中国血液净化

中国血液净化 ›› 2025, Vol. 24 ›› Issue (03): 198-202.doi: 10.3969/j.issn.1671-4091.2025.03.006

• 临床研究 • 上一篇    下一篇

维持性血液透析患者肌少症风险预测模型的构建与验证

陆 飞    袁卫东    范亚平   

  1. 226100 南通，1南通市海门区人民医院肾内科
    226001 南通，2南通大学附属医院肾内科
  • 收稿日期:2024-09-06 修回日期:2024-12-14 出版日期:2025-03-12 发布日期:2025-03-12
  • 通讯作者: 范亚平 E-mail:fanyp19107@126.com
  • 基金资助:

Construction and validation of a risk prediction model for sarcopenia in maintenance hemodialysis patients

LU Fei, YUAN Wei-dong, FAN Ya-ping   

  1. Department of Nephrology, Nantong Haimen People's Hospital, Nantong 226100, China; 2Department of Nephrology, The Affiliated Hospital of Nantong University, Nantong 226001, China
  • Received:2024-09-06 Revised:2024-12-14 Online:2025-03-12 Published:2025-03-12
  • Contact: 226001 南通，2南通大学附属医院肾内科 E-mail:fanyp19107@126.com

摘要： 目的  通过Meta分析构建维持性血液透析(maintenance hemodialysis,MHD)患者肌少症风险预测模型，同时验证其预测效能。 方法 计算机检索中国知网、中国生物医学文献数据库、维普、万方、PubMed、Embase、Web of Science等发表的关于MHD肌少症影响因素的文献，检索时限为2010年1月—2022年4月。按照纳排标准筛选文献并进行Meta分析，构建MHD患者肌少症风险预测模型。回顾性选取2022年3月—2023年3月南通市海门区人民医院血液净化中心收治的血液透析患者作为验证集，采用ROC曲线、Hosmer-Lemeshow检验评估风险预测模型的效能。 结果 共纳入20篇文献。Meta分析结果显示：年龄≥60岁(RR=1.781,95% CI:1.661～4.623,P＜0.001)、C反应蛋白增高(RR=1.663,95% CI:1.234～4.923,P＜0.001)、低活动水平(RR=1.488,95% CI:1.214～3.853,P＜0.001)、糖尿病史(RR=1.559,95% CI:1.222～2.732,P＜0.001)、透析龄长(RR=1.101,95% CI:1.041～1.161,P＜0.001)、男性(RR=1.354,95% CI:1.142～2.958,P＜0.001)、改良主观综合性营养评估(modified quantitative subjective global assessment,MQSGA)评分高(RR=1.523,95% CI:1.323～3.266,P＜0.001)是MHD患者发生肌少症的危险因素，高体质量指数(body mass index,BMI)(RR=0.553,95% CI:0.122～0.986,P＜0.001)和高血磷(RR=0.586,95% CI:0.443～0.777,P＜0.001)是其保护因素。根据Meta分析结果构建MHD肌少症发生风险的预测模型，风险预测模型预测验证集发生肌少症的AUC为0.849(95% CI：0.768～0.930),最佳截断值为35分，灵敏度为70.0%、特异度为93.7%，Hosmer-Lemeshow检验结果显示χ2=8.971，P=0.201。 结论　本研究构建的风险预测模型对MHD患者发生肌少症具有良好的预测效能和区分度，具有一定的临床应用和推广价值。

关键词: 维持性血液透析, 肌少症, 风险预测模型

Abstract: Objective  To construct a risk prediction model for sarcopenia in maintenance hemodialysis (MHD) patients based on the data from meta-analysis and to verify the predictive efficacy of the model.  Methods   Literature about the risk factors for sarcopenia in MHD patients published by CNKI, China Biology Medicine disc, VIP, Wanfang, Pub Med, Embase, Web of Science were retrieved . The search period was from January 2010 to April 2022. The related articles were manually retrieved and rescreened according to the inclusion and exclusion criteria, and meta-analysis was then performed. A risk prediction model using the data from meta-analysis for the presence of sarcopenia in MHD patients was constructed. A total of 134 MHD patients admitted to Nantong Haimen People's Hospital from March 2022 to March 2023 were retrospectively recruited as a validation set. Receiver operating characteristic (ROC) curve and Hosmer-Lemeshow test were used to evaluate the efficacy of the model to predict sarcopenia in the validation set.  Results  A total of 20 studies were found from the databases. Meta-analysis showed that age ≥60 years old (RR=1.781, 95% CI:1.661～4.623,P＜0.001), higher C-reactive protein (RR=1.663, 95% CI:1.234～4.923, P＜0.001), low physical activity (RR=1.488, 95% CI:1.214～3.853,P＜0.001), history of diabetes (RR=1.559, 95% CI:1.222～2.732, P＜0.001), longer dialysis vintage (RR=1.101,95% CI:1.041～1.161,P＜0.001), male (RR=1.354, 95% CI: 1.142～2.958, P＜0.001), and higher modified quantitative subjective global assessment (MQSGA) score (RR=1.523, 95% CI:1.323～3.266, P＜0.001) were the risk factors, while higher body mass index (BMI) (RR=0.553, 95% CI:0.122～0.986, P＜0.001) and high plasma phosphorus (RR=0.586, 95% CI:0.443～0.777, P＜0.001) were the protective factors for sarcopenia in MHD patients. Based on the results of the meta-analysis, a risk prediction model for sarcopenia in MHD patients was constructed. ROC curve analysis showed that the area under the curve (AUC) of the model for prediction of sarcopenia in the validation set was 0.849 (95% CI: 0.768～0.930), the optimal truncation value was 35 scores, the sensitivity was 70.0%, and the specificity was 93.7%. Hosmer-Lemeshow test showed χ2=8.97 and P=0.201.  Conclusion  The risk prediction model we constructed has better prediction and discrimination abilities for the presence of sarcopenia in MHD patients. This prediction model may be clinically useful.

Key words: Maintenance hemodialysis, Sarcopenia, Risk prediction model

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