Objective: Clinical guidelines recommend screening people with epilepsy (PWE) regularly for mental dis-tress, but it is unclear how guidelines are implemented. We surveyed epilepsy specialists in adult Scottish services to determine approaches used to screen for anxiety, depression, and suicidality; the perceived difficulty of screening; factors associated with intention to screen; and treatment decisions made follow-ing positive screens.Methods: An anonymous email-based questionnaire survey of epilepsy nurses and epilepsy neurology specialists (n = 38) was conducted.Results: Two in every three specialists used a systematic screening approach; a third did not. Clinical interview was employed more often than standardized questionnaire. Clinicians reported positive atti-tudes towards screening but found screening difficult to implement. Intention to screen was associated with favorable attitude, perceived control, and social norm. Pharmacological and non-pharmacological interventions were proposed equally often for those screening positive for anxiety or depression.Conclusion: Routine screening for mental distress is carried out in Scottish epilepsy treatment settings but is not universal. Attention should be paid to clinician factors associated with screening, such as inten-tion to screen and resulting treatment decisions. These factors are potentially modifiable, offering a means of closing the gap between guideline recommendations and clinical practice.(c) 2023 Elsevier Inc. All rights reserved.

Objective Mental distress is present in a significant proportion of people with epilepsy (PWE), with a negative impact across life domains. It is underdiagnosed and under-treated despite guidelines recommending screening for its presence (e.g., SIGN, 2015). We describe a tertiary-care epilepsy mental distress screening and treatment pathway, with a preliminary investigation of its feasibility. Methods We selected psychometric screening instruments for depression, anxiety, quality of life (QOL), and suicidality, establishing treatment options matched to instrument scores on the Patient Health Questionnaire 9 (PHQ-9), along 'traffic light' lines. We determined feasibility outcomes including recruitment and retention rates, resources required to run the pathway, and level of psychological need. We undertook a preliminary investigation of change in distress scores over a 9-month interval and determined PWE engagement and the perceived usefulness of pathway treatment options. Results Two-thirds of eligible PWE were included in the pathway with an 88% retention rate. At the initial screen, 45.8% of PWE required either an 'Amber-2' intervention (for moderate distress) or a 'Red' one (for severe distress). The equivalent figure at the 9-month re-screen was 36.8%, reflective of an improvement in depression and QOL scores. Online charity-delivered well-being sessions and neuropsychology were rated highly for engagement and perceived usefulness, but computerized cognitive behavioral therapy was not. The resources required to run the pathway were modest. Conclusion Outpatient mental distress screening and intervention are feasible in PWE. The challenge is to optimize methods for screening in busy clinics and to determine the best (and most acceptable) interventions for screening positive PWE.

BACKGROUND:Methods to undertake diagnostic accuracy studies of administrative epilepsy data are challenged by lack of a way to reliably rank case-ascertainment algorithms in order of their accuracy. This is because it is difficult to know how to prioritise positive predictive value (PPV) and sensitivity (Sens). Large numbers of true negative (TN) instances frequently found in epilepsy studies make it difficult to discriminate algorithm accuracy on the basis of negative predictive value (NPV) and specificity (Spec) as these become inflated (usually >90%). This study demonstrates the complementary value of using weather forecasting or machine learning metrics critical success index (CSI) or F measure, respectively, as unitary metrics combining PPV and sensitivity. We reanalyse data published in a diagnostic accuracy study of administrative epilepsy mortality data in Scotland. METHOD:CSI was calculated as 1/[(1/PPV) + (1/Sens) - 1]. F measure was calculated as 2.PPV.Sens/(PPV + Sens). CSI and F values range from 0 to 1, interpreted as 0 = inaccurate prediction and 1 = perfect accuracy. The published algorithms were reanalysed using these and their accuracy re-ranked according to CSI in order to allow comparison to the original rankings. RESULTS:CSI scores were conservative (range 0.02-0.826), always less than or equal to the lower of the corresponding PPV (range 39-100%) and sensitivity (range 2-93%). F values were less conservative (range 0.039-0.905), sometimes higher than either PPV or sensitivity, but were always higher than CSI. Low CSI and F values occurred when there was a large difference between PPV and sensitivity, e.g. CSI was 0.02 and F was 0.039 in an instance when PPV was 100% and sensitivity was 2%. Algorithms with both high PPV and sensitivity performed best in terms of CSI and F measure, e.g. CSI was 0.826 and F was 0.905 in an instance when PPV was 90% and sensitivity was 91%. CONCLUSION:CSI or F measure can combine PPV and sensitivity values into a convenient single metric that is easier to interpret and rank in terms of diagnostic accuracy than trying to rank diagnostic accuracy according to the two measures themselves. CSI or F prioritise instances where both PPV and sensitivity are high over instances where there are large differences between PPV and sensitivity (even if one of these is very high), allowing diagnostic accuracy thresholds based on combined PPV and sensitivity to be determined. Therefore, CSI or F measures may be helpful complementary metrics to report alongside PPV and sensitivity in diagnostic accuracy studies of administrative epilepsy data.

This study aimed to develop a risk prediction model for epilepsy-related death in adults. In this age- and sex-matched case-control study, we compared adults (aged ≥16 years) who had epilepsy-related death between 2009 and 2016 to living adults with epilepsy in Scotland. Cases were identified from validated administrative national datasets linked to mortality records. ICD-10 cause-of-death coding was used to define epilepsy-related death. Controls were recruited from a research database and epilepsy clinics. Clinical data from medical records were abstracted and used to undertake univariable and multivariable conditional logistic regression to develop a risk prediction model consisting of four variables chosen a priori. A weighted sum of the factors present was taken to create a risk index-the Scottish Epilepsy Deaths Study Score. Odds ratios were estimated with 95% confidence intervals (CIs). Here, 224 deceased cases (mean age 48 years, 114 male) and 224 matched living controls were compared. In univariable analysis, predictors of epilepsy-related death were recent epilepsy-related accident and emergency attendance (odds ratio 5.1, 95% CI 3.2-8.3), living in deprived areas (odds ratio 2.5, 95% CI 1.6-4.0), developmental epilepsy (odds ratio 3.1, 95% CI 1.7-5.7), raised Charlson Comorbidity Index score (odds ratio 2.5, 95% CI 1.2-5.2), alcohol abuse (odds ratio 4.4, 95% CI 2.2-9.2), absent recent neurology review (odds ratio 3.8, 95% CI 2.4-6.1) and generalized epilepsy (odds ratio 1.9, 95% CI 1.2-3.0). Scottish Epilepsy Deaths Study Score model variables were derived from the first four listed before, with Charlson Comorbidity Index ≥2 given 1 point, living in the two most deprived areas given 2 points, having an inherited or congenital aetiology or risk factor for developing epilepsy given 2 points and recent epilepsy-related accident and emergency attendance given 3 points. Compared to having a Scottish Epilepsy Deaths Study Score of 0, those with a Scottish Epilepsy Deaths Study Score of 1 remained low risk, with odds ratio 1.6 (95% CI 0.5-4.8). Those with a Scottish Epilepsy Deaths Study Score of 2-3 had moderate risk, with odds ratio 2.8 (95% CI 1.3-6.2). Those with a Scottish Epilepsy Deaths Study Score of 4-5 and 6-8 were high risk, with odds ratio 14.4 (95% CI 5.9-35.2) and 24.0 (95% CI 8.1-71.2), respectively. The Scottish Epilepsy Deaths Study Score may be a helpful tool for identifying adults at high risk of epilepsy-related death and requires external validation.

The Critical Success Index (CSI) and Gilbert Skill score (GS) are verification measures that are commonly used to check the accuracy of weather forecasting. In this article, we propose that they can also be used to simplify the joint interpretation of positive predictive value (PPV) and sensitivity estimates across diagnostic accuracy studies of epilepsy data. This is because CSI and GS each provide a single measure that takes the weather forecasting equivalent of PPV and sensitivity into account. We have re-analysed data from our recent systematic review of diagnostic accuracy studies of administrative epilepsy data using CSI and GS. We summarise the results and benefits of this approach.

PurposeTo ascertain the accuracy of using administrative healthcare data to identify epilepsy cases.MethodWe searched MEDLINE and Embase from 01/01/1975–03/07/2018 for studies evaluating the diagnostic accuracy of administrative data in identifying epilepsy cases using any disease coding system. Two authors independently screened studies, extracted data, and quality-assessed studies. We assessed PPV, sensitivity, NPV, and specificity. The primary analysis was narrative.ResultsThirty studies were included between 1989–2018. Risks of bias were low, high, and unclear in four, 14, and 12 studies, respectively. Coding systems included ICD-9, ICD-10 and Read Codes, with or without antiepileptic drugs (AEDs). PPVs included ranges of 5.2–100% (Canada), 32.7–96.0% (US), 47.0–100% (UK), and 37.0–88.0% (Norway). Sensitivities included ranges of 22.2–99.7% (Canada), 12.2–97.3% (US), and 79.0–94.0% (UK). Nineteen studies contained ≥1 algorithm with a PPV >80%. Sixteen studies contained ≥1 algorithm with a sensitivity >80%. PPV was highest in algorithms consisting of disease codes (ICD-10 G40–41, ICD-9 345) in combination with ≥1 AED. The addition of symptom codes to this (ICD-10 R56, ICD-9 780.3, 780.39) lowered PPV. Sensitivity was highest in algorithms consisting of symptom codes with ≥1 AED. Whilst using AEDs alone achieved high sensitivities, the associated PPVs were low. Most NPVs and specificities were >90%.ConclusionIn the first global systematic review of this topic, we conclude that it is reasonable to use administrative data to identify people with epilepsy in epidemiological research.mbizvogkm@gmail.com

Objective:­ This study was undertaken to investigate the trends and mechanisms of epilepsy-related deaths in Scotland, highlighting the proportion that were potentially avoidable. Methods:­ This was a retrospective observational data-linkage study of administrative data from 2009– 2016. We linked nationwide data encompassing mortality records, hospital admissions, outpatient attendance, antiepileptic drug (AED) prescriptions

This study was undertaken to investigate the trends and mechanisms of epilepsy‐related deaths in Scotland, highlighting the proportion that were potentially avoidable.

Our objective was to undertake a systematic review ascertaining the accuracy of using administrative healthcare data to identify epilepsy cases. We searched MEDLINE and Embase from 01/01/1975 to 03/07/2018 for studies evaluating the diagnostic accuracy of routinely collected healthcare data in identifying epilepsy cases. Any disease coding system in use since the International Classification of Diseases, Ninth Revision (ICD-9) was permissible. Two authors independently screened studies, extracted data, and quality-assessed studies. We assessed positive predictive value (PPV), sensitivity, negative predictive value (NPV), and specificity. The primary analysis was a narrative synthesis of review findings. Thirty studies were included, published between 1989 and 2018. Risks of bias were low, high, and unclear in 4, 14, and 12 studies, respectively. Coding systems included ICD-9, ICD-10, and Read Codes, with or without antiepileptic drugs (AEDs). PPVs included ranges of 5.2%-100% (Canada), 32.7%-96.0% (USA), 47.0%-100% (UK), and 37.0%-88.0% (Norway). Sensitivities included ranges of 22.2%-99.7% (Canada), 12.2%-97.3% (USA), and 79.0%-94.0% (UK). Nineteen studies contained at least one algorithm with a PPV >80%. Sixteen studies contained at least one algorithm with a sensitivity >80%. PPV was highest in algorithms consisting of disease codes (ICD-10 G40-41, ICD-9 345) in combination with one or more AEDs. The addition of symptom codes to this (ICD-10 R56; ICD-9 780.3, 780.39) lowered PPV. Sensitivity was highest in algorithms consisting of symptom codes with one or more AEDs. Although using AEDs alone achieved high sensitivities, the associated PPVs were low. Most NPVs and specificities were >90%. We conclude that it is reasonable to use administrative data to identify people with epilepsy (PWE) in epidemiological research. Studies prioritizing high PPVs should focus on combining disease codes with AEDs. Studies prioritizing high sensitivities should focus on combining symptom codes with AEDs. We caution against the use of AEDs alone to identify PWE.

BACKGROUND:We investigate the case-ascertainment accuracy for potentially active epilepsy of four administrative healthcare datasets used to identify deceased adults in Scotland.METHODS:In this diagnostic accuracy study, unique patient identifiers were used to link administrative healthcare data for adults (aged 16 years and over) who died in Scotland between 01/01/09-01/01/16. Cases were ascertained from linking mortality records, hospital admissions, antiepileptic drug (AED) prescriptions, and primary care attendances. We assessed ICD-10 codes G40 (epilepsy), G41 (status epilepticus), and R56.8 (seizures) listed as causes of death and as hospital admission reasons, various AEDs, and F25 primary care epilepsy Read codes. These epilepsy indicators were searched through 01/01/09-01/01/16, suggesting active epilepsy during a maximal period of seven years before death. They were compared to epilepsy diagnoses made from medical records reviewed by a senior epileptologist, with a second senior epileptologist independently reviewing the medical records in a 10 % sample to check for specialist interrater agreement in epilepsy diagnoses. We validated how accurately epilepsy was identified by each dataset alone and when combined, calculating positive predictive value (PPV) and sensitivity (with 95 % confidence intervals (CIs)).RESULTS:159,032 deceased potential epilepsy cases were captured across the four datasets. Medical records reviewed in a random sample of 936 confirmed that epilepsy was present in 614 and absent in 322. Specialist interrater diagnostic agreement was substantial (100 medical records reviewed in duplicate, kappa = 0.72, CI 0.58-0.86). G40-41 cause of death codes had a PPV of 86 % (CI 84-89 %) and sensitivity of 73 % (CI 69-76 %). Adding R56.8 lowered PPV to 69 % (CI 65-72 %) and raised sensitivity to 87 % (CI 84-90 %). The optimal algorithm combining two datasets consisted of F25 Read codes paired with AEDs (PPV 86 % (CI 80-92 %), sensitivity 93 % (CI 88-97 %)). Also effective was pairing G40-41 and/or R56.8 cause of death codes with AEDs (PPV 91 % (CI 89-94 %), sensitivity 81 % (CI 77-84 %)). Whilst algorithms combining three datasets raised PPV to as high as 93-95 %, the associated sensitivities were low (71 % at most).CONCLUSIONS:Routinely-collected Scottish data can accurately identify epilepsy in deceased adults. It may be necessary to combine the diagnostic coding used with AEDs to ensure optimal case-ascertainment. The results help inform the design of future Scottish epilepsy mortality studies recruiting from administrative data sources.

Purpose This systematic review of epilepsy mortality systematic reviews evaluates comparative risks, causes, and risk factors for all-cause mortality in people with epilepsy (PWE) to specifically establish the burden of epilepsy-related deaths. Method MEDLINE and Embase were searched from conception to 26/12/2018 for systematic reviews evaluating all-cause mortality in PWE of any age. Two authors performed independent study selection, data extraction and quality assessment. Deaths were separated into epilepsy-related and unrelated using a recently published classification system. Outcomes included standardised mortality ratio (SMR) and mortality rate (MR) in a primary analysis of comparative risks, causes, and risk factors for all-cause and epilepsy-related mortality. A narrative synthesis of review findings was used to present results. Results Six moderate- or high-quality systematic reviews were included, evaluating 103 observational studies. All-cause mortality remained similarly high between 1950 and present (median SMR range 2.2–3.4). Africa had the highest SMR (median 5.4, range 2.6–7.2). SMRs were also higher for children < 18 years (median 7.5, range 3.1–22.4) than adults (median 2.6, range 1.3–8.7), and for epilepsy-related (median 3.8, range 0.0–82.4) than unrelated causes (median 1.7, range 0.7–17.6). Structural brain disease conferred the greatest risk for all-cause mortality (SMR range 24.0–41.5). Common epilepsy-related death causes included SUDEP, alcohol, drowning, pneumonia, and suicide. Conclusion Premature all-cause mortality remains a major problem in PWE globally, particularly in children and young adults, with most being epilepsy-related and potentially preventable. mbizvogkm@gmail.com

Background We describe a patient copresenting with epilepsia partialis continua, tuberculosis, and hemophagocytic lymphohistiocytosis. To our knowledge, this is the first documented case of this triad. Case presentation A 54-year-old black South African woman presented to a hospital in Scotland with an acute history of right-sided facial twitching, breathlessness, and several months of episodic night sweats. Clinical examination revealed pyrexia and continuous, stereotyped, right-sided facial contractions. These worsened with speech and continued through sleep. A clinical diagnosis of epilepsia partialis continua was made, and we provide a video of her seizures. Computed tomographic imaging of the chest and serous fluid analyses were consistent with a diagnosis of disseminated Mycobacterium tuberculosis . An additional diagnosis of hemophagocytic lymphohistiocytosis was made following the identification of pancytopenia and hyperferritinemia in peripheral blood, with hemophagocytosis evident in bone marrow investigation. We provide images of her hematopathology. The patient was extremely unwell and was hospitalized for 6 months, including two admissions to the intensive care unit for ventilatory support. She was treated successfully with high doses of antiepileptic drugs (benzodiazepines, levetiracetam, and phenytoin) and 12 months of oral antituberculosis therapy, and she underwent chemotherapy with 8 weeks of etoposide and dexamethasone for hemophagocytic lymphohistiocytosis, followed by 12 months of cyclosporine and prednisolone. Conclusions This combination of pathologies is unusual, and this case report helps educate clinicians on how such a patient may present and be managed. A lack of evidence surrounding the coexpression of this triad may represent absolute rarity, underdiagnosis, or incomplete case ascertainment due to early death caused by untreated tuberculosis or hemophagocytic lymphohistiocytosis. Further research is needed.

PurposeQuantify avoidable epilepsy-related mortality in adults (aged ≥ 16 years).MethodWe identified adult epilepsy-related deaths occurring between 01/01/2009–2016 in Scotland by linking death certificates to administrative primary and secondary care datasets. International Classification of Disease (ICD-10) codes (G40–41, R56.8), Read Codes (F25..), and antiepileptic drugs (AED) were examined in the linked dataset to identify potential epilepsy patients, using clinical data from medical records as a diagnostic reference to calculate positive predictive values (PPV). EPRDs were determined from death certificates (including post-mortem indicators) and medical records, estimating their standardised mortality ratio (SMR) and mortality rate (MR). The Office for National Statistics’ Revised Definition of Avoidable Mortality Causes 2016 was used to identify potentially avoidable EPRDs.ResultsCombining AED prescription with death certificate epilepsy codes G40–41 had the highest PPV (99%) for epilepsy diagnosis. There were 2,149 EPRDs. Age-standardised MR per 100,000 ranged between 6.8 (95%CI 6.0–7.6) in 2009 and 9.1 (95%CI 8.2–9.9) in 2015. SMR was higher in young adults (≤55 years), peaking at 6 (95%CI 2.3–9.7) in the 16–24-year-old group. There were 579 EPRDs in those aged ≤55 years. 31% were SUDEP, 26% respiratory causes (mainly aspiration pneumonias), and 15% mental disorders (mostly alcohol-related). 79% of all EPRDs were potentially avoidable. Commonest modifiable factors were absent epilepsy specialist input, poor patient education, and drug errors.ConclusionThe burden of avoidable epilepsy-related mortality remains high, particularly in young adults.

Introduction In an increasingly digital age for healthcare around the world, administrative data have become rich and accessible tools for potentially identifying and monitoring population trends in diseases including epilepsy. However, it remains unclear (1) how accurate administrative data are at identifying epilepsy within a population and (2) the optimal algorithms needed for administrative data to correctly identify people with epilepsy within a population. To address this knowledge gap, we will conduct a novel systematic review of all identified studies validating administrative healthcare data in epilepsy identification. We provide here a protocol that will outline the methods and analyses planned for the systematic review. Methods and analysis The systematic review described in this protocol will be conducted to follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. MEDLINE and Embase will be searched for studies validating administrative data in epilepsy published from 1975 to current (01 June 2018). Included studies will validate the International Classification of Disease (ICD), Ninth Revision (ICD-9) onwards (ie, ICD-9 code 345 and ICD-10 codes G40–G41) as well as other non-ICD disease classification systems used, such as Read Codes in the UK. The primary outcome will be providing pooled estimates of accuracy for identifying epilepsy within the administrative databases validated using sensitivity, specificity, positive and negative predictive values, and area under the receiver operating characteristic curves. Heterogeneity will be assessed using the I2 statistic and descriptive analyses used where this is present. The secondary outcome will be the optimal administrative data algorithms for correctly identifying epilepsy. These will be identified using multivariable logistic regression models. 95% confidence intervals will be quoted throughout. We will make an assessment of risk of bias, quality of evidence, and completeness of reporting for included studies. Ethics and dissemination Ethical approval is not required as primary data will not be collected. Results will be disseminated in peer-reviewed journals, conference presentations and in press releases. PROSPERO registration CRD42017081212.