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Article(id=1322931522952635169, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322931411006665649, articleNumber=null, orderNo=null, doi=10.12102/j.issn.1009-6493.2026.13.011, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1773676800000, receivedDateStr=2026-03-17, revisedDate=1778774400000, revisedDateStr=2026-05-15, acceptedDate=null, acceptedDateStr=null, onlineDate=1793341053951, onlineDateStr=2026-10-30, pubDate=1783612800000, pubDateStr=2026-07-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793341053951, onlineIssueDateStr=2026-10-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793341053950, creator=13701087609, updateTime=1793341053950, updator=13701087609, issue=Issue{id=1322931411006665649, tenantId=1146029695717560320, journalId=1220038315760373764, year='2026', volume='40', issue='13', pageStart='2193', pageEnd='2384', issueExtLink='null', onlineDate='null', pubDate='1783612800000', pubDateStr='2026-07-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793341027261, creator='13701087609', updateTime=1793343114456, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322940165425082661, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322931411006665649, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322940165425082662, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322931411006665649, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=2270, endPage=2277, ext={EN=ArticleExt(id=1322931523246236450, articleId=1322931522952635169, tenantId=1146029695717560320, journalId=1220038315760373764, language=EN, title=Development and validation of a sarcopenia risk prediction model for peritoneal dialysis patients, columnId=1220689484526572183, journalTitle=Chinese Nursing Research, columnName=Original Articles, runingTitle=null, highlight=null, articleAbstract=
Objective

To develop and validate a high-precision,interpretable sarcopenia risk prediction model for peritoneal dialysis patients.

Methods

A retrospective study included 441 peritoneal dialysis patients from two tertiary hospitals in Anhui province from January 2023 to June 2025 as the training set,and 198 patients as the external validation set.Core predictors were screened using univariate analysis,Logistic regression,and LASSO regression.Eight machine learning algorithms were used to build models.Internal validation employed Bootstrap sampling,and external validation used an independent dataset.The optimal model was interpreted using the Shapley additive explanations(SHAP).

Results

Six core predictors were identified:sex,dialysis vintage,and diabetes,prealbumin、interleukin-6、25-hydroxyvitamin D.The support vector machine(SVM) model showed the best performance,with an area under the receiver operating characteristic curve(AUC) of 0.958 in the internal test set and 0.968 in the external validation set.Accuracy,sensitivity,and specificity all exceeded 0.9.SHAP analysis revealed the contribution of each predictor and their nonlinear relationships.

Conclusions

The developed machine learning model demonstrates high accuracy,generalizability,and interpretability.It can provide intelligent decision support for early identification and precise intervention of sarcopenia in peritoneal dialysis patients,showing potential for clinical translation.

, authors=Jin LI, Wenli ZHU, Pei ZHANG, Zhe FAN, authorsList=Jin LI, Wenli ZHU, Pei ZHANG, Zhe FAN, authorCompany=null, correspAuthors=Jin LI, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1322931524437418795, articleId=1322931522952635169, tenantId=1146029695717560320, journalId=1220038315760373764, language=CN, title=腹膜透析病人肌少症风险预测模型的构建及验证, columnId=1220689484681761433, journalTitle=护理研究, columnName=科研论著, runingTitle=null, highlight=null, articleAbstract=
目的

构建高精度、可解释的腹膜透析病人肌少症风险预测模型并进行验证。

方法

回顾性纳入安徽省2所三级甲等医院2023年1月—2025年6月441例腹膜透析病人为训练集，198例为验证集；经单因素分析、Logistic与LASSO回归筛选核心变量，采用8种机器学习算法建模，以Bootstrap抽样内部验证、独立集外部验证，并用沙普利可加解释（SHAP）分析最优模型。

结果

最终获得性别、透析龄、糖尿病、前清前白蛋白、白细胞介素-6、25-羟基维生素D 6个核心预测因子；支持向量机模型性能最优，内部测试集受试者工作特征曲线下面积（AUC）为0.958，外部验证集AUC达0.968，准确率、灵敏度与特异度均>0.9；SHAP分析揭示了各因子贡献度与非线性关系。

结论

构建的机器学习模型精度高、泛化性与可解释性强，可为腹膜透析病人肌少症早期识别与精准干预提供智能化决策支持，具备临床转化潜力。

, authors=李锦, 朱文莉, 张培, 范哲, authorsList=李锦, 朱文莉, 张培, 范哲, authorCompany=null, correspAuthors=李锦, authorNote=null, correspAuthorsNote=
LI Jin, E-mail:
李锦，副主任护师，本科，E-mail:
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pmcid=null, year=2023, volume=15, issue=5, pageStart=1237, pageEnd=null, url=null, language=null, rfNumber=21, rfOrder=20, authorNames=Jeznach-Steinhagen A, Boniecka I, Rymarz A, journalName=Nutrients, refType=null, unstructuredReference=Jeznach-Steinhagen A,Boniecka I,Rymarz A,et al.Higher muscle mass and higher serum prealbumin levels are associated with better survival in hemodialysis patients during a five-year observation period[J].Nutrients,2023,15(5):1237., articleTitle=Higher muscle mass and higher serum prealbumin levels are associated with better survival in hemodialysis patients during a five-year observation period, refAbstract=null), Reference(id=1322931532737946485, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322931522952635169, doi=null, pmid=null, pmcid=null, year=2025, volume=27, issue=1, pageStart=3, pageEnd=null, url=null, language=null, rfNumber=22, rfOrder=21, authorNames=Chen F Y, Fu J J, Feng H, journalName=NeuroMolecular Medicine, 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articleTitle=Prevalence and risk factors of sarcopenia in patients on maintenance hemodialysis:a retrospective cohort study, refAbstract=null), Reference(id=1322931532893135735, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322931522952635169, doi=null, pmid=null, pmcid=null, year=2023, volume=26, issue=9, pageStart=1080, pageEnd=1085, url=null, language=null, rfNumber=24, rfOrder=23, authorNames=林燕平, 黄佳纯, 郭海威, journalName=中国全科医学, refType=null, unstructuredReference=林燕平,黄佳纯,郭海威,等.原发性骨质疏松症患者中医证型分布规律与肌-骨-脂关系研究[J].中国全科医学,2023,26(9):1080-1085., articleTitle=原发性骨质疏松症患者中医证型分布规律与肌-骨-脂关系研究, refAbstract=null), Reference(id=1322931532964438904, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322931522952635169, doi=null, pmid=null, pmcid=null, year=2024, volume=25, issue=1, pageStart=848, pageEnd=null, url=null, language=null, rfNumber=25, rfOrder=24, authorNames=Abdi D R, Zargar B N, Shirazi S, journalName=Trials, refType=null, unstructuredReference=Abdi D R,Zargar B N,Shirazi S,et al.The effect of Sarcomeal® oral supplementation plus vitamin D3 on muscle parameters and metabolic factors in diabetic sarcopenia patients:study protocol of a randomized controlled clinical trial[J].Trials,2024,25(1):848., articleTitle=The effect of Sarcomeal® oral supplementation plus vitamin D3 on muscle parameters and metabolic factors in diabetic sarcopenia patients:study protocol of a randomized controlled clinical trial, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1322931524642939692, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322931522952635169, xref=1, ext=[AuthorCompanyExt(id=1322931524651328301, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322931522952635169, companyId=1322931524642939692, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1The First Affiliated Hospital of Anhui Medical University, Anhui 230022 China), AuthorCompanyExt(id=1322931524659716910, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322931522952635169, companyId=1322931524642939692, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1安徽医科大学第一附属医院（肾脏内科），安徽 230022)])], figs=[ArticleFig(id=1322931528023548751, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322931522952635169, language=EN, label=Figure 1, caption=Graph of independent variable selection by LASSO regression, figureFileSmall=rDMOfP6WlcW5nrTOWqgJpw==, figureFileBig=FZ/SUfeLQG5iFkSYrb7HZA==, tableContent=null), ArticleFig(id=1322931528090657616, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322931522952635169, language=CN, label=图1, caption=LASSO回归筛选自变量图

（A为回归系数曲线图，B为最佳惩罚系数λ的变化）

, figureFileSmall=rDMOfP6WlcW5nrTOWqgJpw==, figureFileBig=FZ/SUfeLQG5iFkSYrb7HZA==, tableContent=null), ArticleFig(id=1322931528266818385, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322931522952635169, language=EN, label=Figure 2, caption=ROC curve of the support vector machine in the external validation cohort, figureFileSmall=zj3AZ31H7k/4npRlWlDj4A==, figureFileBig=WUjvOsj23NY4/1oZ3m7dHA==, tableContent=null), ArticleFig(id=1322931528350704466, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322931522952635169, language=CN, label=图2, caption=支持向量机在外部验证集上的ROC曲线, figureFileSmall=zj3AZ31H7k/4npRlWlDj4A==, figureFileBig=WUjvOsj23NY4/1oZ3m7dHA==, tableContent=null), ArticleFig(id=1322931528430396243, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322931522952635169, language=EN, label=Figure 3, caption=Shapviz visualization graph, figureFileSmall=IFjT/rbq7cvhKfl4UmcB2g==, figureFileBig=5/CVPUURhKw3FfgHho2UlQ==, tableContent=null), ArticleFig(id=1322931528514282324, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322931522952635169, language=CN, label=图3, caption=Shapviz可视化图, figureFileSmall=IFjT/rbq7cvhKfl4UmcB2g==, figureFileBig=5/CVPUURhKw3FfgHho2UlQ==, tableContent=null), ArticleFig(id=1322931528593974101, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322931522952635169, language=EN, label=Figure 4, caption=SHAP feature analysis of the support vector model, figureFileSmall=cDwxRaObhJOvvR6rSVMTWw==, figureFileBig=7HaNwrseHDYU7cFUvsFgWQ==, tableContent=null), ArticleFig(id=1322931528665277270, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322931522952635169, language=CN, label=图4, caption=支持向量机模型SHAP特征分析, figureFileSmall=cDwxRaObhJOvvR6rSVMTWw==, figureFileBig=7HaNwrseHDYU7cFUvsFgWQ==, tableContent=null), ArticleFig(id=1322931528749163351, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322931522952635169, language=EN, label=Table 1, caption=

Univariate analysis of factors influencing the occurrence of sarcopenia in peritoneal dialysis patients

, figureFileSmall=null, figureFileBig=null, tableContent=
项目非肌少症组（n=260）肌少症组（n=181）P
性别[例（%）] 男147(56.54)169(93.37)<0.05
女113(43.46)12(6.63)
糖尿病[例（%）] 否139(53.46)121(66.85)<0.05
是121(46.54)60(33.15)
高血压[例（%）] 否57(21.92)23(12.71)>0.05
是203(78.08)158(87.29)
腹膜透析方式[例（%）] CAPD255(98.08)179(98.90)>0.05
APD5(1.92)2(1.10)
腹膜转运[例（%）] 高转运134(51.54)88(48.62)>0.05
低转运126(48.46)93(51.38)
年龄（岁）46(36,54)44(34,53)>0.05
透析龄（月）90.15±58.73110.26±59.87<0.05
血清前白蛋白（mg/L）324.52(146.73,396.54)126.63(103.76,164.78)<0.05
血清白蛋白（g/L）35.30(29.58,39.42)32.70(27.90,37.50)<0.05
25-羟基维生素D（ng/mL）37.63(29.74,46.56)6.78(4.78,21.78)<0.05
超敏C反应蛋白（mg/L）13.20(5.60,22.47)17.60(6.00,27.50)<0.05
白细胞介素-6（pg/mL）5.47(3.92,10.03)47.84(6.64,104.70)<0.05
小腿围（cm）31(27,35)27(24,31)<0.05
24 h残余尿量（mL）587(399,800)645(498,829)<0.05
), ArticleFig(id=1322931528824660824, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322931522952635169, language=CN, label=表1, caption=

腹膜透析病人肌少症发生影响因素的单因素分析

, figureFileSmall=null, figureFileBig=null, tableContent=
项目非肌少症组（n=260）肌少症组（n=181）P
性别[例（%）] 男147(56.54)169(93.37)<0.05
女113(43.46)12(6.63)
糖尿病[例（%）] 否139(53.46)121(66.85)<0.05
是121(46.54)60(33.15)
高血压[例（%）] 否57(21.92)23(12.71)>0.05
是203(78.08)158(87.29)
腹膜透析方式[例（%）] CAPD255(98.08)179(98.90)>0.05
APD5(1.92)2(1.10)
腹膜转运[例（%）] 高转运134(51.54)88(48.62)>0.05
低转运126(48.46)93(51.38)
年龄（岁）46(36,54)44(34,53)>0.05
透析龄（月）90.15±58.73110.26±59.87<0.05
血清前白蛋白（mg/L）324.52(146.73,396.54)126.63(103.76,164.78)<0.05
血清白蛋白（g/L）35.30(29.58,39.42)32.70(27.90,37.50)<0.05
25-羟基维生素D（ng/mL）37.63(29.74,46.56)6.78(4.78,21.78)<0.05
超敏C反应蛋白（mg/L）13.20(5.60,22.47)17.60(6.00,27.50)<0.05
白细胞介素-6（pg/mL）5.47(3.92,10.03)47.84(6.64,104.70)<0.05
小腿围（cm）31(27,35)27(24,31)<0.05
24 h残余尿量（mL）587(399,800)645(498,829)<0.05
), ArticleFig(id=1322931528937907033, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322931522952635169, language=EN, label=Table 2, caption=

Multivariate Logistic regression analysis of factors associated with sarcopenia in peritoneal dialysis patients

, figureFileSmall=null, figureFileBig=null, tableContent=
变量回归系数标准误Z值POR值95%CI
血清前白蛋白-0.0110.0052.3040.0210.989[0.980,0.998]
白细胞介素-60.1960.0762.5730.0101.216[1.048,1.411]
性别2.6320.5904.461<0.00113.904[4.374,44.193]
透析龄0.0580.0163.531<0.0011.060[1.026,1.095]
25-羟基维生素D-0.0140.0053.0000.0030.986[0.977,0.995]
糖尿病3.9570.5876.742<0.00152.316[16.557,165.310]
), ArticleFig(id=1322931529021793114, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322931522952635169, language=CN, label=表2, caption=

腹膜透析病人肌少症发生影响因素的多因素Logistic回归分析

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变量回归系数标准误Z值POR值95%CI
血清前白蛋白-0.0110.0052.3040.0210.989[0.980,0.998]
白细胞介素-60.1960.0762.5730.0101.216[1.048,1.411]
性别2.6320.5904.461<0.00113.904[4.374,44.193]
透析龄0.0580.0163.531<0.0011.060[1.026,1.095]
25-羟基维生素D-0.0140.0053.0000.0030.986[0.977,0.995]
糖尿病3.9570.5876.742<0.00152.316[16.557,165.310]
), ArticleFig(id=1322931529097290587, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322931522952635169, language=EN, label=Table 3, caption=

Comparison of the performance of 8 prediction models

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模型AUC灵敏度特异度准确率召回率F1分数95%CI
XGBoost0.9400.9770.9190.8650.9780.918[0.884,0.974]
LightGBM0.9480.9850.9200.9040.9850.943[0.917,0.970]
CatBoost0.7470.7330.7550.6570.7330.693[0.694,0.794]
随机森林0.8330.7170.8950.7860.7170.750[0.759,0.892]
支持向量机0.9580.9920.9310.9020.9920.944[0.928,0.975]
K近邻0.8670.9000.8460.7880.9000.841[0.824,0.903]
朴素贝叶斯0.9060.9580.8720.8270.9580.888[0.868,0.936]
神经网络0.9451.0000.9020.8871.0000.940[0.913,0.968]
), ArticleFig(id=1322931529176982364, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322931522952635169, language=CN, label=表3, caption=

8种预测模型性能比较

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模型AUC灵敏度特异度准确率召回率F1分数95%CI
XGBoost0.9400.9770.9190.8650.9780.918[0.884,0.974]
LightGBM0.9480.9850.9200.9040.9850.943[0.917,0.970]
CatBoost0.7470.7330.7550.6570.7330.693[0.694,0.794]
随机森林0.8330.7170.8950.7860.7170.750[0.759,0.892]
支持向量机0.9580.9920.9310.9020.9920.944[0.928,0.975]
K近邻0.8670.9000.8460.7880.9000.841[0.824,0.903]
朴素贝叶斯0.9060.9580.8720.8270.9580.888[0.868,0.936]
神经网络0.9451.0000.9020.8871.0000.940[0.913,0.968]
), ArticleFig(id=1322931529265062749, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322931522952635169, language=EN, label=Table 4, caption=

Comparison of baseline characteristics between the development cohort and external validation cohort

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特征训练集(n=441)验证集(n=198)统计值P
年龄(岁)46.0±10.247.6±12.3t=-1.620.105
男性[例(%)]316(71.66)112(56.60)χ²=2.010.156
透析龄(月)98.31±60.0296.20±28.70t=1.140.253
糖尿病[例(%)]181(41.04)78(39.40)χ²=0.120.728
肌少症发生率[例(%)]181(41.04)81(40.90)χ²=0.010.976
血清前白蛋白(mg/L)223.56±82.13328.50±75.60t=-1.890.059
白细胞介素-6(pg/mL)26.66±22.3112.80±5.20t=1.760.078
25-羟基维生素D(ng/mL)22.21±12.4518.40±6.90t=1.900.058
), ArticleFig(id=1322931529348948830, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322931522952635169, language=CN, label=表4, caption=

训练集与外部验证集基线资料均衡性比较

, figureFileSmall=null, figureFileBig=null, tableContent=
特征训练集(n=441)验证集(n=198)统计值P
年龄(岁)46.0±10.247.6±12.3t=-1.620.105
男性[例(%)]316(71.66)112(56.60)χ²=2.010.156
透析龄(月)98.31±60.0296.20±28.70t=1.140.253
糖尿病[例(%)]181(41.04)78(39.40)χ²=0.120.728
肌少症发生率[例(%)]181(41.04)81(40.90)χ²=0.010.976
血清前白蛋白(mg/L)223.56±82.13328.50±75.60t=-1.890.059
白细胞介素-6(pg/mL)26.66±22.3112.80±5.20t=1.760.078
25-羟基维生素D(ng/mL)22.21±12.4518.40±6.90t=1.900.058
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腹膜透析病人肌少症风险预测模型的构建及验证
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李锦 , 朱文莉 , 张培 , 范哲
护理研究 | 科研论著 2026,40(13): 2270-2277
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护理研究 |科研论著 2026 , 40 (13) : 2270 -2277
腹膜透析病人肌少症风险预测模型的构建及验证
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李锦 , 朱文莉, 张培, 范哲
作者信息
  • 1安徽医科大学第一附属医院（肾脏内科），安徽 230022
通讯作者:
LI Jin, E-mail:
李锦，副主任护师，本科，E-mail:
Development and validation of a sarcopenia risk prediction model for peritoneal dialysis patients
Jin LI , Wenli ZHU, Pei ZHANG, Zhe FAN
Affiliations
  • 1The First Affiliated Hospital of Anhui Medical University, Anhui 230022 China
出版时间: 2026-07-10 doi: 10.12102/j.issn.1009-6493.2026.13.011
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摘要
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目的

构建高精度、可解释的腹膜透析病人肌少症风险预测模型并进行验证。

方法

回顾性纳入安徽省2所三级甲等医院2023年1月—2025年6月441例腹膜透析病人为训练集，198例为验证集；经单因素分析、Logistic与LASSO回归筛选核心变量，采用8种机器学习算法建模，以Bootstrap抽样内部验证、独立集外部验证，并用沙普利可加解释（SHAP）分析最优模型。

结果

最终获得性别、透析龄、糖尿病、前清前白蛋白、白细胞介素-6、25-羟基维生素D 6个核心预测因子；支持向量机模型性能最优，内部测试集受试者工作特征曲线下面积（AUC）为0.958，外部验证集AUC达0.968，准确率、灵敏度与特异度均>0.9；SHAP分析揭示了各因子贡献度与非线性关系。

结论

构建的机器学习模型精度高、泛化性与可解释性强，可为腹膜透析病人肌少症早期识别与精准干预提供智能化决策支持，具备临床转化潜力。

腹膜透析  /  肌少症  /  机器学习  /  影响因素  /  预测模型  /  支持向量机
Objective

To develop and validate a high-precision,interpretable sarcopenia risk prediction model for peritoneal dialysis patients.

Methods

A retrospective study included 441 peritoneal dialysis patients from two tertiary hospitals in Anhui province from January 2023 to June 2025 as the training set,and 198 patients as the external validation set.Core predictors were screened using univariate analysis,Logistic regression,and LASSO regression.Eight machine learning algorithms were used to build models.Internal validation employed Bootstrap sampling,and external validation used an independent dataset.The optimal model was interpreted using the Shapley additive explanations(SHAP).

Results

Six core predictors were identified:sex,dialysis vintage,and diabetes,prealbumin、interleukin-6、25-hydroxyvitamin D.The support vector machine(SVM) model showed the best performance,with an area under the receiver operating characteristic curve(AUC) of 0.958 in the internal test set and 0.968 in the external validation set.Accuracy,sensitivity,and specificity all exceeded 0.9.SHAP analysis revealed the contribution of each predictor and their nonlinear relationships.

Conclusions

The developed machine learning model demonstrates high accuracy,generalizability,and interpretability.It can provide intelligent decision support for early identification and precise intervention of sarcopenia in peritoneal dialysis patients,showing potential for clinical translation.

peritoneal dialysis  /  sarcopenia  /  machine learning  /  influencing factors  /  prediction model  /  support vector machine
李锦, 朱文莉, 张培, 范哲. 腹膜透析病人肌少症风险预测模型的构建及验证. 护理研究, 2026 , 40 (13) : 2270 -2277 . DOI: 10.12102/j.issn.1009-6493.2026.13.011
Jin LI, Wenli ZHU, Pei ZHANG, Zhe FAN. Development and validation of a sarcopenia risk prediction model for peritoneal dialysis patients[J]. Chinese Nursing Research, 2026 , 40 (13) : 2270 -2277 . DOI: 10.12102/j.issn.1009-6493.2026.13.011
正文
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腹膜透析（peritoneal dialysis,PD）是终末期肾病病人重要的肾脏替代治疗方式，在维持病人生命、提高生活质量方面具有不可替代的临床价值[1]。由于蛋白质能量消耗、微炎症状态、代谢紊乱及活动能力下降等多重因素影响，腹膜透析病人肌少症发生风险显著升高。受诊断标准与人群特征差异影响，该类病人肌少症患病率为4%~68%[2]；肌少症不仅会增加跌倒、骨折及心血管事件风险，还可升高全因死亡率，严重影响病人长期预后[3-4]。Meta分析显示，合并肌少症的腹膜透析病人死亡风险约为非肌少症病人的2.02倍[5]，凸显早期识别与干预的重要性。目前，国际上已将随机森林、支持向量机等机器学习方法用于透析人群肌肉量下降及肌少症预测，展现出良好的非线性拟合与预测优势[6-8]。但国内相关研究多以传统Logistic回归分析为主[9-10]，其在处理多维度、非线性临床数据时存在局限，且多为单中心、小样本设计，缺乏泛化能力强、可解释性高且适合临床推广的预测工具。为弥补上述不足，本研究基于多中心临床数据，构建并验证腹膜透析病人肌少症风险预测模型，结合沙普利可加解释（SHAP）开展可解释性分析，明确核心危险因素及其贡献路径，旨在为临床早期筛查高危人群、实施精准护理干预、改善病人生活质量与长期预后提供循证依据和智能化决策支持。
1　对象与方法
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1.1　研究对象
选取2023年1月—2025年6月安徽省2所三级甲等医院腹膜透析病人共441例为训练集，同期选取另一医院198例腹膜透析病人为外部验证集。纳入标准：规律腹膜透析治疗≥3个月；年龄≥18岁；知情同意参与研究。排除标准：合并恶性肿瘤、严重感染、心力衰竭、活动性肝病及胃肠道疾病；自身免疫性疾病；近3个月有手术、创伤史或使用免疫抑制剂、激素；肾移植或联合血液透析；存在生物电阻抗测量禁忌证。
样本量计算：本研究为肌少症风险预测模型构建研究，采用每例预测因子样本量（EPV）≥10的原则估算样本量。结合前期预试验及相关文献报道，初步筛选出10项潜在预测因子，估算最低样本量需≥100例；考虑到数据缺失、样本脱落及模型验证需求，扩大样本量至441例作为训练集，以保证统计效能。外部验证集样本量参考相关研究[11]，选取198例，满足模型泛化性验证最低样本量要求。
本研究经医院伦理委员会批准（审批号：PJ2025-06-57），所有受试者均签署知情同意书。
1.2　资料收集
1.2.1　一般资料和实验室指标
通过医院电子病历系统收集病人的临床资料。1）一般资料与透析特征：性别、年龄、透析龄、当前腹膜透析方式。2）并发症与腹膜功能：糖尿病、高血压病史、腹膜转运类型。3）实验室血清学指标：营养状态指标（血清前白蛋白、血清白蛋白、25-羟基维生素D）、炎症与代谢指标（超敏C反应蛋白、白细胞介素-6、血清磷、全段甲状旁腺激素、血红蛋白）。4）身体成分与体液指标：使用生物电阻抗分析仪测量脂肪组织指数、组织水分/细胞外水比值，体格测量指标为小腿围，肾功能残余指标为残余尿量。
1.2.2　肌少症诊断标准及相关参数的评估
1.2.2.1　肌少症诊断标准
严格遵循欧洲老年肌少症工作组（EWGSOP 2）诊断标准：1）肌肉力量下降，男性握力<27 kg，女性握力<16 kg；2）肌肉质量下降，瘦组织指数（LTI）男性<9.2 kg/m²，女性<7.2 kg/m²；3）躯体功能下降，4 m步速<0.8 m/s。满足肌肉力量下降+肌肉质量下降即可诊断为肌少症；若同时合并躯体功能下降，则诊断为严重肌少症[12]。
1.2.2.2　肌少症相关参数评估流程
1）人体成分测定：采用德国费森尤斯BCM人体成分分析仪，严格按照仪器操作规范测定瘦组织指数（LTI）、脂肪组织指数（FTI）及体液过多/细胞外液（OH/ECW），测量前指导病人去除金属物品、保持空腹静息状态≥30 min。2）肌肉力量评估：使用香山EH101电子握力器测量优势手握力，连续测量2次，取平均值作为最终结果；测量时指导病人保持坐位、手臂与地面平行，避免借力。3）躯体功能评估：采用4 m步速测试，指导病人以日常步行速度完成4 m直线行走，记录时间并计算步速，连续测量2次取平均值。
1.2.3　质量控制
1）人员规范：所有测量由2名经统一培训并考核合格的护士完成，确保操作流程标准化；2）仪器校准：人体成分分析仪、握力器定期进行计量校准，每次使用前核查仪器状态；3）测量质控：握力、步速等指标均测量2次取平均值，避免单次测量误差；4）数据管理：采用双人双录入法录入数据，录入后由专人交叉复核，及时修正错误，确保数据准确性。
1.3　统计学分析
使用R4.2.0与Python软件进行统计分析与模型构建，研究遵循个体预后或诊断多变量预测模型的透明报告声明（TRIPOD）[13]。符合正态分布的定量资料以均数±标准差（x±s）表示，非正态分布的定量资料以中位数（四分位数）[M(P25,P75)]表示；定性资料以例数、百分比（%）表示。组间比较采用χ²检验、t检验或非参数检验，检验水准α=0.05。
1.3.1　特征筛选
先进行单因素分析，将P<0.05的变量纳入多因素Logistic回归分析，筛选肌少症发生的独立影响因素；再采用LASSO回归（10折交叉验证选择最优λ）进一步降维、控制多重共线性。本研究采用两步筛选策略：先通过Logistic回归保证变量的临床意义与可解释性，再通过LASSO回归优化维度与稳健性；两者结果一致，进一步验证核心变量稳定可靠，兼顾临床实用性与统计严谨性。
1.3.2　模型构建
按7∶3划分训练集与测试集，构建XGBoost、LightGBM、CatBoost、随机森林(random forest)、支持向量机(support vector machine)、K近邻(K-nearest neighbor)、朴素贝叶斯(naive Bayes)、神经网络(neutral network)共8种模型。对8种模型均采用10折交叉验证进行参数优化；其中支持向量机采用网格搜索（GridSearchCV）优化惩罚系数C与核函数gamma，以最优参数组合构建最终模型。采用Bootstrap抽样1 000次进行内部验证。
1.3.3　模型评价与比较
以受试者工作特征（ROC）曲线下面积（AUC）、灵敏度、特异度、准确率、F1分数评价模型性能。绘制并计算决策曲线(decision curve analysis,DCA)和校准曲线(calibration curve)对模型临床实用价值和校准度进行对比[14]。模型间AUC比较采用DeLong检验。
2　结果
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2.1　腹膜透析病人肌少症发生影响因素的单因素分析
441例训练集腹膜透析病人中，肌少症发生率为41.04%（181/441）；198例外部验证集病人肌少症发生率为40.91%（81/198）。两组核心基线指标（年龄、性别、透析龄、糖尿病）及肌少症发生率比较，差异均无统计学意义（均P>0.05），提示基线均衡可比，可用于模型外部验证。
单因素分析显示，训练集中性别、透析龄、糖尿病、血清前白蛋白等10项指标在肌少症组与非肌少症组间比较差异均有统计学意义（均P<0.05）。见表1。
2.2　变量筛选
多因素Logistic回归分析结果显示，性别、透析龄、糖尿病、血清前白蛋白、25-羟基维生素D、白细胞介素-6是肌少症发生的影响因素（均P<0.05）；LASSO回归筛选结果与其一致，确定为6项核心预测因子，见表2。
为使模型更精简并应对多重共线性，本研究进一步采用LASSO回归进行特征筛选（见图1）。最终LASSO回归筛选出的变量与多因素Logistic回归结果完全一致，均为性别、透析龄、血清前白蛋白、糖尿病、白细胞介素-6和25-羟基维生素D。这6项变量被确认为构建预测模型的核心预测因子。
2.3　腹膜透析病人肌少症预测模型建立与评价
2.3.1　模型构建与性能比较
为系统评估不同机器学习算法在预测任务中的表现，本研究基于训练集构建8种预测模型，并在独立测试集上对其性能进行比较。性能评估结果（见表3）表明，支持向量机在本研究中表现最优，其AUC值达到0.958，同时灵敏度（0.992）、召回率（0.992）和F1分数（0.944）均表现优异，展现出卓越的综合预测能力。
LightGBM（AUC=0.948）和神经网络（AUC=0.945）表现次之，而LightGBM在保持高AUC的同时，各项指标均衡且稳定。XGBoost模型（AUC=0.940）灵敏度与召回率表现良好，但准确率相对较低。在其余模型中，朴素贝叶斯（AUC=0.906）与K近邻（AUC=0.867）处于中等水平，随机森林（AUC=0.833）与CatBoost（AUC=0.747）的多项指标相对较低，提示其在当前预测任务中的适用性有限。综上所述，基于AUC最高、关键指标表现优异且结果具有统计学意义这3个关键因素，支持向量机被确定为最佳预测模型。
2.3.2　外部验证
为严格评估最优模型（支持向量机）的泛化能力与临床适用性，本研究从独立医疗机构纳入198例腹膜透析病人构成外部验证队列。两组基线资料均衡性比较显示，年龄、性别构成、透析龄、糖尿病患病及肌少症发生率等核心指标差异均无统计学意义（均P>0.05），具有可比性。见表4。
将基于训练集构建完成的支持向量机模型直接应用于外部验证集，不做任何参数调整与再训练。结果显示，模型在外部验证集上仍保持优异的预测性能：AUC为0.968，准确率为0.978，灵敏度为0.930，特异度为0.882，与内部测试集性能（AUC=0.958）高度一致，提示模型稳健性强、泛化性好，可适用于不同中心、不同人群的临床场景。外部验证ROC曲线见图2。
2.3.3　模型解释与评价
为提升模型透明度与临床可解释性，本研究采用SHAP对最优模型进行可视化解析，明确各特征贡献度与作用方向。结果显示，6项预测因子的重要性依次为性别、糖尿病、血清前白蛋白、白细胞介素-6、透析龄、25-羟基维生素D，与多因素Logistic回归及LASSO回归筛选结果高度一致，进一步验证核心危险因素的可靠性（见图3）。SHAP结果直观揭示，性别为第一重要特征，男性肌少症风险高于女性，与男性肌肉衰减更快、蛋白质合成不足、尿毒症状态对肌肉抑制更显著有关。血清前白蛋白与肌少症风险呈负相关，即营养状态越差，风险越高；透析龄与肌少症风险呈正相关，即暴露时间越长，风险越高，各因子均呈现非线性关联（见图4）。上述结果不仅验证模型决策逻辑，更将“黑箱”模型转化为临床可理解、护理可落地的风险指标体系，为开展精准风险筛查、分层护理干预提供直观、量化的循证依据。
3　讨论
收起
3.1　研究思路与方法学优势
腹膜透析病人因蛋白质能量消耗、慢性微炎症、代谢紊乱及活动能力下降等多重因素影响，肌少症发生风险升高，且与跌倒、骨折、心血管事件及全因死亡密切相关，早期识别与干预是改善病人长期预后的关键。当前临床多采用传统Logistic回归构建预测模型，难以拟合多维度、非线性临床数据，且多数研究为单中心、小样本设计，缺乏外部验证与可解释性，难以满足临床推广需求。本研究基于多中心回顾性数据，采用8种机器学习算法构建腹膜透析病人肌少症风险预测模型，经内部验证与独立外部队列验证，并结合SHAP实现模型可解释，为临床精准筛查与护理干预提供循证依据。
本研究通过单因素分析、Logistic回归与LASSO回归两步筛选，最终确定性别、透析龄、糖尿病、血清前白蛋白、25-羟基维生素D、白细胞介素-6共6项核心预测因子。在8种机器学习模型中，支持向量机预测效能最优，内部测试集AUC为0.958，独立外部验证集AUC达0.968，准确率、灵敏度、特异度均高于0.9，模型判别能力、稳健性与泛化性优异。SHAP分析显示，预测因子重要性依次为性别、糖尿病、血清前白蛋白、白细胞介素-6、透析龄、25-羟基维生素D，且各因子与肌少症风险呈非线性关联，清晰揭示模型决策逻辑，有效解决机器学习“黑箱”问题，更符合临床应用需求。
本研究在方法学上具备明显优势：一是采用多算法横向比较，避免单一算法偏倚，提升模型选择科学性；二是设置独立外部验证队列，严格检验模型泛化能力，结果更可靠；三是引入SHAP可解释框架，将复杂模型转化为临床可理解的风险贡献度，兼顾预测精度与临床实用性。整体研究遵循TRIPOD规范，为腹膜透析相关肌少症预测提供了可复制、可推广的方法学范式，也为护理领域开展智能化风险评估提供参考。
3.2　腹膜透析病人肌少症发生影响因素与临床护理意义
本研究显示腹膜透析病人肌少症发生率为41.04%，与国内外相关研究结果[15-17]一致，提示研究队列具有良好人群代表性，也印证了肌少症已成为腹膜透析病人不可忽视的重要并发症。从病理生理机制看，本研究筛选的核心预测因子均有明确临床意义。男性肌少症风险更高，与雄激素水平下降、肌肉合成代谢减弱密切相关，尿毒症状态会进一步放大这一效应[18-19]。糖尿病通过胰岛素抵抗、晚期糖基化终末产物蓄积及微血管病变，多重途径加速肌肉量流失，是仅次于性别的重要预测因素[20]。血清前白蛋白降低直接反映蛋白质能量消耗，是肌少症发生的核心营养驱动因素[21]。白细胞介素-6介导慢性炎症，激活泛素-蛋白酶体系统，持续促进肌肉蛋白降解[22]。透析龄延长提示尿毒症毒素累积与代谢紊乱加重，风险随暴露时间增加而升高[23]。25-羟基维生素D参与肌细胞增殖、分化与收缩功能调节，其缺乏会直接损害肌肉质量与力量[24-25]。
与既往研究相比，本研究不仅验证了危险因素的合理性，更通过SHAP量化各因素的贡献大小与作用方向，使风险评估从“定性”走向“定量”，为临床护理提供可操作、可量化、可排序的干预依据。这一突破使护理人员能够快速识别高危对象、优先分配护理资源、精准实施干预措施，真正实现从“经验护理”向“精准护理”的转变。
3.3　研究局限性
本研究虽经多中心数据建模及外部验证，初步证实模型具有较好的预测性能与稳定性，但仍存在一定局限性。首先，在统计学稳健性方面，多因素Logistic回归中部分变量（如性别、糖尿病）的OR值偏高且95%CI较宽，提示效应估计精度有限，可能与数据分布极化、亚组样本量不均衡及变量间潜在交互作用有关。与之相关的是，单因素分析中肌少症组糖尿病比例略低于非肌少症组，与既往认知相悖，而经多因素校正年龄、性别、透析龄等混杂因素后，糖尿病又转为风险因素。这一方向性偏移不支持未校正混杂因素完全排除的可能性，反映出本研究中该关联具有一定混杂敏感性；尽管校正后结果提示糖尿病可能为腹膜透析病人肌少症的独立危险因素，但其独立效应仍需更大样本、更均衡的队列研究加以验证。其次，本研究数据仅来源于安徽省2所三级甲等医院，地域及人群代表性有限，且外部验证集与训练集在部分营养及炎症指标水平上存在一定数值差异，可能对模型泛化能力产生轻度影响，后续需进一步扩大多中心样本以提高模型稳定性和适用性。第三，本研究未纳入体力活动、膳食结构、社会经济状况及心理行为等重要生活方式与社会因素，可能对肌少症发生风险的综合评估存在一定欠缺。最后，受限于回顾性横断面设计，本研究无法确立变量间的因果关系，亦不能动态反映肌少症的发展轨迹，未来有待前瞻性队列研究进一步验证和完善。
4　小结
收起
本研究构建的支持向量机腹膜透析病人肌少症风险预测模型，具有高精度、高稳健性、高可解释性与良好泛化能力，基于6项临床易获取指标即可快速筛查高危人群，可直接用于临床护理实践，真正解决临床“缺工具、难筛查、干预盲”的现实问题。
在护理实践层面，本模型可直接用于：1）入院/门诊快速风险分层，对高风险病人优先启动肌少症评估；2）精准护理干预，对男性、长透析龄、糖尿病、营养差、炎症水平高的病人，强化营养支持、维生素D补充、抗阻运动训练、血糖管控与定期监测；3）延续性护理与随访，建立“筛查-评估-干预-随访”闭环管理模式；4）护理科研与质量改进，为护理结局监测、专科质量评价提供客观指标。
未来可从3个方面优化完善：一是扩大多中心研究范围，纳入不同地区、不同级别医院病人，增加样本量以提升结果稳健性与模型普适性；二是补充体力活动、饮食、心理等维度指标，进一步提升模型预测精度与临床实用性；三是开展前瞻性队列研究，构建动态风险预测模型，实现肌少症早期预警与长期追踪。同时，可推动模型与电子健康记录系统融合，实现高危病人自动识别与实时提醒，最终构建“风险预测-分层筛查-精准干预”一体化管理路径，为改善腹膜透析病人躯体功能、生活质量与长期预后提供可靠支撑。

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2026年第40卷第13期
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doi: 10.12102/j.issn.1009-6493.2026.13.011
  • 接收时间：2026-03-17
  • 首发时间：2026-10-30
  • 出版时间：2026-07-10
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  • 收稿日期：2026-03-17
  • 修回日期：2026-05-15
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    1安徽医科大学第一附属医院（肾脏内科），安徽 230022

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LI Jin, E-mail:
李锦，副主任护师，本科，E-mail:
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