Background Maintenance hemodialysis (MHD) patients often suffer from sarcopenia, which is strongly associated with their long-term mortality. The diagnosis and treatment of sarcopenia, especially possible sarcopenia for MHD patients are of great importance. This study aims to use machine learning and medical data to develop two simple sarcopenia identification assistant tools for MHD patients and focuses on sex specificity. Methods Data were retrospectively collected from patients undergoing MHD and included patients’ basic information, body measurement results and laboratory findings. The 2019 consensus update by Asian working group for sarcopenia was used to assess whether a MHD patient had sarcopenia. Finally, 140 male (58 with possible sarcopenia or sarcopenia) and 102 female (65 with possible sarcopenia or sarcopenia) patients’ data were collected. Participants were divided into sarcopenia and control groups for each sex to develop binary classifiers. After statistical analysis and feature selection, stratified shuffle split and Synthetic Minority Oversampling Technique were conducted and voting classifiers were developed. Results After eliminating handgrip strength, 6-m walk, and skeletal muscle index, the best three features for sarcopenia identification of male patients are age, fasting blood glucose, and parathyroid hormone. Meanwhile, age, arm without vascular access, total bilirubin, and post-dialysis creatinine are the best four features for females. After abandoning models with overfitting or bad performance, voting classifiers achieved good sarcopenia classification performance for both sexes (For males: sensitivity: 77.50% ± 11.21%, specificity: 83.13% ± 9.70%, F1 score: 77.32% ± 5.36%, the area under the receiver operating characteristic curves (AUC): 87.40% ± 4.41%. For females: sensitivity: 76.15% ± 13.95%, specificity: 71.25% ± 15.86%, F1 score: 78.04% ± 8.85%, AUC: 77.69% ± 7.92%). Conclusions Two simple sex-specific sarcopenia identification tools for MHD patients were developed. They performed well on the case finding of sarcopenia, especially possible sarcopenia.

Background Maintenance hemodialysis (MHD) patients have complex medication regimens that require a high level of skill to interpret medication information. However, there is currently a lack of research evaluating the ability to read and understand medication labels in Chinese MHD patients. In addition, the relationship between frailty and medication literacy among MHD patients remains unclear. Therefore, this study aims to assess the potential factors affecting medication literacy in MHD patients and to explore the relationship between frailty and medication literacy. Methods This cross-sectional study was conducted using convenience sampling in West China Hospital of Sichuan University, China. Using a general questionnaire, we collected demographic, clinical and laboratory data. Medication literacy was assessed by the Chinese Medication Literacy Scale, and frailty was assessed by the FRAIL Scale. Univariate analyses examined potential factors impacting medication literacy. An ordered logistic regression was used to analyze the relationships between medication literacy and these factors. Spearman's correlation was used to assess the association between medication literacy and frailty. Results A total of 290 MHD patients were included in the analysis. Inadequate, marginal, and adequate medication literacy was found in 56 (19.3%), 153 (52.8%), and 81 (27.9%) patients, respectively. Ordered logistic regression revealed factors impacting medication literacy: age (OR = 3.561, 95% CI = 1.769–7.171, p < 0.001 for < 65 years); education (OR = 0.116, 95% CI = 0.048–0.284, p < 0.001 for ≤ primary school education; OR = 0.294, 95% CI = 0.145–0.594, p = 0.001 for junior high school education); caregiver medication assistance (OR = 0.434, 95% CI = 0.221–0.852, p = 0.015); frailty (OR = 2.275, 95% CI = 1.120–4.621, p = 0.023 for prefrail patients); and β2-microglobulin (β2-MG) (OR = 0.990, 95% CI = 0.982–0.998, p = 0.012). Spearman's analysis showed that medication literacy was associated with frailty in MHD patients (R=-0.189, p = 0.001). Conclusions The Chinese version of the MedLitRxSE tool can help evaluate medication literacy in MHD patients. Medication literacy levels in MHD patients need improvement and differ among patient groups, such as by age, education, caregiver support, β2-microglobulin levels, and risk of frailty.

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