Department of Applied Mechanics, College of Architecture and Environment, Sichuan University, Chengdu, 610065, Sichuan, China.
Department of Nephrology, West China Hospital, Sichuan University/ West China School of Nursing, Sichuan University, Chengdu, 610041, Sichuan, China.
BMC Nephrol. 2023 Feb 14;24(1):34. doi: 10.1186/s12882-023-03084-7.
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.
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.
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%).
Two simple sex-specific sarcopenia identification tools for MHD patients were developed. They performed well on the case finding of sarcopenia, especially possible sarcopenia.
维持性血液透析（MHD）患者常患有肌少症，这与他们的长期死亡率密切相关。肌少症的诊断和治疗，尤其是 MHD 患者的可能肌少症的诊断和治疗非常重要。本研究旨在使用机器学习和医疗数据为 MHD 患者开发两种简单的肌少症识别辅助工具，并重点关注性别特异性。
回顾性收集接受 MHD 治疗的患者数据，包括患者的基本信息、身体测量结果和实验室检查结果。使用 2019 年亚洲肌少症工作组更新的共识来评估 MHD 患者是否患有肌少症。最终，共收集了 140 名男性（58 名可能患有肌少症或肌少症）和 102 名女性（65 名可能患有肌少症或肌少症）患者的数据。将参与者按性别分为肌少症组和对照组，以开发二分类器。经过统计分析和特征选择，进行分层洗牌分割和合成少数过采样技术，并开发投票分类器。
在排除握力、6 米步行和骨骼肌指数后，男性患者肌少症识别的最佳三个特征是年龄、空腹血糖和甲状旁腺激素。同时，女性患者肌少症识别的最佳四个特征是年龄、无血管通路的手臂、总胆红素和透析后肌酐。在放弃具有过拟合或性能不佳的模型后，投票分类器对两性的肌少症分类性能均表现良好（对于男性：敏感性：77.50%±11.21%，特异性：83.13%±9.70%，F1 评分：77.32%±5.36%，受试者工作特征曲线下面积（AUC）：87.40%±4.41%。对于女性：敏感性：76.15%±13.95%，特异性：71.25%±15.86%，F1 评分：78.04%±8.85%，AUC：77.69%±7.92%）。
为 MHD 患者开发了两种简单的性别特异性肌少症识别工具。它们在肌少症，尤其是可能肌少症的病例发现方面表现良好。
