidities known to increase the risk of COVID-19 hospitalization and death. We used Cox proportional hazard models to calculate hazard ratios.

RESULTS

There were 158 (.58%) COVID-19 deaths and 933 (3.4%) COVID-19 hospitalizations in PWE, and 370 (.27%) deaths and 1871 (1.4%) hospitalizations in controls. Hazard ratios for COVID-19 death and hospitalization in PWE compared to controls were 2.15 (95% confidence interval [CI] = 1.78-2.59) and 2.15 (95% CI = 1.94-2.37), respectively. Adjusted hazard ratios (adjusted for comorbidities) for death and hospitalization were 1.32 (95% CI = 1.08-1.62) and 1.60 (95% CI = 1.44-1.78).

SIGNIFICANCE

PWE are at increased risk of being hospitalized with, and dying from, COVID-19 when compared to age-, sex-, and deprivation-matched controls, even when adjusting for comorbidities. This may have implications for prioritizing future COVID-19 treatments and vaccinations for PWE.

Natural language processing to automate a web-based model of care and modernize skin cancer multidisciplinary team meetings

BACKGROUND

Cancer multidisciplinary team (MDT) meetings are under intense pressure to reform given the rapidly rising incidence of cancer and national mandates for protocolized streaming of cases. The aim of this study was to validate a natural language processing (NLP)-based web platform to automate evidence-based MDT decisions for skin cancer with basal cell carcinoma as a use case.

METHODS

A novel and validated NLP information extraction model was used to extract perioperative tumour and surgical factors from histopathology reports. A web application with a bespoke application programming interface used data from this model to provide an automated clinical decision support system, mapped to national guidelines and generating a patient letter to communicate ongoing management. Performance was assessed against retrospectively derived recommendations by two independent and blinded expert clinicians.

RESULTS

There were 893 patients (1045 lesions) used to internally validate the model. High accuracy was observed when compared against human predictions, with an overall value of 0.92. Across all classifiers the virtual skin MDT was highly specific (0.96), while sensitivity was lower (0.72).

CONCLUSION

This study demonstrates the feasibility of a fully automated, virtual, web-based service model to host the skin MDT with good system performance. This platform could be used to support clinical decision-making during MDTs as 'human in the loop' approach to aid protocolized streaming. Future prospective studies are needed to validate the model in tumour types where guidelines are more complex.

Revisiting basal cell carcinoma clinical margins: Leveraging natural language processing and multivariate analysis with updated Royal College of Pathologists histological reporting standards

INTRODUCTION

Data supporting the current British Association of Dermatologists guidelines for the management of basal cell carcinoma (BCC) are based on historic studies and do not consider the updated Royal College of Pathologists (RCPath) histological reporting standards. The aim of this study was to use natural language processing (NLP)-derived data and undertake a multivariate analysis with updated RCPath standards, providing a contemporary update on the excision margins required to achieve histological clearance in BCC.

METHODS

A validated NLP information extraction model was used to perform a rapid multi-centre, pan-specialty, consecutive retrospective analysis of BCCs, managed with surgical excision using a pre-determined clinical margin, over a 17-year period (2004-2021) at Swansea Bay University Health Board. Logistic regression assessed the relationship between the peripheral and deep margins and histological clearance.

RESULTS

We ran our NLP algorithm on 34,955 BCCs. Out of the 1447 BCCs that met the inclusion criteria, the peripheral margin clearance was not influenced by the BCC risk level (p = 0.670). A clinical peripheral margin of 6 mm achieved a 95% histological clearance rate (95% confidence interval [CI], 0.93-0.98). Tumour thickness inversely affected deep-margin histological clearance (OR 0.720, 95% CI, 0.525-0.991, p < 0.05). Depth level 2 had a 97% probability of achieving deep-margin histological clearance across all tumour thicknesses.

CONCLUSION

Updated RCPath reporting standards minimally impact the peripheral margin histological clearance in BCC. Larger clinical peripheral margins than those indicated by current guidelines may be necessary to achieve excision rates of ≥95%. These findings emphasise the need for continuous reassessment of clinical standards to enhance patient care.

Does Health & Her app use improve menopausal symptoms? A longitudinal cohort study

OBJECTIVES

The Health & Her app provides menopausal women with a means of monitoring their symptoms, symptom triggers and menstrual periods, and enables them to engage in a variety of digital activities designed to promote well-being. This study aimed to examine whether sustained weekly engagement with the app is associated with improvements in menopausal symptoms.

DESIGN

A pre-post longitudinal cohort study.

SETTING

Analysed data collected from Health & Her app users.

PARTICIPANTS

1900 women who provided symptom data via the app across a 2-month period.

PRIMARY AND SECONDARY OUTCOME MEASURES

Symptom changes from baseline to 2 months was the outcome measure. A linear mixed effects model explored whether levels of weekly app engagement influenced symptom changes. Secondary analyses explored whether app-usage factors such as total number of days spent logging symptoms, reporting triggers, reporting menstrual periods and using in-app activities were independently predictive of symptom changes from baseline. Covariates included hormone replacement therapy use, hormonal contraceptive use, present comorbidities, age and dietary supplement use.

RESULTS

Findings demonstrated that greater engagement with the Health & Her app for 2 months was associated with greater reductions in symptoms over time. Daily use of in-app activities and logging symptoms and menstrual periods were each independently associated with symptom reductions.

CONCLUSIONS

This study demonstrated that greater weekly engagement with the app was associated with greater reductions in symptoms. It is recommended that women be made aware of menopause-specific apps, such as that provided by Health & Her, to support them to manage their symptoms.

Genetic influences on epilepsy outcomes: A whole-exome sequencing and health care records data linkage study

OBJECTIVE

This study was undertaken to develop a novel pathway linking genetic data with routinely collected data for people with epilepsy, and to analyze the influence of rare, deleterious genetic variants on epilepsy outcomes.

METHODS

We linked whole-exome sequencing (WES) data with routinely collected primary and secondary care data and natural language processing (NLP)-derived seizure frequency information for people with epilepsy within the Secure Anonymised Information Linkage Databank. The study participants were adults who had consented to participate in the Swansea Neurology Biobank, Wales, between 2016 and 2018. DNA sequencing was carried out as part of the Epi25 collaboration. For each individual, we calculated the total number and cumulative burden of rare and predicted deleterious genetic variants and the total of rare and deleterious variants in epilepsy and drug metabolism genes. We compared these measures with the following outcomes: (1) no unscheduled hospital admissions versus unscheduled admissions for epilepsy, (2) antiseizure medication (ASM) monotherapy versus polytherapy, and (3) at least 1 year of seizure freedom versus <1 year of seizure freedom.

RESULTS

We linked genetic data for 107 individuals with epilepsy (52% female) to electronic health records. Twenty-six percent had unscheduled hospital admissions, and 70% were prescribed ASM polytherapy. Seizure frequency information was linked for 100 individuals, and 10 were seizure-free. There was no significant difference between the outcome groups in terms of the exome-wide and gene-based burden of rare and deleterious genetic variants.

SIGNIFICANCE

We successfully uploaded, annotated, and linked genetic sequence data and NLP-derived seizure frequency data to anonymized health care records in this proof-of-concept study. We did not detect a genetic influence on real-world epilepsy outcomes, but our study was limited by a small sample size. Future studies will require larger (WES) data to establish genetic variant contribution to epilepsy outcomes.

COVID-19 vaccination uptake in people with epilepsy in wales

PURPOSE

People with epilepsy (PWE) are at increased risk of severe COVID-19. Assessing COVID-19 vaccine uptake is therefore important. We compared COVID-19 vaccination uptake for PWE in Wales with a matched control cohort.

METHODS

We performed a retrospective, population, cohort study using linked, anonymised, Welsh electronic health records within the Secure Anonymised Information Linkage (SAIL) Databank (Welsh population=3.1 million).We identified PWE in Wales between 1st March 2020 and 31st December 2021 and created a control cohort using exact 5:1 matching (sex, age and socioeconomic status). We recorded 1st, 2nd and booster COVID-19 vaccinations.

RESULTS

There were 25,404 adults with epilepsy (127,020 controls). 23,454 (92.3%) had a first vaccination, 22,826 (89.9%) a second, and 17,797 (70.1%) a booster. Comparative figures for controls were: 112,334 (87.8%), 109,057 (85.2%) and 79,980 (62.4%).PWE had higher vaccination rates in all age, sex and socioeconomic subgroups apart from booster uptake in older subgroups. Vaccination rates were higher in older subgroups, women and less deprived areas for both cohorts. People with intellectual disability and epilepsy had higher vaccination rates when compared with controls with intellectual disability.

CONCLUSIONS

COVID-19 vaccination uptake for PWE in Wales was higher than that for a matched control group.

Epilepsy mortality in Wales during COVID-19

Purpose

We aimed to compare mortality rates in people with epilepsy in Wales during the pandemic with pre-pandemic rates.

Methods

We performed a retrospective study using population-scale anonymised health records. We identified deaths in people with epilepsy (DPWE), those with a diagnosis of epilepsy, and deaths associ- ated with epilepsy (DAE), where epilepsy was recorded as a cause of death. We compared death rates in 2020 with average rates in 2015–2019 using Poisson models.

Results

There were 188 DAE and 628 DPWE in Wales in 2020 (death rates: 7.7/100,000/year and 25.7/100,000/year). The average rates for DAE and DPWE from 2015 to 2019 were 5.8/100,000/year and 23.8/100,000/year, respectively. Death rate ratios (2020 compared to 2015–2019) for DAE were 1.34 (95%CI 1.14–1.57, p<0.001) and for DPWE were 1.08 (0.99–1.17, p = 0.09). The death rate ratios for non-COVID deaths (deaths without COVID mentioned on death certificates) for DAE were 1.17 (0.99–1.39, p = 0.06) and for DPWE were 0.96 (0.87–1.05, p = 0.37).

Conclusions

The significant increase in DAE in Wales during 2020 could be explained by the direct effect of COVID-19 infection. Non-COVID-19 deaths have not increased significantly but further work is needed to assess the longer-term impact.

Development and validation of an automated basal cell carcinoma histopathology information extraction system using natural language processing

Introduction

Routinely collected healthcare data are a powerful research resource, but often lack detailed disease-specific information that is collected in clinical free text such as histopathology reports. We aim to use natural Language Processing (NLP) techniques to extract detailed clinical and pathological information from histopathology reports to enrich routinely collected data.

Methods

We used the general architecture for text engineering (GATE) framework to build an NLP information extraction system using rule-based techniques. During validation, we deployed our rule-based NLP pipeline on 200 previously unseen, de-identified and pseudonymised basal cell carcinoma (BCC) histopathological reports from Swansea Bay University Health Board, Wales, UK. The results of our algorithm were compared with gold standard human annotation by two independent and blinded expert clinicians involved in skin cancer care.

Results

We identified 11,224 items of information with a mean precision, recall, and F1 score of 86.0% (95% CI: 75.1-96.9), 84.2% (95% CI: 72.8-96.1), and 84.5% (95% CI: 73.0-95.1), respectively. The difference between clinician annotator F1 scores was 7.9% in comparison with 15.5% between the NLP pipeline and the gold standard corpus. Cohen's Kappa score on annotated tokens was 0.85.

Conclusion

Using an NLP rule-based approach for named entity recognition in BCC, we have been able to develop and validate a pipeline with a potential application in improving the quality of cancer registry data, supporting service planning, and enhancing the quality of routinely collected data for research.

146 Epilepsy, deprivation and mortality in Wales 2005–2017

Background

Public Health England have recently reported that deaths associated with epilepsy are increasing and are associated with increased deprivation. We investigated comparable Welsh mortality trends and associations between epilepsy mortality and deprivation.

Method

We used routinely-collected health data within the Secure Anonymised Information Linkage (SAIL) Databank. We recorded deaths associated with epilepsy (DAE), epilepsy recorded on death certificates, and deaths in people with epilepsy (DPWE), people with diagnoses of epilepsy and epilepsy prescriptions before death. We compared death rates in different deprivation deciles adjusting for epilepsy prevalence.

Results

During 2005–2017 (41million patient-years) there were 2116 DAE and 7821 DPWE. DAE and DPWE increased from 4.3/100,000/yr and 17.2/100,000/yr in 2005–2007 to 5.7/100,000/yr and 20.9/100,000/yr in 2015–2017. The age-standardised mortality rates (ASMR) in 2006–2008 for DAE and DPWE were 5.3/100,000/yr and 20/100,000/yr respectively, in 2015–2017 they were 5.8/100,000/yr and 20/100,000/yr. DAE were not significantly associated with deprivation when adjusted for epilepsy prevalence.

Conclusion

When adjusting for age, deaths associated wtih epilepsy and deaths in people with epilepsy did not increase significantly in Wales between 2005–2007 and 2015–2017. The association between dep- rivation and deaths associated with epilepsy appears to be explained by higher epilepsy prevalence in areas of higher deprivation.

w.o.pickrell@swansea.ac.uk

Epilepsy mortality in Wales during COVID-19

PURPOSE

The COVID-19 pandemic has increased mortality worldwide and those with chronic conditions may have been disproportionally affected. However, it is unknown whether the pandemic has changed mortality rates for people with epilepsy. We aimed to compare mortality rates in people with epilepsy in Wales during the pandemic with pre-pandemic rates.

METHODS

We performed a retrospective study using individual-level linked population-scale anonymised electronic health records. We identified deaths in people with epilepsy (DPWE), i.e. those with a diagnosis of epilepsy, and deaths associated with epilepsy (DAE), where epilepsy was recorded as a cause of death on death certificates. We compared death rates in 2020 with average rates in 2015-2019 using Poisson models to calculate death rate ratios.

RESULTS

There were 188 DAE and 628 DPWE in Wales in 2020 (death rates: 7.7/100,000/year and 25.7/100,000/year). The average rates for DAE and DPWE from 2015 to 2019 were 5.8/100,000/year and 23.8/100,000/year, respectively. Death rate ratios (2020 compared to 2015-2019) for DAE were 1.34 (95%CI 1.14-1.57, p<0.001) and for DPWE were 1.08 (0.99-1.17, p = 0.09). The death rate ratios for non-COVID deaths (deaths without COVID mentioned on death certificates) for DAE were 1.17 (0.99-1.39, p = 0.06) and for DPWE were 0.96 (0.87-1.05, p = 0.37).

CONCLUSIONS

The significant increase in DAE in Wales during 2020 could be explained by the direct effect of COVID-19 infection. Non-COVID-19 deaths have not increased significantly but further work is needed to assess the longer-term impact.

Using natural language processing to extract structured epilepsy data from unstructured clinic letters: development and validation of the ExECT (extraction of epilepsy clinical text) system

OBJECTIVE

Routinely collected healthcare data are a powerful research resource but often lack detailed disease-specific information that is collected in clinical free text, for example, clinic letters. We aim to use natural language processing techniques to extract detailed clinical information from epilepsy clinic letters to enrich routinely collected data.

DESIGN

We used the general architecture for text engineering (GATE) framework to build an information extraction system, ExECT (extraction of epilepsy clinical text), combining rule-based and statistical techniques. We extracted nine categories of epilepsy information in addition to clinic date and date of birth across 200 clinic letters. We compared the results of our algorithm with a manual review of the letters by an epilepsy clinician.

SETTING

De-identified and pseudonymised epilepsy clinic letters from a Health Board serving half a million residents in Wales, UK.

RESULTS

We identified 1925 items of information with overall precision, recall and F1 score of 91.4%, 81.4% and 86.1%, respectively. Precision and recall for epilepsy-specific categories were: epilepsy diagnosis (88.1%, 89.0%), epilepsy type (89.8%, 79.8%), focal seizures (96.2%, 69.7%), generalised seizures (88.8%, 52.3%), seizure frequency (86.3%-53.6%), medication (96.1%, 94.0%), CT (55.6%, 58.8%), MRI (82.4%, 68.8%) and electroencephalogram (81.5%, 75.3%).

CONCLUSIONS

We have built an automated clinical text extraction system that can accurately extract epilepsy information from free text in clinic letters. This can enhance routinely collected data for research in the UK. The information extracted with ExECT such as epilepsy type, seizure frequency and neurological investigations are often missing from routinely collected data. We propose that our algorithm can bridge this data gap enabling further epilepsy research opportunities. While many of the rules in our pipeline were tailored to extract epilepsy specific information, our methods can be applied to other diseases and also can be used in clinical practice to record patient information in a structured manner.

Educational attainment of children born to mothers with epilepsy

OBJECTIVE

Small prospective studies have identified that children exposed to valproate in utero have poorer scores on cognitive testing. We wanted to identify whether children exposed to antiepileptic drugs (AEDs) in utero have poorer school performance.

METHODS

We used anonymised, linked, routinely collected healthcare records to identify children born to mothers with epilepsy. We linked these children to their national attainment Key Stage 1 (KS1) tests in mathematics, language and science at the age of 7 and compared them with matched children born to mothers without epilepsy, and with the national KS1 results. We used the core subject indicator (CSI) as an outcome measure (the proportion of children achieving a minimum standard in all subjects) and the results in individual subjects.

RESULTS

We identified 440 children born to mothers with epilepsy with available KS1 results. Compared with a matched control group, fewer children with mothers being prescribed sodium valproate during pregnancy achieved the national minimum standard in CSI (-12.7% less than the control group), mathematics (-12.1%), language (-10.4%) and in science (-12.2%). Even fewer children with mothers being prescribed multiple AEDs during pregnancy achieved a national minimum standard: CSI (by -20.7% less than the control group), mathematics (-21.9%), language (-19.3%) and science (-19.4%). We did not observe any significant difference in children whose mothers were prescribed carbamazepine or were not taking an AED when compared with the control group.

CONCLUSIONS

In utero exposure to AEDs in combination, or sodium valproate alone, is associated with a significant decrease in attainment in national educational tests for 7-year-old children compared with both a matched control group and the all-Wales national average. These results give further support to the cognitive and developmental effects of in utero exposure to sodium valproate as well as multiple AEDs, which should be balanced against the need for effective seizure control for women during pregnancy.

Validating epilepsy diagnoses in routinely collected data

•

Cases with and without epilepsy were linked with anonymised primary care data.

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Primary care diagnosis and drug codes accurately identify the cases with epilepsy.

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Drug codes alone can be used to identify children with epilepsy.

•

Combining drug and diagnosis codes for adults and children increases accuracy.

Purpose

Anonymised, routinely-collected healthcare data is increasingly being used for epilepsy research. We validated algorithms using general practitioner (GP) primary healthcare records to identify people with epilepsy from anonymised healthcare data within the Secure Anonymised Information Linkage (SAIL) databank in Wales, UK.

Method

A reference population of 150 people with definite epilepsy and 150 people without epilepsy was ascertained from hospital records and linked to records contained within SAIL (containing GP records for 2.4 million people). We used three different algorithms, using combinations of GP epilepsy diagnosis and anti-epileptic drug (AED) prescription codes, to identify the reference population.

Results

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).

Conclusion

GP epilepsy diagnosis and AED prescription codes can be confidently used to identify people with epilepsy using anonymised healthcare records in Wales, UK.

Obtaining structured clinical data from unstructured data using natural language processing software

ABSTRACT BackgroundFree text documents in healthcare settings contain a wealth of information not captured in electronic healthcare records (EHRs). Epilepsy clinic letters are an example of an unstructured data source containing a large amount of intricate disease information. Extracting meaningful and contextually correct clinical information from free text sources, to enhance EHRs, remains a significant challenge. SCANR (Swansea University Collaborative in the Analysis of NLP Research) was set up to use natural language processing (NLP) technology to extract structured data from unstructured sources. IBM Watson Content Analytics software (ICA) uses NLP technology. It enables users to define annotations based on dictionaries and language characteristics to create parsing rules that highlight relevant items. These include clinical details such as symptoms and diagnoses, medication and test results, as well as personal identifiers.   ApproachTo use ICA to build a pipeline to accurately extract detailed epilepsy information from clinic letters. MethodsWe used ICA to retrieve important epilepsy information from 41 pseudo-anonymized unstructured epilepsy clinic letters. The 41 letters consisted of 13 ‘new’ and 28 ‘follow-up’ letters (for 15 different patients) written by 12 different doctors in different styles. We designed dictionaries and annotators to enable ICA to extract epilepsy type (focal, generalized or unclassified), epilepsy cause, age of onset, investigation results (EEG, CT and MRI), medication, and clinic date. Epilepsy clinicians assessed the accuracy of the pipeline. ResultsThe accuracy (sensitivity, specificity) of each concept was: epilepsy diagnosis 98% (97%, 100%), focal epilepsy 100%, generalized epilepsy 98% (93%, 100%), medication 95% (93%, 100%), age of onset 100% and clinic date 95% (95%, 100%). Precision and recall for each concept were respectively, 98% and 97% for epilepsy diagnosis, 100% each for focal epilepsy, 100% and 93% for generalized epilepsy, 100% each for age of onset, 100% and 93% for medication, 100% and 96% for EEG results, 100% and 83% for MRI scan results, and 100% and 95% for clinic date. Conclusions ICA is capable of extracting detailed, structured epilepsy information from unstructured clinic letters to a high degree of accuracy. This data can be used to populate relational databases and be linked to EHRs. Researchers can build in custom rules to identify concepts of interest from letters and produce structured information. We plan to extend our work to hundreds and then thousands of clinic letters, to provide phenotypically rich epilepsy data to link with other anonymised, routinely collected data.

HbA1c measurement and relationship to incident stroke

AIMS

To determine the proportion of people with diabetes who have HbA1c measured, what proportion achieve an HbA1c level of < 58 mmol/mol (7.5%), the frequency of testing and if there was any change in HbA1c level in the year before and the year after an incident stroke.

METHODS

This study used the Secure Anonymised Information Linkage (SAIL) databank, which stores hospital data for the whole of Wales and ~ 65% of Welsh general practice records, to identify cases of stroke in patients with diabetes between 2000 and 2010. These were matched against patients with diabetes but without stroke disease. We assessed the frequency of HbA1c testing and change in HbA1c in the first year after stroke. Estimation was made of the proportion of patients achieving an HbA1c measurement ≤ 58 mmol/mol (7.5%).

RESULTS

There were 1741 patients with diabetes and stroke. Of these, 1173 (67.4%) had their HbA1c checked before their stroke and 1137 (65.3%) after their stroke. In the control group of 16 838 patients with diabetes but no stroke, 8413 (49.9%) and 9288 (55.1%) had their HbA1c checked before and after the case-matched stroke date, respectively. In patients with diabetes and stroke, HbA1c fell from 61-56 mmol/mol (7.7-7.3%) after their stroke (P < 0.001). Before the study, 55.0% of patients with stroke had an HbA1c ≥ 58 mmol/mol compared with 65.2% of control patients, these figures were 62.5% and 65.3% after the stroke.

CONCLUSIONS

The frequency of diabetes testing was higher in patients who had experienced a stroke before and after their incident stroke compared with control patients but did not increase after their stroke. Glucose control improved significantly in the year after a stroke.

Epilepsy and deprivation, a data linkage study

OBJECTIVE

To investigate whether the link between epilepsy and deprivation is due to factors associated with deprivation (social causation) or factors associated with a diagnosis of epilepsy (social drift).

METHODS

We reviewed electronic primary health care records from 2004 to 2010, identifying prevalent and incident cases of epilepsy and recording linked deprivation scores. Logistic and Poisson regression models were used to calculate odds ratios and incidence rate ratios. The change in deprivation was measured 10 years after the initial diagnosis of epilepsy for a cohort of people.

RESULTS

Between 2004 and 2010, 8.1 million patient-years of records were reviewed. Epilepsy prevalence and incidence were significantly associated with deprivation. Epilepsy prevalence ranged from 1.13% (1.07-1.19%) in the most deprived decile to 0.49% (0.45-0.53%) in the least deprived decile (adjusted odds ratio 0.92, p < 0.001). Epilepsy incidence ranged from 40/100,000 per year in the most deprived decile to 19/100,000 per year in the least deprived decile (adjusted incidence rate ratio 0.94, p < 0.001). There was no statistically significant change in deprivation index decile 10 years after a new diagnosis of epilepsy (mean difference -0.04, p = 0.85).

SIGNIFICANCE

Epilepsy prevalence and incidence are strongly associated with deprivation; the deprivation score remains unchanged 10 years after a diagnosis of epilepsy. These findings suggest that increasing rates of epilepsy in deprived areas are more likely explained by social causation than by social drift. The nature of the association between incident epilepsy and social deprivation needs further exploration.

Weight change associated with antiepileptic drugs

AIM

To investigate antiepileptic drug (AED)-related weight changes in patients with epilepsy through a retrospective observational study.

METHOD

We analysed the anonymised electronic primary care records of 1.1 million adult patients in Wales. We included patients aged 18 years and over with a diagnosis of epilepsy, whose body weight had been measured up to 12 months before starting, and between 3 and 12 months after starting, one of five AEDs. We calculated the weight difference after starting the AED for each patient.

RESULTS

1423 patients were identified in total. The mean difference between body weight after and before starting each AED (together with 95% CI and p values for no difference) were: carbamazepine (CBZ) 0.43 (-0.19 to 1.05) p=0.17; lamotrigine (LTG) 0.31 (-0.38 to 1.00) p=0.38; levetiracetam (LEV) 1.00 (0.16 to 1.84) p=0.02; sodium valproate (VPA) 0.74 (0.10 to 1.38) p=0.02; topiramate (TPM) -2.30 (-4.27 to -0.33) p=0.02.

CONCLUSIONS

LEV and VPA were associated with significant weight gain, TPM was associated with significant weight loss, and LTG and CBZ were not associated with significant weight change.