Abstract (Educational surveys （II）)

継続後誌：統計数理研究所彙

Abstract (Educational surveys （I）)

継続後誌：統計数理研究所彙

社會科テストのサムプリング計画 (教育調査の企畫から分析まで（I）)

継続後誌：統計数理研究所彙

全國小中学校教育課程調査について(其の二) (教育調査の企畫から分析まで（II）)

継続後誌：統計数理研究所彙

Modeling Low Muscle Mass Screening in Hemodialysis Patients

Introduction: Computed tomography (CT) can accurately measure muscle mass, which is necessary for diagnosing sarcopenia, even in dialysis patients. However, CT-based screening for such patients is challenging, especially considering the availability of equipment within dialysis facilities. We therefore aimed to develop a bedside prediction model for low muscle mass, defined by the psoas muscle mass index (PMI) from CT measurement. Methods: Hemodialysis patients (n = 619) who had undergone abdominal CT screening were divided into the development (n = 441) and validation (n = 178) groups. PMI was manually measured using abdominal CT images to diagnose low muscle mass by two independent investigators. The development group’s data were used to create a logistic regression model using 42 items extracted from clinical information as predictive variables; variables were selected using the stepwise method. External validity was examined using the validation group’s data, and the area under the curve (AUC), sensitivity, and specificity were calculated. Results: Of all subjects, 226 (37%) were diagnosed with low muscle mass using PMI. A predictive model for low muscle mass was calculated using ten variables: each grip strength, sex, height, dry weight, primary cause of end-stage renal disease, diastolic blood pressure at start of session, pre-dialysis potassium and albumin level, and dialysis water removal in a session. The development group’s adjusted AUC, sensitivity, and specificity were 0.81, 60%, and 87%, respectively. The validation group’s adjusted AUC, sensitivity, and specificity were 0.73, 64%, and 82%, respectively. Discussion/Conclusion: Our results facilitate skeletal muscle screening in hemodialysis patients, assisting in sarcopenia prophylaxis and intervention decisions

Construction of an explanatory model for quality of life in outpatients with ulcerative colitis

Abstract Introduction: To date, no studies have reported explanatory models of health-related quality of life (HRQoL) in patients with ulcerative colitis. Therefore, this study aimed to examine HRQoL and its related factors in outpatients with ulcerative colitis to construct an explanatory model. Methods: We conducted a cross-sectional survey at a clinic in Japan. The HRQoL was evaluated using the 32-item Inflammatory Bowel Disease Questionnaire. We extracted explanatory variables of HRQoL from demographic, physical, psychological, and social factors reported in previous studies and created a predictive explanatory model. The relationship between explanatory variables and the questionnaire total score was examined using Spearman’s rank correlation coefficient, the Mann–Whitney test, or the Kruskal–Wallis test. We conducted multiple regression and path analyses to examine the effect of explanatory variables on the total score. Results: We included 203 patients. Variables that were associated with the total score were the partial Mayo Score (r = −0.451), treatment side effects (p = 0.004), the Hospital Anxiety Scale-Anxiety score (r = −0.678), the Hospital Anxiety Scale-Depression score (r = −0.528), and the availability of an advisor during difficult times (p = 0.001). The model included the partial Mayo Score, treatment side effects, the Hospital Anxiety Scale-Anxiety score, and the availability of an advisor during difficult times as explanatory variables of the total score that showed the best goodness-of-fit (adjusted R2 = 0.597). The anxiety score exerted the greatest negative effect on the questionnaire total score (β = −0.586), followed by the partial Mayo Score (β = –0.373), treatment side effects (β = 0.121), and availability of an advisor during difficult times (β = −0.101). Conclusion: Psychological symptoms exerted the strongest direct effect on HRQoL in outpatients with ulcerative colitis and mediated the relationship between social support and HRQoL. Nurses should listen carefully to the concerns and anxieties of patients to ensure that a social support system is provided by leveraging multidisciplinary collaborations