Obesity is a common disorder with many complications. Although chronodisruption plays a role in obesity, few epidemiological studies have investigated the association between artificial light at night (ALAN) and obesity. Since sleep health is related to both obesity and ALAN, we investigated the association between outdoor ALAN and obesity after adjusting for sleep health. We also investigated the association between outdoor ALAN and sleep health. This cross-sectional survey included 8526 adults, 39–70 years of age, who participated in the Korean Genome and Epidemiology Study. Outdoor ALAN data were obtained from satellite images provided by the US Defense Meteorological Satellite Program. We obtained individual data regarding outdoor ALAN; body mass index; depression; and sleep health including sleep duration, mid-sleep time, and insomnia; and other demographic data including age, sex, educational level, type of residential building, monthly household income, alcohol consumption, smoking status and consumption of caffeine or alcohol before sleep. A logistic regression model was used to investigate the association between outdoor ALAN and obesity. The prevalence of obesity differed significantly according to sex (women 47% versus men 39%, p < 0.001) and outdoor ALAN (high 55% versus low 40%, p < 0.001). Univariate logistic regression analysis revealed a significant association between high outdoor ALAN and obesity (odds ratio [OR] 1.24, 95% confidence interval [CI] 1.14–1.35, p < 0.001). Furthermore, multivariate logistic regression analyses showed that high outdoor ALAN was significantly associated with obesity after adjusting for age and sex (OR 1.25, 95% CI 1.14–1.37, p < 0.001) and even after controlling for various other confounding factors including age, sex, educational level, type of residential building, monthly household income, alcohol consumption, smoking, consumption of caffeine or alcohol before sleep, delayed sleep pattern, short sleep duration and habitual snoring (OR 1.20, 95% CI 1.06–1.36, p = 0.003). The findings of our study provide epidemiological evidence that outdoor ALAN is significantly related to obesity.

Background and Objectives: The outcome of status epilepticus (SE) largely varies depending on clinical characteristics. Risk stratification is necessary for tailoring the aggressiveness of treatment and predicting outcomes of individual patients with SE. In this study, we assessed differences in mortality, neurologic disability, and prognostic factors associated with SE across sociodemographic and clinical characteristics. Methods: We conducted a nationwide population-based retrospective cohort study using the National Health Insurance Service (NHIS) database linked with the national death and disability registries. SE was identified from admission or emergency room visits using a diagnostic code of G41 from the International Classification of Disease, 10^th Revision. Individuals with new-onset SE that occurred from January 1, 2010, to December 31, 2018, were included. Active epilepsy, refractoriness of SE, potential etiology, and comorbidities were ascertained by diagnostic codes and/or prescription records from the NHIS database as potential prognostic factors. Outcomes included 30-day and 1-year mortality and neurologic disabilities following SE. Prognostic factors for mortality were assessed by the Cox regression hazard model. We performed a subgroup analysis according to age: pediatric SE (age < 20 years old) and adult SE (age ≥ 20 years old). Results: A total of 33,814 new-onset SE patients were included (6,818 children/adolescents and 26,996 adults). The 30-day mortality was 8.5% (1.8% in pediatric SE and 10.2% in adult SE), and the 1-year mortality was 25.1% (4.6% in pediatric SE and 30.3% in adult SE). Overall, 10.7% of patients newly acquired neurologic disabilities following SE, with the highest incidence in children aged 5 to 9 years (21.3%). Intractable epilepsy developed in 0.8% of entire SE. Old age, presence of acute etiology, and refractoriness were poor prognostic factors for mortality in both pediatric and adult SE. Male sex, low economic status, no active epilepsy, and comorbidities were additional factors for a poor prognosis in adults. Conclusions: New-onset SE was associated with substantial mortality and disability. While SE-related mortality was higher in adults, disabilities developed more commonly in children and adolescents. The major determinants of mortality differed between pediatric and adult SE.

Background: Two clinical scoring systems, the status epilepticus severity score (STESS) and the epidemiology-based mortality score in status epilepticus (EMSE), are used to predict mortality in patients with status epilepticus (SE). The aim of this study was to compare the outcome-prediction function of the two scoring systems regarding in-hospital mortality using a multicenter large cohort of adult patients with SE. Moreover, we studied the potential role of these two scoring systems in predicting the functional outcome in patients with SE. Methods: The SE cohort consisted of patients from the epilepsy centers of eight academic tertiary medical centers in South Korea. The clinical and electroencephalography data for all adult patients with SE from January 2013 to December 2014 were derived from a prospective SE database. The primary outcome variable was defined as in-hospital death. The secondary outcome variable was defined as a poor functional outcome, i.e., a score of 1–3 on the Glasgow Outcome Scale, at discharge. Results: Among the 120 non-hypoxic patients with SE recruited into the study, 16 (13.3 %) died in the hospital and 64 (53.3 %) were discharged with a poor functional outcome. The receiver-operating characteristic (ROC) curve for prediction of in-hospital death based on the STESS had an area under the curve of 0.673 with an optimal cutoff value for discrimination (best match for both sensitivity (0.56) and specificity (0.70)) that was ≥4 points. The two combinations of elements of the EMSE system (EMSE-ALDEg and EMSE-ECLEg) predicted not only in-hospital mortality with the best match for sensitivity (more than 0.6) and specificity (more than 0.6), but also a poor functional outcome with the best match for both sensitivity (>0.7) and specificity (>0.6). STESS did not predict a poor functional outcome (area under the ROC, 0.581; P = 0.23). Conclusion: Although the EMSE is a clinical scoring system that focuses on individual mortality, we did not find differences between the EMSE and STESS in the prediction of in-hospital death. The EMSE was useful in predicting poor functional outcome, as it was significantly better than STESS.

Sleep scoring is the first step for diagnosing sleep disorders. A variety of chronic diseases related to sleep disorders could be identified using sleep-state estimation. This paper presents an end-to-end deep learning architecture using wrist actigraphy, called Deep-ACTINet, for automatic sleep-wake detection using only noise canceled raw activity signals recorded during sleep and without a feature engineering method. As a benchmark test, the proposed Deep-ACTINet is compared with two conventional fixed model based sleep-wake scoring algorithms and four feature engineering based machine learning algorithms. The datasets were recorded from 10 subjects using three-axis accelerometer wristband sensors for eight hours in bed. The sleep recordings were analyzed using Deep-ACTINet and conventional approaches, and the suggested end-to-end deep learning model gained the highest accuracy of 89.65%, recall of 92.99%, and precision of 92.09% on average. These values were approximately 4.74% and 4.05% higher than those for the traditional model based and feature based machine learning algorithms, respectively. In addition, the neuron outputs of Deep-ACTINet contained the most significant information for separating the asleep and awake states, which was demonstrated by their high correlations with conventional significant features. Deep-ACTINet was designed to be a general model and thus has the potential to replace current actigraphy algorithms equipped in wristband wearable devices.

Background and Objectives: People with epilepsy (PWE) are at risk of premature death with considerable variability according to the study population. We aimed to estimate the risk and causes of death in PWE according to age, disease severity, disease course, comorbidities and socioeconomic status in Korea. Methods: We conducted a nationwide population-based retrospective cohort study using the National Health Insurance database linked with the national death register. Newly treated PWE from 2008 to 2016 who were identified by antiseizure medication (ASM) prescriptions and diagnostic codes for epilepsy/seizure were included and observed until 2017. We assessed all-cause and cause-specific crude mortality rates and standardized mortality ratios (SMRs). Results: Among 138,998 PWE, 20,095 deaths were identified, and the mean follow-up period was 4.79 years. The SMR was 2.25 in the overall group of PWE, with a higher value in the younger age group at diagnosis and a shorter time interval after diagnosis. The SMR in the monotherapy group was 1.56, while that in the group with 4 or more ASMs was 4.93. PWE without any comorbidities had an SMR of 1.61. PWE who were rural residents had a higher SMR than those who were urban residents (2.47 versus 2.03, respectively). The causes of death among PWE were cerebrovascular disease (18.9%, SMR 4.50), malignant neoplasms outside the central nervous system (CNS) (15.7%, SMR 1.37), malignant neoplasms of the CNS (6.7%, SMR 46.95), pneumonia (6.0%, SMR 2.08), and external causes (7.2%, SMR 2.17), including suicide (2.6%, SMR 2.07). Epilepsy itself and status epilepticus accounted for 1.9% of the overall death. The excess mortality associated with pneumonia and external causes was persistently high, whereas the excess mortality associated with malignancy and cerebrovascular diseases tended to decrease with increasing time since diagnosis. Discussion: This study showed excess mortality in PWE, even in those without comorbidities and those receiving monotherapy. Regional disparities and sustained risks of deaths from external causes over ten years imply potential points of intervention. In addition to active control of seizures, education about injury prevention, monitoring for suicidal ideation and efforts to improve accessibility to epilepsy care are all required to reduce mortality.