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.	鄧菲菲, 趙智芳, 李建瓊, 等. 社區老年肌少癥風險篩查工具與方法的研究進展. 軍事護理, 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.	陳香萍, 張奕, 莊一渝, 等. PROBAST: 診斷或預后多因素預測模型研究偏倚風險的評估工具. 中國循證醫學雜志, 2020, 20(6): 737-744.
8.	孔令慧, 于杰, 張會君, 等. 基于 Logistic 回歸和決策樹的老年腦卒中病人肌少癥風險預測模型的構建. 護理研究, 2024, 38(10): 1703-1710.
9.	馬娟, 侯彥杰, 黎妲, 等. 腫瘤患者肌少癥的危險因素分析及模型建立. 現代腫瘤醫學, 2024, 32(10): 1877-1881.
10.	李香香, 王梅芳, 馮秀娟, 等. 中老年 2 型糖尿病患者肌少癥風險預測模型的構建與驗證. 預防醫學情報雜志, 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.	陳琳琳, 達雪萍, 馬松華. 老年腦卒中患者繼發肌少癥危險因素及預測模型構建. 中國老年學雜志, 2023, 43(20): 4981-4983.
13.	周銀, 莊彩麗, 倪好, 等. 肺癌患者并發肌肉減少癥的危險因素分析及其列線圖預測模型的應用價值. 腫瘤代謝與營養電子雜志, 2023, 10(5): 652-657.
14.	張媛, 馬艷, 史凌云, 等. 基于 Logistic 回歸、決策樹、神經網絡構建住院老年患者肌少癥相對風險預測模型. 現代醫學, 2023, 51(8): 1134-1143.
15.	陳禧, 肖江琴, 黃莉. 老年慢性阻塞性肺疾病并發肌少癥風險預警模型構建與驗證. 中華保健醫學雜志, 2023, 25(6): 646-649.
16.	秦紅菊, 倪燕丹, 張小梅, 等. 維持性血液透析患者肌少癥發生風險預測模型的構建. 現代臨床護理, 2023, 22(6): 15-21.
17.	陳佳惟, 李澤云, 彭坤, 等. 湘潭市社區老年人肌少癥患病率調查及預測模型構建. 中華老年多器官疾病雜志, 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.	劉艷平, 譚明楊, 徐超強, 等. 社區老年慢性病患者肌少癥風險預測模型的構建. 中國護理管理, 2022, 22(12): 1814-1819.
23.	丁妍, 常立陽, 張紅梅. 維持性血液透析病人肌少癥發生風險預測模型的構建與驗證. 護理研究, 2022, 36(20): 3586-3591.
24.	韓婷, 錢緒芬, 王慶芳, 等. 基于 Logistic 回歸和決策樹模型的老年住院患者肌少癥風險的影響因素分析. 護理學報, 2022, 29(12): 56-62.
25.	周起帆, 尹麗霞, 張海林, 等. 中青年維持性血液透析患者肌少癥預測模型的構建與驗證. 實用臨床醫藥雜志, 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.	張穎, 許曉磊, 汪元浚, 等. 老年住院患者肌肉減少癥發生的危險因素分析及預測模型建立. 中國實用護理雜志, 2020, 36(30): 2337-2342.
33.	王晶, 李玲利, 趙春林, 等. 機器學習在構建護理風險預測模型中的研究進展. 護士進修雜志, 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.	徐園, 朱麗筠, 王鈺, 等. 我國護理學者開展預測模型研究的現狀和啟示: 一項范圍綜述. 中國護理管理, 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. 鄧菲菲, 趙智芳, 李建瓊, 等. 社區老年肌少癥風險篩查工具與方法的研究進展. 軍事護理, 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. 陳香萍, 張奕, 莊一渝, 等. PROBAST: 診斷或預后多因素預測模型研究偏倚風險的評估工具. 中國循證醫學雜志, 2020, 20(6): 737-744.
8. 孔令慧, 于杰, 張會君, 等. 基于 Logistic 回歸和決策樹的老年腦卒中病人肌少癥風險預測模型的構建. 護理研究, 2024, 38(10): 1703-1710.
9. 馬娟, 侯彥杰, 黎妲, 等. 腫瘤患者肌少癥的危險因素分析及模型建立. 現代腫瘤醫學, 2024, 32(10): 1877-1881.
10. 李香香, 王梅芳, 馮秀娟, 等. 中老年 2 型糖尿病患者肌少癥風險預測模型的構建與驗證. 預防醫學情報雜志, 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. 陳琳琳, 達雪萍, 馬松華. 老年腦卒中患者繼發肌少癥危險因素及預測模型構建. 中國老年學雜志, 2023, 43(20): 4981-4983.
13. 周銀, 莊彩麗, 倪好, 等. 肺癌患者并發肌肉減少癥的危險因素分析及其列線圖預測模型的應用價值. 腫瘤代謝與營養電子雜志, 2023, 10(5): 652-657.
14. 張媛, 馬艷, 史凌云, 等. 基于 Logistic 回歸、決策樹、神經網絡構建住院老年患者肌少癥相對風險預測模型. 現代醫學, 2023, 51(8): 1134-1143.
15. 陳禧, 肖江琴, 黃莉. 老年慢性阻塞性肺疾病并發肌少癥風險預警模型構建與驗證. 中華保健醫學雜志, 2023, 25(6): 646-649.
16. 秦紅菊, 倪燕丹, 張小梅, 等. 維持性血液透析患者肌少癥發生風險預測模型的構建. 現代臨床護理, 2023, 22(6): 15-21.
17. 陳佳惟, 李澤云, 彭坤, 等. 湘潭市社區老年人肌少癥患病率調查及預測模型構建. 中華老年多器官疾病雜志, 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. 劉艷平, 譚明楊, 徐超強, 等. 社區老年慢性病患者肌少癥風險預測模型的構建. 中國護理管理, 2022, 22(12): 1814-1819.
23. 丁妍, 常立陽, 張紅梅. 維持性血液透析病人肌少癥發生風險預測模型的構建與驗證. 護理研究, 2022, 36(20): 3586-3591.
24. 韓婷, 錢緒芬, 王慶芳, 等. 基于 Logistic 回歸和決策樹模型的老年住院患者肌少癥風險的影響因素分析. 護理學報, 2022, 29(12): 56-62.
25. 周起帆, 尹麗霞, 張海林, 等. 中青年維持性血液透析患者肌少癥預測模型的構建與驗證. 實用臨床醫藥雜志, 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. 張穎, 許曉磊, 汪元浚, 等. 老年住院患者肌肉減少癥發生的危險因素分析及預測模型建立. 中國實用護理雜志, 2020, 36(30): 2337-2342.
33. 王晶, 李玲利, 趙春林, 等. 機器學習在構建護理風險預測模型中的研究進展. 護士進修雜志, 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. 徐園, 朱麗筠, 王鈺, 等. 我國護理學者開展預測模型研究的現狀和啟示: 一項范圍綜述. 中國護理管理, 2022, 22(5): 744-749.

West China Medical Journal

Scoping review of sarcopenia risk prediction models in China

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