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Objective: Health state utilities are required to obtain quality adjusted life years, a common metric that informs clinical decision-making at individual, group, and health policy levels. Health state utilities are different from health-related quality of life, and their distribution across patients with epilepsy, as well as the factors that impact them, have not been studied in depth. We aimed to describe the distribution of health state utilities in people with epilepsy and the impact of different combinations of clinical and demographic factors on health state evaluation., Methods: We performed a retrospective analysis of patients' data prospectively collected in the Calgary Comprehensive Epilepsy Program registry. Patient-reported health state utilities were measured using the 5-level EuroQol 5-Dimension scale (EQ-5D-5L) completed at their initial assessment. EQ-5D-5L index scores were derived via the time trade-off approach based on Canadian norms, and their distribution across different health states and patient characteristics was obtained. The Tobit regression model was used to evaluate the determinants of EQ-5D-5L index scores., Results: Of 1446 patients included in this analysis, 724 (50.5%) were female. The median (interquartile range) Canada-normed EQ-5D-5L index score was .87 (.71-.91). Patients with significantly lower health utilities were more likely to be female (p = .008), to be older (p = .034), to be unmarried (p = .013), to have failed to achieve 1-year seizure freedom (p < .001), to have no postsecondary education (p = .028), to be depressed (p < .001), to have antiseizure medication side effects (p = .001), to be unemployed (p < .001), and to be unable to drive (p < .001). A look-up table of health utilities based on combinations of clinical-demographic characteristics was produced., Significance: Health utility estimates for combinations of different health states in people with epilepsy attending specialty clinics are now available. These can help guide clinical decision-making in routine clinical practice, economic evaluations of treatment interventions, and health care policies., (© 2024 The Author(s). Epilepsia published by Wiley Periodicals LLC on behalf of International League Against 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., (© 2024 The Author(s). Epilepsia published by Wiley Periodicals LLC on behalf of International League Against Epilepsy.)

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 ( β  = -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., (© 2021 International League Against 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., (© 2020 International League Against Epilepsy.)

Objective: To apply unsupervised machine learning to patient-reported outcomes to identify clusters of epilepsy patients exhibiting unique psychosocial characteristics., Methods: Consecutive outpatients seen at the Calgary Comprehensive Epilepsy Program outpatient clinics with complete patient-reported outcome measures on quality of life, health state valuation, depression, and epilepsy severity and disability were studied. Data were acquired at each patient's first clinic visit. We used k-means++ to segregate the population into three unique clusters. We then used multinomial regression to determine factors that were statistically associated with patient assignment to each cluster., Results: We identified 462 consecutive patients with complete patient-reported outcome measure (PROM) data. Post hoc analysis of each cluster revealed one reporting elevated measures of psychosocial health on all five PROMs ("high psychosocial health" cluster), one with intermediate measures ("intermediate" cluster), and one with poor overall measures of psychosocial health ("poor psychosocial health" cluster). Failing to achieve at least 1 year of seizure freedom (relative risk [RR] = 4.34, 95% confidence interval [CI] = 2.13-9.09) predicted placement in the "intermediate" cluster relative to the "high" cluster. In addition, failing to achieve seizure freedom, social determinants of health, including the need for partially or completely subsidized income support (RR = 6.10, 95% CI = 2.79-13.31, P < .001) and inability to drive (RR = 4.03, 95% CI = 1.6-10.00, P = .003), and a history of a psychiatric disorder (RR = 3.16, 95% CI = 1.46-6.85, P = .003) were associated with the "poor" cluster relative to the "high" cluster., Significance: Seizure-related factors appear to drive placement in the "intermediate" cluster, with social determinants driving placement in the "poor" cluster, suggesting a threshold effect. Precision intervention based on cluster assignment, with an initial emphasis on improving social support and careful titration of medications for those reporting the worst psychosocial health, could help optimize health for patients with epilepsy., (© 2020 International League Against Epilepsy.)