Scoping review of sarcopenia risk prediction models in China_West China Medical Journal

Authors：

TANG Lulu ^1,2,3 , YUAN Shuyi ^2,3 , CAO Yuanlin ^2,3 , HE Lanfang ^2,3 , SONG Lingxia ² ,  XIA Tongxia ^2,3

1. Department of Hepatology, Guiyang Public Health Treatment Center, Guiyang, Guizhou 550004, P. R. China;
2. Nursing Department, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou 56300, P. R. China;
3. Nursing School of Zunyi Medical University, Zunyi, Guizhou 56300, P. R. China;

Corresponding author：

XIA Tongxia, Email: Xiatongxia@zmu.edu.cn

Keywords：

Sarcopenia; risk prediction; prediction model; scoping review

DOI：

10.7507/1002-0179.202411359

Video：

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Objective To scoping review the risk prediction models for sarcopenia in China was conducted, and provide reference for scientific prevention and treatment of the disease and related research. Methods We systematically searched PubMed, Web of Science, Cochrane Library, Embase, China Knowledge Network, China Biomedical Literature Database, Wanfang Database, and Weipu Database for literature related to myasthenia gravis prediction models in China, with a time frame from the construction of the database to April 30, 2024 for the search. The risk of bias and applicability of the included literature were assessed, and information on the construction of myasthenia gravis risk prediction models, model predictors, model presentation form and performance were extracted. Results A total of 25 literatures were included, the prevalence of sarcopenia ranged from 12.16% to 54.17%, and the study population mainly included the elderly, the model construction methods were categorized into two types: logistic regression model and machine learning, and age, body mass index, and nutritional status were the three predictors that appeared most frequently. Conclusion Clinical caregivers should pay attention to the high-risk factors for the occurrence of sarcopenia, construct models with accurate predictive performance and high clinical utility with the help of visual model presentation, and design prospective, multicenter internal and external validation methods to continuously improve and optimize the models to achieve the best predictive effect.

Citation： TANG Lulu, YUAN Shuyi, CAO Yuanlin, HE Lanfang, SONG Lingxia, XIA Tongxia. Scoping review of sarcopenia risk prediction models in China. West China Medical Journal, 2025, 40(8): 1288-1293. doi: 10.7507/1002-0179.202411359 Copy

1.	Cruz-Jentoft AJ, Sayer AA. Sarcopenia. Lancet, 2019, 393(10191): 2636-2646.
2.	Ye C, Zheng X, Aihemaitijiang S, et al. Sarcopenia and catastrophic health expenditure by socio-economic groups in China: an analysis of household-based panel data. J Cachexia Sarcopenia Muscle, 2022, 13(3): 1938-1947.
3.	Yuan S, Larsson SC. Epidemiology of sarcopenia: prevalence, risk factors, and consequences. Metabolism, 2023, 144: 155533.
4.	邓菲菲, 赵智芳, 李建琼, 等. 社区老年肌少症风险筛查工具与方法的研究进展. 军事护理, 2023, 40(5): 82-85.
5.	Nascimento CM, Ingles M, Salvador-Pascual A, et al. Sarcopenia, frailty and their prevention by exercise. Free Radic Biol Med, 2019, 132: 42-49.
6.	Elemento O. The future of precision medicine: towards a more predictive personalized medicine. Emerg Top Life Sci, 2020, 4(2): 175-177.
7.	陈香萍, 张奕, 庄一渝, 等. PROBAST: 诊断或预后多因素预测模型研究偏倚风险的评估工具. 中国循证医学杂志, 2020, 20(6): 737-744.
8.	孔令慧, 于杰, 张会君, 等. 基于 Logistic 回归和决策树的老年脑卒中病人肌少症风险预测模型的构建. 护理研究, 2024, 38(10): 1703-1710.
9.	马娟, 侯彦杰, 黎妲, 等. 肿瘤患者肌少症的危险因素分析及模型建立. 现代肿瘤医学, 2024, 32(10): 1877-1881.
10.	李香香, 王梅芳, 冯秀娟, 等. 中老年 2 型糖尿病患者肌少症风险预测模型的构建与验证. 预防医学情报杂志, 2024, 40(12): 1538-1545.
11.	Li Q, Cheng H, Cen W, et al. Development and validation of a predictive model for the risk of sarcopenia in the older adults in China. Eur J Med Res, 2024, 29(1): 278.
12.	陈琳琳, 达雪萍, 马松华. 老年脑卒中患者继发肌少症危险因素及预测模型构建. 中国老年学杂志, 2023, 43(20): 4981-4983.
13.	周银, 庄彩丽, 倪好, 等. 肺癌患者并发肌肉减少症的危险因素分析及其列线图预测模型的应用价值. 肿瘤代谢与营养电子杂志, 2023, 10(5): 652-657.
14.	张媛, 马艳, 史凌云, 等. 基于 Logistic 回归、决策树、神经网络构建住院老年患者肌少症相对风险预测模型. 现代医学, 2023, 51(8): 1134-1143.
15.	陈禧, 肖江琴, 黄莉. 老年慢性阻塞性肺疾病并发肌少症风险预警模型构建与验证. 中华保健医学杂志, 2023, 25(6): 646-649.
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. 现代临床护理, 2023, 22(6): 15-21.
17.	陈佳惟, 李泽云, 彭坤, 等. 湘潭市社区老年人肌少症患病率调查及预测模型构建. 中华老年多器官疾病杂志, 2023, 22(9): 663-668.
18.	Yu M, Pan M, Liang Y, et al. A nomogram for screening sarcopenia in Chinese type 2 diabetes mellitus patients. Exp Gerontol, 2023, 172: 112069.
19.	Zhang Y, Zhu Y. Development and validation of risk prediction model for sarcopenia in patients with colorectal cancer. Front Oncol, 2023, 13: 1172096.
20.	Zhang H, Yin M, Liu Q, et al. Machine and deep learning-based clinical characteristics and laboratory markers for the prediction of sarcopenia. Chin Med J (Engl), 2023, 136(8): 967-973.
21.	Wu J, Lin S, Guan J, et al. Prediction of the sarcopenia in peritoneal dialysis using simple clinical information: a machine learning-based model. Semin Dial, 2023, 36(5): 390-398.
22.	刘艳平, 谭明杨, 徐超强, 等. 社区老年慢性病患者肌少症风险预测模型的构建. 中国护理管理, 2022, 22(12): 1814-1819.
23.	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. 护理研究, 2022, 36(20): 3586-3591.
24.	韩婷, 钱绪芬, 王庆芳, 等. 基于 Logistic 回归和决策树模型的老年住院患者肌少症风险的影响因素分析. 护理学报, 2022, 29(12): 56-62.
25.	周起帆, 尹丽霞, 张海林, 等. 中青年维持性血液透析患者肌少症预测模型的构建与验证. 实用临床医药杂志, 2022, 26(5): 44-47, 53.
26.	Yu S, Chen L, Zhang Y, et al. A combined diagnostic approach based on serum biomarkers for sarcopenia in older patients with hip fracture. Australas J Ageing, 2022, 41(4): e339-e347.
27.	Mo YH, Su YD, Dong X, et al. Development and validation of a nomogram for predicting sarcopenia in community-dwelling older adults. J Am Med Dir Assoc, 2022, 23(5): 715-721.
28.	Du X, Chen G, Zhang H, et al. Development of a practical screening tool to predict sarcopenia in patients on maintenance hemodialysis. Med Sci Monit, 2022, 28: e937504.
29.	Cai G, Ying J, Pan M, et al. Development of a risk prediction nomogram for sarcopenia in hemodialysis patients. BMC Nephrol, 2022, 23(1): 319.
30.	Chen YS, Cai YX, Kang XR, et al. Predicting the risk of sarcopenia in elderly patients with patellar fracture: development and assessment of a new predictive nomogram. PeerJ, 2020, 8: e8793.
31.	Cui M, Gang X, Gao F, et al. Risk assessment of sarcopenia in patients with type 2 diabetes mellitus using data mining methods. Front Endocrinol (Lausanne), 2020, 11: 123.
32.	张颖, 许晓磊, 汪元浚, 等. 老年住院患者肌肉减少症发生的危险因素分析及预测模型建立. 中国实用护理杂志, 2020, 36(30): 2337-2342.
33.	王晶, 李玲利, 赵春林, 等. 机器学习在构建护理风险预测模型中的研究进展. 护士进修杂志, 2022, 37(23): 2167-2171.
34.	Handelman GS, Kok HK, Chandra RV, et al. eDoctor: machine learning and the future of medicine. J Intern Med, 2018, 284(6): 603-619.
35.	徐园, 朱丽筠, 王钰, 等. 我国护理学者开展预测模型研究的现状和启示: 一项范围综述. 中国护理管理, 2022, 22(5): 744-749.

1. Cruz-Jentoft AJ, Sayer AA. Sarcopenia. Lancet, 2019, 393(10191): 2636-2646.
2. Ye C, Zheng X, Aihemaitijiang S, et al. Sarcopenia and catastrophic health expenditure by socio-economic groups in China: an analysis of household-based panel data. J Cachexia Sarcopenia Muscle, 2022, 13(3): 1938-1947.
3. Yuan S, Larsson SC. Epidemiology of sarcopenia: prevalence, risk factors, and consequences. Metabolism, 2023, 144: 155533.
4. 邓菲菲, 赵智芳, 李建琼, 等. 社区老年肌少症风险筛查工具与方法的研究进展. 军事护理, 2023, 40(5): 82-85.
5. Nascimento CM, Ingles M, Salvador-Pascual A, et al. Sarcopenia, frailty and their prevention by exercise. Free Radic Biol Med, 2019, 132: 42-49.
6. Elemento O. The future of precision medicine: towards a more predictive personalized medicine. Emerg Top Life Sci, 2020, 4(2): 175-177.
7. 陈香萍, 张奕, 庄一渝, 等. PROBAST: 诊断或预后多因素预测模型研究偏倚风险的评估工具. 中国循证医学杂志, 2020, 20(6): 737-744.
8. 孔令慧, 于杰, 张会君, 等. 基于 Logistic 回归和决策树的老年脑卒中病人肌少症风险预测模型的构建. 护理研究, 2024, 38(10): 1703-1710.
9. 马娟, 侯彦杰, 黎妲, 等. 肿瘤患者肌少症的危险因素分析及模型建立. 现代肿瘤医学, 2024, 32(10): 1877-1881.
10. 李香香, 王梅芳, 冯秀娟, 等. 中老年 2 型糖尿病患者肌少症风险预测模型的构建与验证. 预防医学情报杂志, 2024, 40(12): 1538-1545.
11. Li Q, Cheng H, Cen W, et al. Development and validation of a predictive model for the risk of sarcopenia in the older adults in China. Eur J Med Res, 2024, 29(1): 278.
12. 陈琳琳, 达雪萍, 马松华. 老年脑卒中患者继发肌少症危险因素及预测模型构建. 中国老年学杂志, 2023, 43(20): 4981-4983.
13. 周银, 庄彩丽, 倪好, 等. 肺癌患者并发肌肉减少症的危险因素分析及其列线图预测模型的应用价值. 肿瘤代谢与营养电子杂志, 2023, 10(5): 652-657.
14. 张媛, 马艳, 史凌云, 等. 基于 Logistic 回归、决策树、神经网络构建住院老年患者肌少症相对风险预测模型. 现代医学, 2023, 51(8): 1134-1143.
15. 陈禧, 肖江琴, 黄莉. 老年慢性阻塞性肺疾病并发肌少症风险预警模型构建与验证. 中华保健医学杂志, 2023, 25(6): 646-649.
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. 现代临床护理, 2023, 22(6): 15-21.
17. 陈佳惟, 李泽云, 彭坤, 等. 湘潭市社区老年人肌少症患病率调查及预测模型构建. 中华老年多器官疾病杂志, 2023, 22(9): 663-668.
18. Yu M, Pan M, Liang Y, et al. A nomogram for screening sarcopenia in Chinese type 2 diabetes mellitus patients. Exp Gerontol, 2023, 172: 112069.
19. Zhang Y, Zhu Y. Development and validation of risk prediction model for sarcopenia in patients with colorectal cancer. Front Oncol, 2023, 13: 1172096.
20. Zhang H, Yin M, Liu Q, et al. Machine and deep learning-based clinical characteristics and laboratory markers for the prediction of sarcopenia. Chin Med J (Engl), 2023, 136(8): 967-973.
21. Wu J, Lin S, Guan J, et al. Prediction of the sarcopenia in peritoneal dialysis using simple clinical information: a machine learning-based model. Semin Dial, 2023, 36(5): 390-398.
22. 刘艳平, 谭明杨, 徐超强, 等. 社区老年慢性病患者肌少症风险预测模型的构建. 中国护理管理, 2022, 22(12): 1814-1819.
23. 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. 护理研究, 2022, 36(20): 3586-3591.
24. 韩婷, 钱绪芬, 王庆芳, 等. 基于 Logistic 回归和决策树模型的老年住院患者肌少症风险的影响因素分析. 护理学报, 2022, 29(12): 56-62.
25. 周起帆, 尹丽霞, 张海林, 等. 中青年维持性血液透析患者肌少症预测模型的构建与验证. 实用临床医药杂志, 2022, 26(5): 44-47, 53.
26. Yu S, Chen L, Zhang Y, et al. A combined diagnostic approach based on serum biomarkers for sarcopenia in older patients with hip fracture. Australas J Ageing, 2022, 41(4): e339-e347.
27. Mo YH, Su YD, Dong X, et al. Development and validation of a nomogram for predicting sarcopenia in community-dwelling older adults. J Am Med Dir Assoc, 2022, 23(5): 715-721.
28. Du X, Chen G, Zhang H, et al. Development of a practical screening tool to predict sarcopenia in patients on maintenance hemodialysis. Med Sci Monit, 2022, 28: e937504.
29. Cai G, Ying J, Pan M, et al. Development of a risk prediction nomogram for sarcopenia in hemodialysis patients. BMC Nephrol, 2022, 23(1): 319.
30. Chen YS, Cai YX, Kang XR, et al. Predicting the risk of sarcopenia in elderly patients with patellar fracture: development and assessment of a new predictive nomogram. PeerJ, 2020, 8: e8793.
31. Cui M, Gang X, Gao F, et al. Risk assessment of sarcopenia in patients with type 2 diabetes mellitus using data mining methods. Front Endocrinol (Lausanne), 2020, 11: 123.
32. 张颖, 许晓磊, 汪元浚, 等. 老年住院患者肌肉减少症发生的危险因素分析及预测模型建立. 中国实用护理杂志, 2020, 36(30): 2337-2342.
33. 王晶, 李玲利, 赵春林, 等. 机器学习在构建护理风险预测模型中的研究进展. 护士进修杂志, 2022, 37(23): 2167-2171.
34. Handelman GS, Kok HK, Chandra RV, et al. eDoctor: machine learning and the future of medicine. J Intern Med, 2018, 284(6): 603-619.
35. 徐园, 朱丽筠, 王钰, 等. 我国护理学者开展预测模型研究的现状和启示: 一项范围综述. 中国护理管理, 2022, 22(5): 744-749.

West China Medical Journal

Scoping review of sarcopenia risk prediction models in China

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