Epilepsy surgery in adults older than 50 years: A systematic review and meta-analysis.

OBJECTIVE: Despite the general safety and efficacy of epilepsy surgery, there is evidence that epilepsy surgery remains underutilized. Although there are an increasing number of studies reporting epilepsy surgery in older adults, there is no consensus on whether epilepsy surgery is efficacious or safe for this population. Our objective was to systematically assess the efficacy as well as safety of resective surgery in people aged 50 years or older with drug-resistant epilepsy. METHODS: We considered studies that examine the efficacy and safety of epilepsy surgery in adults aged 50 years and older. Study eligibility was limited to studies carried out after 1990, with a minimum of 10 participants and 6 months of follow-up. We searched the following databases for published studies: Ovid MEDLINE, Ovid Embase, Cumulative Index to Nursing and Allied Health Literature, PsychInfo, and Web of Science Conference Proceedings Citation Index - Science. The risk of bias of each included study was independently assessed by two reviewers using the MINORS (Methodological Index for Non-Randomized Studies) instrument. RESULTS: Eleven case series and 14 cohort studies met the criteria for inclusion, for a total of 1111 older adults who underwent epilepsy surgery along with 4111 adults younger than 50 years as control groups. The pooled cumulative incidence of older adults achieving seizure freedom after resective surgery was 70.1% (95% confidence interval [CI] = 65.3-74.7). There was no evident difference in the incidence of seizure freedom among older adults as compared to younger adults (risk ratio [RR] = 1.05, 95% CI = .97-1.14) in cohort studies. The pooled cumulative incidence of perioperative complications in older adults was 26.2% (95% CI = 21.3-31.7). Among them, 7.5% (95% CI = 5.8-9.5) experienced major complications. Older adults were significantly more at risk of experiencing any complication than younger adults (RR = 2.8, 95% CI = 1.5-5.4). SIGNIFICANCE: Despite important considerations, epilepsy surgery may be considered appropriate among carefully selected individuals older than 50 years.

Excess health care use is significantly and persistently reduced following diagnosis of late-onset epilepsy.

OBJECTIVE: The incidence of late-onset epilepsy (LOE) is rising, and these patients may use an excess of health care resources. This study aimed to measure pre-/post-diagnostic health care use (HCU) for patients with LOE compared to controls. METHODS: This was an observational open cohort study covering years 1998-2019 using UK population-based linked primary care (Clinical Practice Research Datalink [CPRD]) and hospital (HES) electronic health records. The participants included patients with incident LOE enrolled in CPRD and 1:10 age-, sex-, and general practice-matched controls. The exposure was incident LOE (diagnosed at age ≥65) using a 5-year washout. The main outcome was all HCU (primary care [PC], accident and emergency [A&E], admitted patient and outpatient care) using inverse proportional weighting to PC use and HCU by setting. An interrupted time-series analysis was used to examine pre-/post-diagnostic HCU between patients with LOE and controls over 4 years either side of diagnosis/matching date. An adjusted mixed-effects negative binomial regression was used for post-diagnosis HCU interactions. RESULTS: Of 2 569 874 people ≥65 years of age, 1048 (4%) developed incident LOE. Mean weighted total HCU increased by 32 visits per patient-year (95% confidence interval [95% CI]: 13-50, p = .003) until LOE diagnosis, and then dropped by a mean of 60 visits per patient-year (95% CI: -81 to -40). There was an acute rise and fall over the 1-2 years immediately pre-/post-diagnosis. Incident HCU remained higher for LOE compared to controls post-diagnosis (adjusted incidence rate ratio: 1.72; 95% CI: 1.65-1.70; p < .001), including A&E, outpatient, and admitted care. SIGNIFICANCE: Health care use demonstrates an acute on chronic rise over the 4 years before diagnosis of LOE. To what extent the partial reversal of the acute pre-diagnosis rise, and the mediators of the accelerated increase compared to controls are attributed to epilepsy, comorbid and bidirectional disease states, or a combination of both warrants further exploration.

The Evolving Role of Electroencephalography in Postarrest Care.

Electroencephalography is an accessible, portable, noninvasive and safe means of evaluating a patient's brain activity. It can aid in diagnosis and management decisions for post-cardiac arrest patients with seizures, myoclonus and other non-epileptic movements. It also plays an important role in a multimodal approach to neuroprognostication predicting both poor and favorable outcomes. Individuals ordering, performing and interpreting these tests, regardless of the indication, should understand the supporting evidence, logistical considerations, limitations and impact the results may have on postarrest patients and their families as outlined herein.

Screening for depression in patients with epilepsy: same questions but different meaning to different patients.

PURPOSE: Patient-reported outcome measures (PROMs) such as the Neurological Disorders Depression Inventory in Epilepsy (NDDI-E), a 6-item epilepsy-specific PROM, is used to screen for major depressive disorder symptoms for patients with epilepsy (PWE). The validity and interpretation of PROMs can be affected by differential item functioning (DIF), which occurs when subgroups of patients with the same underlying health status respond to and interpret questions about their health status differently. This study aims to determine whether NDDI-E items exhibit DIF and to identify subgroups of PWE that exhibit DIF in NDDI-E items. METHODS: Data were from the Calgary Comprehensive Epilepsy Program database, a clinical registry of adult PWE in Calgary, Canada. A tree-based partial credit model based on recursive partitioning (PCTree) was used to identify subgroups that exhibit DIF on NDDI-E items using patients' characteristics as covariates. Differences in the identified subgroups were characterized using multinomial logistic regression. RESULTS: Of the 1,576 patients in this cohort, 806 (51.1%) were female, and the median age was 38.0 years. PCTree identified four patient subgroups defined by employment status, age, and sex. Subgroup 1 were unemployed patients ≤ 26 years old, subgroup 2 were unemployed patients > 26 years, subgroup 3 were employed females, while subgroup 4 were employed male patients. The subgroups exhibited significant differences on education level, comorbidity index scores, marital status, type of epilepsy, and driving status. CONCLUSION: PWE differed in their interpretation and responses to questions about their depression symptoms, and these differences were a function of sociodemographic and clinical characteristics.

Patient-reported depression screening measures are prone to differential item functioning (DIF), which occurs when patients with the same levels of depression respond to and interpret the questions on the measures differently, as a result of different socio-demographic characteristics. Heterogeneity in how epilepsy patients interpret and respond to depression measures, if not identified, could lead to measurement biases that threaten the validity of inferences from patient-reported outcome measure scores to inform clinical and healthcare decisions for epilepsy management. Using data from a clinical registry of patients with epilepsy, the tree-based item response theory model was used to examine the presence of DIF in the items comprising the patient-reported Neurological Disorders Depression Inventory in Epilepsy (NDDI-E) scale. Four subgroups of epilepsy patients were noted to exhibit DIF on NDDI-E items. These groups were defined by interactions among employment status, age and sex. These characteristics affect the interpretation of NDDI-E item scores. It is recommended that clinicians carefully scrutinize responses to individual items alongside the overall NDDI-E score in different patient groups to inform clinical decisions for epilepsy management.

Medical and surgical treatment of epilepsy in older adults: A national survey.

OBJECTIVE: There are no clinical guidelines dedicated to the treatment of epilepsy in older adults. We investigated physician opinion and practice regarding the treatment of people with epilepsy aged 65 years or older. We also sought to study how our opinion and practice varied between geriatricians, general neurologists, and epilepsy neurologists (i.e., epileptologists). METHODS: We initially piloted our survey to measure test-retest reliability. Once finalized, we disseminated the survey via two rounds of facsimiles, and then conventional mail, to eligible individuals listed in a national directory of Canadian physicians. We used descriptive statistics such as stacked bar charts and tables to illustrate our findings. RESULTS: One hundred forty-four physicians (104 general neurologists, 25 geriatricians, and 15 epileptologists) answered our survey in its entirety (overall response rate of 13.2%). Levetiracetam and lamotrigine were the preferred antiseizure medications (ASMs) to treat older adults with epilepsy. Two thirds of epileptologists and almost half of general neurologists would consider prescribing lacosamide in >50% of people aged >65 years; only one geriatrician was of the same opinion. More than 40% of general neurologists and geriatricians erroneously believed that none of the ASMs mentioned in our survey was previously studied in randomized controlled trials specific to the treatment of epilepsy in older adults. Epileptologists were more likely as compared to general neurologists and geriatricians to recommend epilepsy surgery (e.g., 66.6% vs. 22.9%-37.5% among older adults). SIGNIFICANCE: Therapeutic decisions for older adults with epilepsy are heterogeneous between physician groups and sometimes misalign with available clinical evidence. Our surveyed physicians differed in their approach to ASM choice as well as perception of surgery in older adults with epilepsy. These findings likely reflect the lack of clinical guidelines dedicated to this population and the deficient implementation of best practices.

Machine learning using multimodal clinical, electroencephalographic, and magnetic resonance imaging data can predict incident depression in adults with epilepsy: A pilot study.

OBJECTIVE: This study was undertaken to develop a multimodal machine learning (ML) approach for predicting incident depression in adults with epilepsy. METHODS: We randomly selected 200 patients from the Calgary Comprehensive Epilepsy Program registry and linked their registry-based clinical data to their first-available clinical electroencephalogram (EEG) and magnetic resonance imaging (MRI) study. We excluded patients with a clinical or Neurological Disorders Depression Inventory for Epilepsy (NDDI-E)-based diagnosis of major depression at baseline. The NDDI-E was used to detect incident depression over a median of 2.4 years of follow-up (interquartile range [IQR] = 1.5-3.3 years). A ReliefF algorithm was applied to clinical as well as quantitative EEG and MRI parameters for feature selection. Six ML algorithms were trained and tested using stratified threefold cross-validation. Multiple metrics were used to assess model performances. RESULTS: Of 200 patients, 150 had EEG and MRI data of sufficient quality for ML, of whom 59 were excluded due to prevalent depression. Therefore, 91 patients (41 women) were included, with a median age of 29 (IQR = 22-44) years. A total of 42 features were selected by ReliefF, none of which was a quantitative MRI or EEG variable. All models had a sensitivity > 80%, and five of six had an F1 score ≥ .72. A multilayer perceptron model had the highest F1 score (median = .74, IQR = .71-.78) and sensitivity (84.3%). Median area under the receiver operating characteristic curve and normalized Matthews correlation coefficient were .70 (IQR = .64-.78) and .57 (IQR = .50-.65), respectively. SIGNIFICANCE: Multimodal ML using baseline features can predict incident depression in this population. Our pilot models demonstrated high accuracy for depression prediction. However, overall performance and calibration can be improved. This model has promise for identifying those at risk for incident depression during follow-up, although efforts to refine it in larger populations along with external validation are required.

A Prospective Post-Marketing Observational Study of Brivaracetam in People With Focal Epilepsy.

We evaluated the effectiveness and tolerability of brivaracetam (BRV), an adjunctive antiseizure medication, as a treatment for focal epilepsy in adults. In this prospective study, we enrolled 51 participants from 3 sites across Canada. At 6 months, 68% (26/38) of participants were still taking BRV, among whom 35% (8/23) attained seizure freedom and 48% (11/23) saw their seizure frequency reduced by over 50%. We did not measure any significant change in irritability, quality of life, depression, and anxiety while treated with BRV. Our findings suggest BRV is effective in reducing seizure frequency among adults with focal epilepsy.

Association between antiseizure medications and quality of life in epilepsy: A mediation analysis.

OBJECTIVE: The relationship between antiseizure medications (ASMs), which improve health outcomes by controlling seizures, and health-related quality of life (HRQOL) is poorly understood and may involve intermediate variables. We evaluated the potential mediators of the association between ASMs and HRQOL. METHODS: Data are from an outpatient registry of adult patients with epilepsy seen at the Foothills Medical Center, Calgary, Alberta, Canada. Quality of life was measured using the 10-item Quality of Life in Epilepsy, and depression was measured using the Neurological Disorders Depression Inventory for Epilepsy. Propensity score matching was used to adjust for covariate imbalance between patients who received a single ASM (monotherapy) and those who received two or more ASMs (polytherapy) due to confounding. Mediation analysis was used to estimate the mediating effects of depression and ASM side effects on the association between patients' ASM polytherapy and HRQOL. RESULTS: Of 778 patients included in this analysis, 274 (35.2%) were on two or more ASMs. Patient-reported depression and ASM side effects jointly mediated the association between ASMs and HRQOL; these mediators accounted for 42% of the total average effect of ASM polytherapy ( ß  = -13.6, 95% confidence interval = -18.2 to -8.6) on HRQOL. SIGNIFICANCE: These findings highlight the importance of managing depression and ASM side effects for improving health outcomes of patients requiring treatment with ASMs. Intervention programs aimed at improving HRQOL of patients with epilepsy need to target these potential mediators.

Validity and reliability of global ratings of satisfaction with epilepsy surgery.

OBJECTIVE: We aimed to assess the reliability and validity of single-item global ratings (GR) of satisfaction with epilepsy surgery. METHODS: We recruited 240 patients from four centers in Canada and Sweden who underwent epilepsy surgery ≥1 year earlier. Participants completed a validated questionnaire on satisfaction with epilepsy surgery (the ESSQ-19), plus a single-item GR of satisfaction with epilepsy surgery twice, 4-6 weeks apart. They also completed validated questionnaires on quality of life, depression, health state utilities, epilepsy severity and disability, medical treatment satisfaction and social desirability. Test-retest reliability of the GR was assessed with the intra-class correlation coefficient (ICC). Construct and criterion validity were examined with polyserial correlations between the GR measure of satisfaction and validated questionnaires and with the ESSQ-19 summary score. Non-parametric rank tests evaluated levels of satisfaction, and ROC analysis assessed the ability of GRs to distinguish among clinically different patient groups. RESULTS: Median age and time since surgery were 42 years (IQR 32-54) and 5 years (IQR 2-8), respectively. The GR demonstrated good to excellent test-retest reliability (ICC = 0.76; 95% CI 0.67-0.84) and criterion validity (0.85; 95% CI 0.81-0.89), and moderate correlations in the expected direction with instruments assessing quality of life (0.59; 95% CI 0.51-0.63), health utilities (0.55; 95% CI 0.45-0.65), disability (-0.51; 95% CI -0.41, -0.61), depression (-0.48; 95% CI -0.38, -0.58), and epilepsy severity (-0.48; 95% CI -0.38, -0.58). As expected, correlations were lower for social desirability (0.40; 95% CI 0.28-0.52) and medical treatment satisfaction (0.33; 95% CI 0.21-0.45). The GR distinguished participants who were seizure-free (AUC 0.75; 95% CI 0.67-0.82), depressed (AUC 0.75; 95% CI 0.67-0.83), and self-rated as having more severe epilepsy (AUC 0.78; 95% CI 0.71-0.85) and being more disabled (AUC 0.82; 95% CI 0.74-0.90). SIGNIFICANCE: The GR of epilepsy surgery satisfaction showed good measurement properties, distinguished among clinically different patient groups, and appears well-suited for use in clinical practice and research.

Timing of referral to evaluate for epilepsy surgery: Expert Consensus Recommendations from the Surgical Therapies Commission of the International League Against Epilepsy.

Epilepsy surgery is the treatment of choice for patients with drug-resistant seizures. A timely evaluation for surgical candidacy can be life-saving for patients who are identified as appropriate surgical candidates, and may also enhance the care of nonsurgical candidates through improvement in diagnosis, optimization of therapy, and treatment of comorbidities. Yet, referral for surgical evaluations is often delayed while palliative options are pursued, with significant adverse consequences due to increased morbidity and mortality associated with intractable epilepsy. The Surgical Therapies Commission of the International League Against Epilepsy (ILAE) sought to address these clinical gaps and clarify when to initiate a surgical evaluation. We conducted a Delphi consensus process with 61 epileptologists, epilepsy neurosurgeons, neurologists, neuropsychiatrists, and neuropsychologists with a median of 22 years in practice, from 28 countries in all six ILAE world regions. After three rounds of Delphi surveys, evaluating 51 unique scenarios, we reached the following Expert Consensus Recommendations: (1) Referral for a surgical evaluation should be offered to every patient with drug-resistant epilepsy (up to 70 years of age), as soon as drug resistance is ascertained, regardless of epilepsy duration, sex, socioeconomic status, seizure type, epilepsy type (including epileptic encephalopathies), localization, and comorbidities (including severe psychiatric comorbidity like psychogenic nonepileptic seizures [PNES] or substance abuse) if patients are cooperative with management; (2) A surgical referral should be considered for older patients with drug-resistant epilepsy who have no surgical contraindication, and for patients (adults and children) who are seizure-free on 1-2 antiseizure medications (ASMs) but have a brain lesion in noneloquent cortex; and (3) referral for surgery should not be offered to patients with active substance abuse who are noncooperative with management. We present the Delphi consensus results leading up to these Expert Consensus Recommendations and discuss the data supporting our conclusions. High level evidence will be required to permit creation of clinical practice guidelines.

EEG Patterns and Outcomes After Hypoxic Brain Injury: A Systematic Review and Meta-analysis.

Electroencephalography (EEG) is used to prognosticate recovery in comatose patients with hypoxic ischemic brain injury (HIBI) secondary to cardiac arrest. We sought to determine the prognostic use of specific EEG patterns for predicting disability and death following HIBI secondary to cardiac arrest. This systematic review searched Medline, Embase, and Cochrane Central up to January 2020. We included original research involving prospective and retrospective cohort studies relating specific EEG patterns to disability and death in comatose adult patients suffering HIBI post cardiac arrest requiring admission to an intensive care setting. We evaluated study quality using the Quality of Diagnostic Accuracy Studies 2 tool. Descriptive statistics were used to summarize study, patient, and EEG characteristics. We pooled study-level estimates of sensitivity and specificity for EEG patterns defined a priori using a random effect bivariate and univariate meta-analysis when appropriate. Funnel plots were used to assess publication bias. Of 5191 abstracts, 333 were reviewed in full text, of which 57 were included in the systematic review and 32 in meta-analyses. No reported EEG pattern was found to be invariably associated with death or disability across all studies. Pooled specificities of status epilepticus, burst suppression, and electrocerebral silence were high (92-99%), but sensitivities were low (6-39%) when predicting a composite outcome of disability and death. Study quality varied depending on domain; patient flow and timing performed was well conducted in all, whereas EEG interpretation was retrospective in 17 of 39 studies. Accounting for variable study quality, EEG demonstrates high specificity with a low risk of false negative outcome attribution for disability and death when status epilepticus, burst suppression, or electrocerebral silence is detected. Increased use of standardized cross-study protocols and definitions of EEG patterns are required to better evaluate the prognostic use of EEG for comatose patients with HIBI following cardiac arrest.

Precision Medicine: Academic dreaming or clinical reality?

Precision medicine can be distilled into a concept of accounting for an individual's unique collection of clinical, physiologic, genetic, and sociodemographic characteristics to provide patient-level predictions of disease course and response to therapy. Abundant evidence now allows us to determine how an average person with epilepsy will respond to specific medical and surgical treatments. This is useful, but not readily applicable to an individual patient. This has brought into sharp focus the desire for a more individualized approach through which we counsel people based on individual characteristics, as opposed to population-level data. We are now accruing data at unprecedented rates, allowing us to convert this ideal into reality. In addition, we have access to growing volumes of administrative and electronic health records data, biometric, imaging, genetics data, microbiome, and other "omics" data, thus paving the way toward phenome-wide association studies and "the epidemiology of one." Despite this, there are many challenges ahead. The collating, integrating, and storing sensitive multimodal data for advanced analytics remains difficult as patient consent and data security issues increase in complexity. Agreement on many aspects of epilepsy remains imperfect, rendering models sensitive to misclassification due to a lack of "ground truth." Even with existing data, advanced analytics models are prone to overfitting and often failure to generalize externally. Finally, uptake by clinicians is often hindered by opaque, "black box" algorithms. Systematic approaches to data collection and model generation, and an emphasis on education to promote uptake and knowledge translation, are required to propel epilepsy-based precision medicine from the realm of the theoretical into routine clinical practice.

Understanding cardiovascular disease in older adults with epilepsy.

OBJECTIVE: This study was undertaken to investigate the distribution of social, lifestyle/behavior, and chronic disease risk factors for cardiovascular disease (CVD) in people with epilepsy as compared to the general population. We also measured the cross-sectional association between epilepsy and CVD in older adults, with and without adjustments for a history of stroke. METHODS: We analyzed data for 44 817 participants in the Canadian Longitudinal Study on Aging, including 751 individuals with a lifetime history of epilepsy. We modeled associations using ordinal and binomial logistic regression, as well as log-binomial regression, with multiple imputation for missing data. We measured the attributable fraction of CVD burden due to stroke. RESULTS: The majority of the CVD risk factors were significantly more prevalent in people with epilepsy as compared to the general population without epilepsy, independent of age and sex. After adjusting for a history of stroke, people with epilepsy had a significantly higher prevalence of heart disease (prevalence ratio [PR] = 1.27, 95% confidence interval [CI] = 1.02-1.57) and peripheral vascular disease (PR = 1.88, 95% CI = 1.50-2.36). Stroke accounted for 36% (95% CI = 19.85-48.76) of the increased prevalence of any CVD among people with epilepsy, similar to the 32% (95% CI = 27.82-36.25) among people without epilepsy. After adjustment for all other CVD risk factors, peripheral vascular disease remained significantly more prevalent (PR = 1.65, 95% CI = 1.28-2.12) in people with epilepsy as compared to those without. SIGNIFICANCE: CVD risk factors are more prevalent in people with epilepsy, independent of age and sex, and the association between epilepsy and CVD is independent of the association between epilepsy and stroke. The association between peripheral vascular disease and epilepsy may differ from the associations with other types of CVD. These findings are important steps in more comprehensively understanding the origins of CVD in people with epilepsy.

Adult onset epilepsy is defined by phenotypic clusters with unique comorbidities and risks of death.

OBJECTIVE: This study was undertaken to identify clusters of adult onset epilepsy with distinct comorbidities and risks of early and late death. METHODS: This was a retrospective open cohort study that included all adults meeting a case definition for epilepsy after the Acceptable Mortality Recording date in the Health Improvement Network database for the years 2000-2012 inclusive. Unsupervised agglomerative hierarchical clustering was performed to identify unique clusters of patients based on their predicted risk of early (<4 years of epilepsy diagnosis) and late (≥4 years from diagnosis) mortality and patient-level clinical characteristics. RESULTS: We identified 10 499 presumed incident cases of epilepsy from 11 194 182 patients. Four phenotypic clusters were identified in the early and late risk periods. Early clusters include older adults with cardiovascular disease and a high risk of death (median predicted risk = 20%, interquartile range [IQR] = 9%-31%), a group with moderate risk of death and cancer (median predicted risk = 6%, IQR = 2%-15%), a group with psychiatric disease/substance use and few somatic comorbidities (median predicted risk = 5%, IQR = 2%-9%), and one with a younger age at onset and few comorbidities (median predicted risk = 4%, IQR = 1%-11%). There was minimal movement of individuals between clusters for those surviving the early risk period. Age- and sex-standardized 3-year mortality ratios were more than sixfold higher than the general population for every cluster, even those primarily comprised of healthy younger adults. SIGNIFICANCE: Adult onset epilepsy is marked by unique clusters of comorbid conditions and elevated risks of death that form discrete populations for targeted therapeutic interventions. These clusters remain relatively stable between the early and late mortality risk periods. Of particular interest are the clusters marked by young and otherwise healthy adults whose standardized mortality ratio is sixfold higher than general population despite few conventional risk factors for premature death.

Quality of Life in Epilepsy: Same questions, but different meaning to different people.

OBJECTIVES: Patient-reported outcome measures (PROMs) are used widely to elicit patient's self-appraisal of their health status and quality of life. One fundamental assumption when measuring PROMs is that all individuals interpret questions about their health status in a consistent manner. However, subgroups of patients with a similar health condition may respond differently to PROM questions (ie, differential item functioning [DIF]), leading to biased estimates of group differences on PROM scores. Understanding these differences can help inform the clinical interpretation of PROMs. This study examined whether DIF affects 10-item Quality of Life in Epilepsy (QOLIE10) scores reported by patients with epilepsy in outpatient clinics. METHODS: Data were from the Calgary Comprehensive Epilepsy Program, a prospective registry of patients with epilepsy in Calgary, Alberta. Latent variable mixture models (LVMMs) based on standard two-parameter graded response models with increasing numbers of latent classes were applied to QOLIE10 item data. Model fit was assessed using the Bayesian Information Criterion (BIC) and latent class model entropy. Ordinal logistic regression was used to identify QOLIE10 items that exhibited DIF. RESULTS: In this cohort of 1143 patients, 567 (49.6%) were female and the median age was 37.0 (interquartile range [IQR] 27.0) years. A two-class LVMM, which provided the best fit to the data, identified two subgroups of patients with different response patterns to QOLIE10 items, with class proportions of 0.62 and 0.38. The two subgroups differed with respect to antiseizure polytherapy, reported medication side effects, frequency of seizures, and psychiatric comorbidities. QOLIE10 items on the physical and psychological side effects of medication exhibited large DIF effects. SIGNIFICANCE: Our study revealed two different response patterns to quality-of-life instruments, suggesting heterogeneity in how patients interpret some of the questions. Researchers and users of PROMs in epilepsy need to consider the differential interpretation of items for various instruments to ensure valid understanding and comparisons of PROM scores.

Primary care electronic medical records can be used to predict risk and identify potentially modifiable factors for early and late death in adult onset epilepsy.

OBJECTIVE: To use clinically informed machine learning to derive prediction models for early and late premature death in epilepsy. METHODS: This was a population-based primary care observational cohort study. All patients meeting a case definition for incident epilepsy in the Health Improvement Network database for inclusive years 2000-2012 were included. A modified Delphi process identified 30 potential risk factors. Outcome was early (within 4 years of epilepsy diagnosis) and late (4 years or more from diagnosis) mortality. We used regularized logistic regression, support vector machines, Gaussian naive Bayes, and random forest classifiers to predict outcomes. We assessed model calibration, discrimination, and generalizability using the Brier score, mean area under the receiver operating characteristic curve (AUC) derived from stratified fivefold cross-validation, plotted calibration curves, and extracted measures of association where possible. RESULTS: We identified 10 499 presumed incident cases from 11 194 182 patients. All models performed comparably well following stratified fivefold cross-validation, with AUCs ranging from 0.73 to 0.81 and from 0.71 to 0.79 for early and late death, respectively. In addition to comorbid disease, social habits (alcoholism odds ratio [OR] for early death = 1.54, 95% confidence interval [CI] = 1.12-2.11 and OR for late death = 2.62, 95% CI = 1.66-4.16) and treatment patterns (OR for early death when no antiseizure medication [ASM] was prescribed at baseline = 1.33, 95% CI = 1.07-1.64 and OR for late death after receipt of enzyme-inducing ASM at baseline = 1.32, 95% CI = 1.04-1.66) were significantly associated with increased risk of premature death. Baseline ASM polytherapy (OR = 0.55, 95% CI = 0.36-0.85) was associated with reduced risk of early death. SIGNIFICANCE: Clinically informed models using routine electronic medical records can be used to predict early and late mortality in epilepsy, with moderate to high accuracy and evidence of generalizability. Medical, social, and treatment-related risk factors, such as delayed ASM prescription and baseline prescription of enzyme-inducing ASMs, were important predictors.

Predicting postoperative epilepsy surgery satisfaction in adults using the 19-item Epilepsy Surgery Satisfaction Questionnaire and machine learning.

OBJECTIVE: The 19-item Epilepsy Surgery Satisfaction Questionnaire (ESSQ-19) is a validated and reliable post hoc means of assessing patient satisfaction with epilepsy surgery. Prediction models building on these data can be used to counsel patients. METHODS: The ESSQ-19 was derived and validated on 229 patients recruited from Canada and Sweden. We isolated 201 (88%) patients with complete clinical data for this analysis. These patients were adults (≥18 years old) who underwent epilepsy surgery 1 year or more prior to answering the questionnaire. We extracted each patient's ESSQ-19 score (scale is 0-100; 100 represents complete satisfaction) and relevant clinical variables that were standardized prior to the analysis. We used machine learning (linear kernel support vector regression [SVR]) to predict satisfaction and assessed performance using the R2 calculated following threefold cross-validation. Model parameters were ranked to infer the importance of each clinical variable to overall satisfaction with epilepsy surgery. RESULTS: Median age was 41 years (interquartile range [IQR] = 32-53), and 116 (57%) were female. Median ESSQ-19 global score was 68 (IQR = 59-75), and median time from surgery was 5.4 years (IQR = 2.0-8.9). Linear kernel SVR performed well following threefold cross-validation, with an R2 of .44 (95% confidence interval = .36-.52). Increasing satisfaction was associated with postoperative self-perceived quality of life, seizure freedom, and reductions in antiseizure medications. Self-perceived epilepsy disability, age, and increasing frequency of seizures that impair awareness were associated with reduced satisfaction. SIGNIFICANCE: Machine learning applied postoperatively to the ESSQ-19 can be used to predict surgical satisfaction. This algorithm, once externally validated, can be used in clinical settings by fixing immutable clinical characteristics and adjusting hypothesized postoperative variables, to counsel patients at an individual level on how satisfied they will be with differing surgical outcomes.

Impact of ictal subtraction SPECT and PET in presurgical evaluation.

OBJECTIVE: To assess the relative contribution of ictal subtraction single-photon emission computed tomography (ISSPECT) and 18 F-fluorodeoxyglucose positron emission tomography computed tomography (PET) in epilepsy surgery decision making. MATERIALS AND METHODS: A retrospective 3-year study of consecutive patients with resistant focal epilepsy who underwent ISSPECT and PET to evaluate to what extent these modalities influence decisions in epilepsy surgery and outcomes. ISSPECT imaging was performed in 106 patients and 58 (55%) had PET also. The clinical consensus (ClinC) was the final arbiter for decisions. Post-surgical outcomes were collected from follow-up clinics. Non-parametric statistics were used to assess association and logistic regression to evaluate prediction of outcomes. RESULTS: Of 106 patients, 60 were males (57%). MRI was non-lesional in 46 (43%). Concordance with ClinC was seen in 80 patients (76%) for ISSPECT, in 46 patients (79%) for PET, and in 37 patients (64%) for ISSPECT + PET. Fifty-six patients (53%) were planned for intracranial video-electroencephalography monitoring (IVEM). Those with ClinC-PET concordance were likely to proceed to IVEM (p = 0.02). ClinC-PET concordance and ClinC-ISSPECT concordance did not predict decision to proceed to surgery, but VEM-MRI concordance did in lesional cases (p = 0.018). Forty-five (42%) underwent surgery of which 29 had minimum follow-up for 1 year (mean, 20 months; SD, 8) and 22 (76%) had Engel class I outcomes. ClinC-ISSPECT concordance (p = 0.024) and VEM-MRI concordance (p = 0.016) predicted Engel class I outcomes. CONCLUSION: Those with ClinC-PET concordance were more likely to proceed with IVEM. ClinC-ISSPECT concordance and VEM-MRI concordance predicted good surgical outcomes.

Diagnostic accuracy of clinical signs and symptoms for psychogenic nonepileptic attacks versus epileptic seizures: A systematic review and meta-analysis.

BACKGROUND: Psychogenic nonepileptic attacks (PNEA) are events of altered behavior that resemble epileptic seizures (ES) but are not caused by abnormal electrical cortical activity. Understanding which clinical signs and symptoms are associated with PNEA may allow better triaging for video-electroencephalogram monitoring (VEM) and for a more accurate prediction when such testing is unavailable. METHODS: We performed a systematic review searching Medline, Embase, and Cochrane Central from inception to March 29, 2019. We included original research that reported at least one clinical sign or symptom, included distinct groups of adult ES and PNEA with no overlap, and used VEM for the reference standard. Two authors independently assessed quality of the studies using the Quality Assessment of Diagnostic Accuracy Studies tool. Pooled estimates of sensitivity and specificity of studies were evaluated using a bivariate random effects model. RESULTS: We identified 4028 articles, of which 33 were included. There was a female sex predominance in the PNEA population (n = 22). From our meta-analysis, pooled sensitivities (0.27-0.72) and specificities (0.51-0.89) for PNEA were modest for individual signs. History of sexual abuse had the highest pooled specificity (89%), while the most sensitive feature was female sex (72%). Individual studies (n = 4) reported high levels of accuracy for ictal eye closure (sensitivity 64-73.7% and specificity 76.9-100%) and post-traumatic stress disorder (no reported sensitivity or specificity). Assuming the pre-test probability for PNEA in a tertiary care epilepsy center is 14%, even the strongest meta-analyzed features only exert modest diagnostic value, increasing post-test probabilities to a maximum of 33%. CONCLUSIONS: This review reflects the limited certainty afforded by individual clinical features to distinguish between PNEA and ES. Specific demographic and comorbid features, even despite moderately high specificities, impart minimal impact on diagnostic decision making. This emphasizes the need for the development of multisource predictive tools to optimize diagnostic likelihood ratios.

Risk factors for dementia development, frailty, and mortality in older adults with epilepsy - A population-based analysis.

OBJECTIVE: Although the prevalence of comorbid epilepsy and dementia is expected to increase, the impact is not well understood. Our objectives were to examine risk factors associated with incident dementia and the impact of frailty and dementia on mortality in older adults with epilepsy. METHODS: The CALIBER scientific platform was used. People with incident epilepsy at or after age 65 were identified using Read codes and matched by age, sex, and general practitioner to a cohort without epilepsy (10:1). Baseline cohort characteristics were compared using conditional logistic regression models. Multivariate Cox proportional hazard regression models were used to examine the impact of frailty and dementia on mortality, and to assess risk factors for dementia development. RESULTS: One thousand forty eight older adults with incident epilepsy were identified. The odds of having dementia at baseline were 7.39 [95% CI 5.21-10.50] times higher in older adults with epilepsy (n = 62, 5.92%) compared to older adults without epilepsy (n = 88, 0.86%). In the final multivariate Cox model (n = 326), age [HR: 1.20, 95% CI 1.09-1.32], Charlson comorbidity index score [HR: 1.26, 95% CI 1.10-1.44], and sleep disturbances [HR: 2.41, 95% CI 1.07-5.43] at baseline epilepsy diagnosis were significantly associated with an increased hazard of dementia development over the follow-up period. In a multivariate Cox model (n = 1047), age [HR: 1.07, 95% CI 1.03-1.11], baseline dementia [HR: 2.66, 95% CI 1.65-4.27] and baseline e-frailty index score [HR: 11.55, 95% CI 2.09-63.84] were significantly associated with a higher hazard of death among those with epilepsy. Female sex [HR: 0.77, 95% CI 0.59-0.99] was associated with a lower hazard of death. SIGNIFICANCE: The odds of having dementia were higher in older adults with incident epilepsy. A higher comorbidity burden acts as a risk factor for dementia, while prevalent dementia and increasing frailty were associated with mortality.