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Objectives To investigate the association between decreased serum IgG levels caused by remission-induction immunosuppressive therapy of antineutrophil cytoplasmic antibody-associated vasculitis (AAV) and the development of severe infections. Methods We conducted a retrospective cohort study of patients with new-onset or severe relapsing AAV enrolled in the J-CANVAS registry, which was established at 24 referral sites in Japan. The minimum serum IgG levels up to 24 weeks and the incidence of severe infection up to 48 weeks after treatment initiation were evaluated. After multiple imputations for all explanatory variables, we performed the multivariate analysis using a Fine-Gray model to assess the association between low IgG (the minimum IgG levels < 500 mg/dl) and severe infections. In addition, the association was expressed as a restricted cubic spline (RCS) and analysed by treatment subgroups. Results Of 657 included patients (microscopic polyangiitis, 392; granulomatosis with polyangiitis, 139; eosinophilic granulomatosis with polyangiitis, 126), 111 (16.9%) developed severe infections. The minimum serum IgG levels were measured in 510 patients, of whom 77 (15.1%) had low IgG. After multiple imputations, the confounder-adjusted hazard ratio of low IgG for the incidence of severe infections was 1.75 (95% confidence interval: 1.03–3.00). The RCS revealed a U-shaped association between serum IgG levels and the incidence of severe infection with serum IgG 946 mg/dl as the lowest point. Subgroup analysis showed no obvious heterogeneity between treatment regimens. Conclusion Regardless of treatment regimens, low IgG after remission-induction treatment was associated with the development of severe infections up to 48 weeks after treatment initiation.

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ObjectiveThe 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 methodsThe 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.ResultsRegarding 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.ConclusionsSpecific 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.

Background:Cancer patients experience various forms of psychological distress. Their distress, mainly in the form of depression and anxiety, leads to poor quality of life, increased medical spending due to frequent visits, and decrease in treatment adherence. It is estimated that 30-50% among them would require support from mental health professionals: in reality, much less actually receive such support partly due to shortage of qualified professionals and also due to psychological barriers in seeking such help. The purpose of the present study is to develop the easily accessible and the most efficient and effective smartphone psychotherapy package to alleviate depression and anxiety in cancer patients.Methods:Based on the multiphase optimization strategy (MOST) framework, the SMartphone Intervention to LEssen depression/Anxiety and GAIN resilience project (SMILE-AGAIN project) is a parallel-group, multicenter, open, stratified block randomized, fully factorial trial with four experimental components: psychosocial education (PE), behavioral activation (BA), assertion training (AT) and problem-solving therapy (PS). The allocation sequences are maintained centrally. All participants receive PE and then are randomized to the presence/absence of the remaining three components. The primary outcome of this study is the Patient Health Questionnaire-9 (PHQ-9) total score, which will be administered as an electronic patient-reported outcome on the patients’ smartphones after 8 weeks. The protocol was approved by the Institutional Review Board of Nagoya City University on July 15, 2020 (ID: 46-20-0005). The randomized trial, which commenced in March 2021, is currently enrolling participants. The estimated end date for this study is March 2023.Discussion: The highly efficient experimental design will allow for the identification of the most effective components and the most efficient combinations among the four components of the smartphone psychotherapy package for cancer patients. Given that many cancer patients face significant psychological hurdles in seeing mental health professionals, easily accessible therapeutic interventions without hospital visits may offer benefits. If an effective combination of psychotherapy is determined in this study, it can be provided using smartphones to patients who cannot easily access hospitals or clinics.Trial registration: UMIN000041536, CTR- Registered on November 1, 2020https://center6.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000047301

Backgrounds Severely obese patients must follow strict regimens of diet, exercise, and medical therapy. However, such comprehensive weight-loss programs have high dropout rates. In this study, we developed a machine learning prediction model to aid in the early detection of high-risk-dropout patients.Methods 102 severely obese patients were monitored for 3 years to assess their risk of dropout from a comprehensive weight-loss program. The program targeted a 5% weight loss. It consisted of three main components, which include behavioral modification (goal setting and charting weight four times daily), diet, and exercise. A machine learning model was developed to predict dropout risk based on a 1-year dropout event. To extend the prediction ability past 1 year, we plotted a 3-year Kaplan-Meier survival curve using a deep learning (DL) algorithm and logistic regression (LR) classifications.Results Their mean age was 49±14 years, 43% were male, BMI was 42 kg/m2, and hemoglobin A1c was 7.6%. Additionally, 76% had diabetes, 21% had impaired glucose tolerance. After 1 year, the dropout rate was 19%. Using oral hypoglycemic agents had a lower risk of 3-year dropout [Odds ratio 0.26 (95% CI: 0.08 to 0.83, p = 0.023)]. The area under the curve (AUC) was better with DL than LR methods for predicting dropout at 3 years (0.97 vs. 0.77, p<0.001). The AUC for DL was also better than LR using binary classifications (0.86 vs. 0.68, p=0.001).Conclusions We demonstrated a higher precision with machine learning than with the standard logistic regression, based on limited sample size and information available during hospital admission. It is vital to note that machine learning was more accurate than standard analysis. This may have clinical significance because machine learning could be used to identify high-risk groups and allow for early intervention.