[1] Rosenberg I H. Sarcopenia: origins and clinical relevance[J].J Nutr1997,127(5 Suppl): 990S-991S.
[2] Chen L K, Woo J, Assantachai P, et al. Asian Working Group for Sarcopenia: 2019 Consensus Update on Sarcopenia Diagnosis and Treatment[J]. J Am Med Dir Assoc,2020,21(3): 300-307.
[3] 刘娟,丁清清,周白瑜,等. 中国老年人肌少症诊疗专家共识(2021)[J]. 中华老年医学杂志, 2021(08): 943-952.
[4] Ren H,Gong D,Jia F,et al. Sarcopenia in patients undergoing maintenance hemodialysis: incidence rate, risk factors and its effect on survival risk[J]. Ren Fail,2016,38(3): 364-371.
[5] Lamarca F,Carrero J J,Rodrigues J C,et al. Prevalence of sarcopenia in elderly maintenance hemodialysis patients: the impact of different diagnostic criteria[J]. J Nutr Health Aging,2014,18(7): 710-717.
[6] 董志娟,张海林.维持性血液透析患者肌少症发生的危险因素分析[J]. 护理学杂志, 2018, 33(09): 20-24.
[7] Kim J K,Kim S G,Oh J E,et al. Impact of sarcopenia on long-term mortality and cardiovascular events in patients undergoing hemodialysis[J]. Korean J Intern Med,2019,34(3): 599-607.
[8] Cruz-Jentoft A J,Bahat G,Bauer J,et al. Sarcopenia: revised European consensus on definition and diagnosis[J]. Age Ageing,2019,48(1): 16-31.
[9] Wolff R F,Moons K,Riley R D,et al. PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies[J]. Ann Intern Med,2019,170(1): 51-58.
[10] 丁妍,常立阳,张红梅. 相角与维持性血液透析患者肌少症的关系及其预测价值分析[J]. 中国血液净化,2022,21(03): 172-176.
[11] Wang Y,Hu Y,Zhang M,et al. Bioelectrical impedance analysis-derived phase angle predicts sarcopenia in patients on maintenance hemodialysis[J]. Nutr Clin Pract, 2023,38(4): 881-888.
[12] Do J Y,Kim A Y,Kang S H. Association Between Phase Angle and Sarcopenia in Patients Undergoing Peritoneal Dialysis[J]. Front Nutr,2021,8: 742081.
[13] Chen R,Zhang L,Zhang M,et al. The triglyceride-glucose index as a novel marker associated with sarcopenia in non-diabetic patients on maintenance hemodialysis[J]. Ren Fail,2022,44(1): 1615-1621.
[14] Mae Y,Takata T,Yamada K,et al. Creatinine generation rate can detect sarcopenia in patients with hemodialysis[J]. Clin Exp Nephrol,2022,26(3): 272-277.
[15] Wang J,Xu M C,Huang L J,et al. Value of neutrophil-to-lymphocyte ratio for diagnosing sarcopenia in patients undergoing maintenance hemodialysis and efficacy of Baduanjin exercise combined with nutritional support[J]. Front Neurol,2023,14: 1072986.
[16] 秦红菊,倪燕丹,张小梅,等. 维持性血液透析患者肌少症发生风险预测模型的构建[J]. 现代临床护理,2023,22(06): 15-21.
[17] Lin T Y,Wu M Y,Chen H S,et al. Development and validation of a multifrequency bioimpedance spectroscopy equation to predict appendicular skeletal muscle mass in hemodialysis patients[J]. Clin Nutr,2021,40(5): 3288-3295.
[18] Senzaki D,Yoshioka N,Nagakawa O,et al. Modeling Low Muscle Mass Screening in Hemodialysis Patients[J]. Nephron,2023,147(5): 251-259.
[19] 丁妍,常立阳,张红梅. 维持性血液透析病人肌少症发生风险预测模型的构建与验证[J]. 护理研究,2022,36(20): 3586-3591.
[20] 施晴波,樊璠. 三酰甘油-葡萄糖指数、脑源性神经营养因子对非糖尿病维持性血液透析患者的肌肉减少症的诊断价值分析[J]. 中国血液净化,2024,23(01): 30-34.
[21] Chen Y,Wu J,Ran L,et al. The combination of phase angle and age has a good diagnostic value for sarcopenia in continuous ambulatory peritoneal dialysis patients[J]. Front Nutr,2022,9: 1036796.
[22] 宝群,闫燕,丁秀和. 维持性血液透析患者肌少症与同型半胱氨酸、鸢尾素及营养不良-炎症评分的关系[J]. 中国血液净化,2022,21(10): 744-748.
[23] Xie D,Zhu Q,Lu J,et al. Development and validation of a diagnostic nomogram for sarcopenia in Chinese hemodialysis patients[J]. Nephrol Dial Transplant,2023,38(4): 1017-1026.
[24] Du X,Chen G,Zhang H,et al. Development of a Practical Screening Tool to Predict Sarcopenia in Patients on Maintenance Hemodialysis[J]. Med Sci Monit,2022,28: e937504.
[25] Balachandran V P,Gonen M,Smith J J,et al. Nomograms in oncology: more than meets the eye[J]. Lancet Oncol,2015,16(4): e173-e180.
[26] Cai G,Ying J,Pan M,et al. Development of a risk prediction nomogram for sarcopenia in hemodialysis patients[J]. BMC Nephrol,2022,23(1): 319.
[27] Hong W,Earnest A,Sultana P,et al. How accurate are vital signs in predicting clinical outcomes in critically ill emergency department patients[J]. Eur J Emerg Med,2013,20(1): 27-32.
[28] 李彩福,赵伟,叶秀春,等. 基于机器学习算法的社区老年衰弱前期风险预测模型构建[J]. 护理学杂志,2022,37(15): 84-88.
[29] 曲超然,王青,韩琳,等. 机器学习算法在压力性损伤管理中的应用进展[J]. 中华护理杂志,2021,56(02): 212-217.
[30] 郑晓燕.基于机器学习的心血管疾病预测系统研究[D]. 北京交通大学,2018.
[31] Wu J,Lin S,Guan J,et al. Prediction of the sarcopenia in peritoneal dialysis using simple clinical information: A machine learning-based model[J]. Semin Dial,2023,36(5): 390-398.
[32] Liao H,Yang Y,Zeng Y,et al. Use machine learning to help identify possible sarcopenia cases in maintenance hemodialysis patients[J]. BMC Nephrol,2023,24(1): 34.
[33] Wong L,Duque G,Mcmahon L P. Sarcopenia and Frailty: Challenges in Mainstream Nephrology Practice[J]. Kidney Int Rep,2021,6(10): 2554-2564.
[34] Liguori I,Russo G,Aran L,et al. Sarcopenia: assessment of disease burden and strategies to improve outcomes[J]. Clin Interv Aging,2018,13: 913-927.
[35] 黄雨欣,许丽春,庄盼盼,等. 维持性血液透析患者肌少症危险因素的Meta分析[J]. 护理学杂志,2022,37(13): 17-21.
[36] Wolff R F,Moons K,Riley R D,et al. PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies[J]. Ann Intern Med,2019,170(1): 51-58.
[37] 李珂,杨振楠. PICC相关血流感染风险预测模型的研究进展[J]. 中华护理杂志,2022,57(05): 551-554.
[38] 宋思平,刘晓晴,蒋琪霞. 压力性损伤风险预测模型的研究进展[J]. 中华护理杂志,2020,55(04): 628-631.