Yoshihiro Maegaki

Objective: The employment outcomes of childhood-onset drug-resistant epilepsy (DRE) has not been studied enough. The aim of this retrospective cohort study is to investigate the employment outcomes of childhood-onset DRE in June 2022 and identify the risk factors associated with non-employment.Materials and methods: The sample consisted of 65 participants ≥18 years of age with a history of childhood-onset DRE. Fifty participants (77%) were salaried employees and 15 participants (23%) were non-employed. Clinical and psychosocial information were evaluated for calculating the relative risk (RR) of non-employment.Results: Regarding medical factors, lower IQ [RR, 0.645; 95% confidence interval (CI), 0.443–0.938; p = 0.022] was positively associated with employment. In contrast, age at follow-up (RR, 1.046; 95% CI, 1.009–1.085; p = 0.014); number of ASMs at follow-up (RR, 1.517; 95% CI, 1.081–2.129; p = 0.016); use of medications such as phenobarbital (RR, 3.111; 95% CI, 1.383–6.997; p = 0.006), levetiracetam (RR, 2.471; 95% CI, 1.056–5.782; p = 0.037), and topiramate (RR, 3.576; 95% CI, 1.644–7.780; p = 0.001) were negatively associated with employment. Regarding psychosocial factor, initial workplace at employment support facilities (RR, 0.241; 95% CI, 0.113–0.513; p < 0.001) was positively associated with employment. In contrast, complication of psychiatric disorder symptoms (RR, 6.833; 95% CI, 2.141–21.810; p = 0.001) was negatively associated with employment. Regarding educational factor, graduating schools of special needs education (RR, 0.148; 95% CI, 0.061–0.360; p < 0.001) was positively associated with employment.Conclusions: Specific medical, psychosocial, and educational factors may influence the employment outcomes of childhood-onset DRE. Paying attention to ASMs’ side effects, adequately preventing the complications of psychiatric disorder symptoms, and providing an environment suitable for each patient condition would promote a fine working status for people with childhood-onset DRE.

Many algorithms to detect copy number variations (CNVs) using exome sequencing (ES) data have been reported and evaluated on their sensitivity and specificity, reproducibility, and precision. However, operational optimization of such algorithms for a better performance has not been fully addressed. ES of 1199 samples including 763 patients with different disease profiles was performed. ES data were analyzed to detect CNVs by both the eXome Hidden Markov Model (XHMM) and modified Nord's method. To efficiently detect rare CNVs, we aimed to decrease sequencing biases by analyzing, at the same time, the data of all unrelated samples sequenced in the same flow cell as a batch, and to eliminate sex effects of X‐linked CNVs by analyzing female and male sequences separately. We also applied several filtering steps for more efficient CNV selection. The average number of CNVs detected in one sample was <5. This optimization together with targeted CNV analysis by Nord's method identified pathogenic/likely pathogenic CNVs in 34 patients (4.5%, 34/763). In particular, among 142 patients with epilepsy, the current protocol detected clinically relevant CNVs in 19 (13.4%) patients, whereas the previous protocol identified them in only 14 (9.9%) patients. Thus, this batch‐based XHMM analysis efficiently selected rare pathogenic CNVs in genetic diseases.