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Article(id=1241034445931737562, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241034441380917539, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202406026, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1717257600000, receivedDateStr=2024-06-02, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773815269215, onlineDateStr=2026-03-18, pubDate=1749484800000, pubDateStr=2025-06-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773815269215, onlineIssueDateStr=2026-03-18, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773815269215, creator=13701087609, updateTime=1773815269215, updator=13701087609, issue=Issue{id=1241034441380917539, tenantId=1146029695717560320, journalId=1227665162245664772, year='2025', volume='52', issue='11', pageStart='1921', pageEnd='2112', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773815268130, creator=13701087609, updateTime=1773815340947, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241034746873049765, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241034441380917539, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241034746873049766, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241034441380917539, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=1957, endPage=1962, ext={EN=ArticleExt(id=1241034446225338870, articleId=1241034445931737562, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Analysis of influencing factors of social frailty in elderly patients on maintenance hemodialysis and construction of a risk prediction model, columnId=1240413921954295836, journalTitle=Modern Preventive Medicine, columnName=Epidemiology and Statistical Methods, runingTitle=null, highlight=null, articleAbstract=
Objective

To investigate the status and influencing factors of social frailty in elderly patients on Maintenance Hemodialysis (MHD) and to construct a nomogram prediction model.

Methods

A survey was conducted among 340 elderly MHD patients using a general information questionnaire, social frailty scale, hemodialysis self-management scale, Toronto Alexithymia Scale, Family Care Index, and Social Support Rating Scale. Independent influencing factors were identified, and a nomogram prediction model was constructed and evaluated.

Results

The prevalence of social frailty among elderly MHD patients was 28.82%. Binary logistic regression analysis revealed that a history of falls in the past year (OR=3.117, 95% CI: 1.249-7.778), self-care ability (OR=4.058, 95% CI: 2.002-8.228), alexithymia (OR=1.073, 95% CI: 1.009-1.141), family care level (OR=0.744, 95%CI: 0.612-0.903), and social support (OR=0.886, 95% CI: 0.801-0.981) were significant influencing factors for social frailty in elderly MHD patients (all P<0.05). The area under the receiver operating characteristic curve for the constructed model was 0.921(95% CI: 0.893-0.950), with the maximum Youden index being 0.688, an optimal cutoff value of 0.330, sensitivity of 83.7%, and specificity of 85.1%, indicating high discrimination of the model. The Hosmer-Leeshawn test yielded χ2=10.465, P=0.234,suggesting good model fit. Internal validation calibration curves indicated satisfactory calibration of the model. Decision curve analysis demonstrated high clinical utility of the model.

Conclusion

The nomogram prediction model developed in this study is a practical and convenient tool that aids in predicting the risk of social frailty in elderly MHD patients.

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目的

探究老年维持性血液透析(maintenance hemodialysis, MHD)患者社会衰弱现状及影响因素并构建列线图预测模型。

方法

采用一般资料调查表、社会衰弱量表、血液透析自我管理量表、多伦多述情障碍量表、家庭关怀度指数问卷、社会支持评定量表对340名老年MHD患者进行调查，筛选独立影响因素，构建并评估列线图预测模型。

结果

老年MHD患者社会衰弱患病率为28.82%。二分类logistic回归分析结果显示，近一年跌倒史(OR=3.117, 95%CI:1.249～7.778)、自理能力(OR=4.058, 95%CI:2.002～8.228)、述情障碍(OR=1.073, 95%CI:1.009～1.141)、家庭关怀度(OR=0.744,95%CI:0.612～0.903)及社会支持(OR=0.886, 95%CI:0.801～0.981)是老年MHD患者发生社会衰弱的影响因素(均P<0.05)。所构建模型的受试者工作特征曲线下面积为0.921(95%CI:0.893～0.950)，约登指数最大值为0.688，最佳截断值为0.330，灵敏度为83.7%，特异度为85.1%，说明模型区分度较高。Hosmer-Lemeshow检验结果为χ2=10.465，P=0.234，说明模型拟合度较好。内部验证校准曲线示模型校准度较好。决策曲线分析示模型的临床实用性较高。

结论

本研究构建的列线图预测模型是一种实用、方便的工具，有助于预测老年MHD患者社会衰弱的发生风险。

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杜红霞，E-mail:
, copyrightStatement=本刊刊出的所有文章不代表中华预防医学会和本刊编委会的观点，除非特别声明。, copyrightOwner=中华预防医学会和四川大学华西公共卫生学院, extLink=null, articleAbsUrl=null, sourceXml=IVnFxy9ZfgC0ljbw+bnYdg==, magXml=OGsxwleDjywZaSUJnUpL8Q==, pdfUrl=null, pdf=mVKdm6lZtPZ4ajd2XCbxGQ==, pdfFileSize=954449, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=1PNEk3P1iXGM4x+IEuVCgA==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=r3V8ON3Re4b4uwGlPiN4wA==, mapNumber=null, authorCompany=null, fund=null, authors=

王鑫（1998—），女，硕士在读，研究方向：老年护理

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王鑫（1998—），女，硕士在读，研究方向：老年护理

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王鑫（1998—），女，硕士在读，研究方向：老年护理

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BMC Psychology, 2023, 11(1): 130., articleTitle=Mediating effect of anxiety and depression between family function and hope in patients receiving maintenance hemodialysis: a cross-sectional study, refAbstract=null), Reference(id=1241050850169254146, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241034445931737562, doi=null, pmid=null, pmcid=null, year=2025, volume=28, issue=2, pageStart=220, pageEnd=227, url=null, language=null, rfNumber=[26], rfOrder=36, authorNames=卢静, 孙国珍, 王洁, journalName=中国全科医学, refType=null, unstructuredReference=卢静，孙国珍，王洁，等.慢性心力衰竭患者社会衰弱现状及其影响因素可解释性分析研究[J].中国全科医学，2025，28（2）：220-227., articleTitle=慢性心力衰竭患者社会衰弱现状及其影响因素可解释性分析研究, refAbstract=null), Reference(id=1241050850269917442, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241034445931737562, doi=null, pmid=null, pmcid=null, year=2025, volume=28, issue=2, pageStart=220, pageEnd=227, url=null, language=null, rfNumber=[26], rfOrder=37, authorNames=Lu J, Sun GZ, Wang J, journalName=Chinese General Medicine, refType=null, unstructuredReference=Lu J, Sun GZ, Wang J, et al. 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BMC Pulmonary Medicine, 2024, 24(1): 390., articleTitle=Social support mediates social frailty with anxiety and depression, refAbstract=null)], funds=[Fund(id=1241050842745335883, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241034445931737562, awardId=2024105001, language=CN, fundingSource=济南市卫生健康委员会科技发展计划项目(2024105001), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1241050832423154278, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241034445931737562, xref=1., ext=[AuthorCompanyExt(id=1241050832431542887, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241034445931737562, companyId=1241050832423154278, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=School of Nursing, Shandong First Medical University (Shandong Academy of Medical Sciences), Jinan, Shandong 250117,China), AuthorCompanyExt(id=1241050832435737193, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241034445931737562, companyId=1241050832423154278, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.山东第一医科大学(山东省医学科学院)护理学院，山东 济南 250117)]), AuthorCompany(id=1241050832590926453, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241034445931737562, xref=2., ext=[AuthorCompanyExt(id=1241050832603509368, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241034445931737562, companyId=1241050832590926453, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.山东第一医科大学附属中心医院护理部，山东 济南 250013)]), AuthorCompany(id=1241050832834196104, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241034445931737562, xref=3., ext=[AuthorCompanyExt(id=1241050832838390408, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241034445931737562, companyId=1241050832834196104, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=3.山东第一医科大学附属中心医院肾脏病/血液净化科)]), AuthorCompany(id=1241050832993579669, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241034445931737562, xref=4., ext=[AuthorCompanyExt(id=1241050833010356889, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241034445931737562, companyId=1241050832993579669, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=4.潍坊市人民医院急诊科)])], figs=[ArticleFig(id=1241050839742215118, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241034445931737562, language=EN, label=Figure 1, caption=Social frailty nomograph prediction model for elderly MHD patients, figureFileSmall=LEyREAWkuwOTCYtIqP7HIg==, figureFileBig=XY+LyLfwcXjoFrDruomWVg==, tableContent=null), ArticleFig(id=1241050840237143000, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241034445931737562, language=CN, label=图1, caption=老年MHD患者社会衰弱列线图预测模型, figureFileSmall=LEyREAWkuwOTCYtIqP7HIg==, figureFileBig=XY+LyLfwcXjoFrDruomWVg==, tableContent=null), ArticleFig(id=1241050840425886691, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241034445931737562, language=EN, label=Figure 2, caption=ROC curve of social frailty prediction model in elderly MHD patients, figureFileSmall=HF9DvB50OTVs6+EpS5PjDQ==, figureFileBig=479pxivtbAxaF3IFnKCvAA==, tableContent=null), ArticleFig(id=1241050840522355692, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241034445931737562, language=CN, label=图2, caption=老年MHD患者社会衰弱预测模型的ROC曲线, figureFileSmall=HF9DvB50OTVs6+EpS5PjDQ==, figureFileBig=479pxivtbAxaF3IFnKCvAA==, tableContent=null), ArticleFig(id=1241050840761431026, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241034445931737562, language=EN, label=Figure 3, caption=Calibration curve for internal verification, figureFileSmall=8kIeWaVly78PnrwlOOQ6YQ==, figureFileBig=ilTxV9jl+gIGm/1GGBKfSQ==, tableContent=null), ArticleFig(id=1241050840920814590, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241034445931737562, language=CN, label=图3, caption=内部验证校准曲线

注：B=1 000 repetitions, boot mean absolute error=0.017，n=340。

, figureFileSmall=8kIeWaVly78PnrwlOOQ6YQ==, figureFileBig=ilTxV9jl+gIGm/1GGBKfSQ==, tableContent=null), ArticleFig(id=1241050841113751555, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241034445931737562, language=EN, label=Figure 4, caption=DCA curve of social frailty prediction model in elderly MHD patients, figureFileSmall=gn/wX95ttpvcieIqXkbToA==, figureFileBig=iJXlwZ8yM4H3MLTFDgRqGw==, tableContent=null), ArticleFig(id=1241050841252163596, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241034445931737562, language=CN, label=图4, caption=老年MHD患者社会衰弱预测模型的DCA曲线, figureFileSmall=gn/wX95ttpvcieIqXkbToA==, figureFileBig=iJXlwZ8yM4H3MLTFDgRqGw==, tableContent=null), ArticleFig(id=1241050841440907287, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241034445931737562, language=EN, label=Table 1, caption=

Single factor analysis of social frailty in elderly MHD patients ［n=340，n（%），M（P25，P75）］

, figureFileSmall=null, figureFileBig=null, tableContent=
项目无社会衰弱组
(n=242)		社会衰弱组(n
=98)		χ2/Z值P值
性别0.3950.530
男135(72.58)51(27.42)
女107(69.48)47(30.52)
文化程度25.199<0.001
小学及以下38(50.00)38(50.00)
初中95(73.64)34(26.36)
高中或中专69(76.67)21(23.33)
大专及以上40(88.89)5(11.11)
离退休前职业13.9780.007
企事业单位职员99(77.95)28(22.05)
农民42(57.53)31(42.47)
工人85(74.56)29(25.44)
个体8(80.00)2(20.00)
其他8(50.00)8(50.00)
婚姻状况12.784<0.001
已婚232(73.65)83(26.35)
未婚/离婚/丧偶10(40.00)15(60.00)
居住地8.0600.018
城市162(75.00)54(25.00)
城镇43(72.88)16(27.12)
农村37(56.92)28(43.08)
子女数(个)2.3520.309
≤1140(72.54)53(27.46)
273(66.36)37(33.64)
≥329(78.38)8(21.62)
家庭人均月收入(元)24.759<0.001
<1 00011(40.74)16(59.26)
1 000～3 00090(64.75)49(35.25)
3 001～5 00093(78.15)26(21.85)
>5 00048(87.27)7(12.73)
医疗费用支付方式10.6100.005
城镇职工医保173(76.89)52(23.11)
城镇居民医保26(59.09)18(40.91)
新农合43(60.56)28(39.44)
合并症数量(种)7.3980.025
≤171(77.17)21(22.83)
295(75.40)31(24.60)
≥376(62.30)46(37.70)
近一年跌倒史35.974<0.001
无210(78.95)56(21.05)
有32(43.24)42(56.76)
主观睡眠质量12.2580.002
好114(80.28)28(19.72)
一般67(69.79)29(30.21)
差61(59.80)41(40.20)
透析频率1.2960.523
3次/周221(71.99)86(28.01)
2次/周14(60.87)9(39.13)
5次/2周7(70.00)3(30.00)
原发疾病1.6240.805
慢性肾小球肾炎107(71.81)42(28.19)
糖尿病肾病72(68.57)33(31.43)
高血压肾损害36(70.59)15(29.41)
多囊肾18(81.82)4(18.18)
其他9(69.23)4(30.77)
用药数(种)0.7810.377
<5206(70.31)87(29.69)
≥536(76.60)11(23.40)
自理能力64.213<0.001
无需依赖192(84.58)35(15.42)
轻度依赖45(48.39)48(51.61)
中度依赖5(25.00)15(75.00)
年龄（岁）64.00(61.00,68.00)65.00(61.00,71.00)-1.616a0.106
透析龄（年）3.00(1.00 ,6.00 )3.00(1.00,6.00)-0.629a0.530
血液透析自我管理能力（分）55.00(49.00,61.00)41.50(35.00,48.25)-9.951a<0.001
述情障碍（分）47.00(42.00,53.00)62.00(57.00,68.00)-10.905a<0.001
家庭关怀度（分）9.00(6.00,10.00)4.00(3.00,6.00)-9.839a<0.001
社会支持（分）38.00(34.00,42.00)32.00(28.00,34.00)-9.699a<0.001
), ArticleFig(id=1241050841688371231, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241034445931737562, language=CN, label=表1, caption=

老年MHD患者社会衰弱影响因素的单因素分析［n=340，n（%），M（P25，P75）］

, figureFileSmall=null, figureFileBig=null, tableContent=
项目无社会衰弱组
(n=242)		社会衰弱组(n
=98)		χ2/Z值P值
性别0.3950.530
男135(72.58)51(27.42)
女107(69.48)47(30.52)
文化程度25.199<0.001
小学及以下38(50.00)38(50.00)
初中95(73.64)34(26.36)
高中或中专69(76.67)21(23.33)
大专及以上40(88.89)5(11.11)
离退休前职业13.9780.007
企事业单位职员99(77.95)28(22.05)
农民42(57.53)31(42.47)
工人85(74.56)29(25.44)
个体8(80.00)2(20.00)
其他8(50.00)8(50.00)
婚姻状况12.784<0.001
已婚232(73.65)83(26.35)
未婚/离婚/丧偶10(40.00)15(60.00)
居住地8.0600.018
城市162(75.00)54(25.00)
城镇43(72.88)16(27.12)
农村37(56.92)28(43.08)
子女数(个)2.3520.309
≤1140(72.54)53(27.46)
273(66.36)37(33.64)
≥329(78.38)8(21.62)
家庭人均月收入(元)24.759<0.001
<1 00011(40.74)16(59.26)
1 000～3 00090(64.75)49(35.25)
3 001～5 00093(78.15)26(21.85)
>5 00048(87.27)7(12.73)
医疗费用支付方式10.6100.005
城镇职工医保173(76.89)52(23.11)
城镇居民医保26(59.09)18(40.91)
新农合43(60.56)28(39.44)
合并症数量(种)7.3980.025
≤171(77.17)21(22.83)
295(75.40)31(24.60)
≥376(62.30)46(37.70)
近一年跌倒史35.974<0.001
无210(78.95)56(21.05)
有32(43.24)42(56.76)
主观睡眠质量12.2580.002
好114(80.28)28(19.72)
一般67(69.79)29(30.21)
差61(59.80)41(40.20)
透析频率1.2960.523
3次/周221(71.99)86(28.01)
2次/周14(60.87)9(39.13)
5次/2周7(70.00)3(30.00)
原发疾病1.6240.805
慢性肾小球肾炎107(71.81)42(28.19)
糖尿病肾病72(68.57)33(31.43)
高血压肾损害36(70.59)15(29.41)
多囊肾18(81.82)4(18.18)
其他9(69.23)4(30.77)
用药数(种)0.7810.377
<5206(70.31)87(29.69)
≥536(76.60)11(23.40)
自理能力64.213<0.001
无需依赖192(84.58)35(15.42)
轻度依赖45(48.39)48(51.61)
中度依赖5(25.00)15(75.00)
年龄（岁）64.00(61.00,68.00)65.00(61.00,71.00)-1.616a0.106
透析龄（年）3.00(1.00 ,6.00 )3.00(1.00,6.00)-0.629a0.530
血液透析自我管理能力（分）55.00(49.00,61.00)41.50(35.00,48.25)-9.951a<0.001
述情障碍（分）47.00(42.00,53.00)62.00(57.00,68.00)-10.905a<0.001
家庭关怀度（分）9.00(6.00,10.00)4.00(3.00,6.00)-9.839a<0.001
社会支持（分）38.00(34.00,42.00)32.00(28.00,34.00)-9.699a<0.001
), ArticleFig(id=1241050841906475046, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241034445931737562, language=EN, label=Table 2, caption=

Assignment of argument variables

, figureFileSmall=null, figureFileBig=null, tableContent=
变量赋值
文化程度小学及以下=1，初中=2，高中或中专=3，
大专及以上=4
离退休前职业
婚姻状况		企事业单位职员作参照设置哑变量；X1=
农民(1, 0, 0, 0)，X2=工人(0, 1, 0, 0)，X3=个
体(0, 0, 1, 0)，X4=其他(0, 0, 0, 1)
已婚=1，未婚/离婚/丧偶=2
居住地城市作参照设置哑变量；X1=城镇(1, 0)，X2
=农村(0, 1)
家庭人均月收入(元)<1 000=1，1 000～3 000=2，3 001～5000=
3，>5 000=4
医疗费用支付方式城镇职工医保作参照设置哑变量；X1=城镇
居民医保(1, 0)，X2=新农合(0, 1)
合并症数量(种)≤1=1，2=2，≥3=3
近一年跌倒史无=0，有=1
主观睡眠质量好=1，一般=2，差=3
自理能力无需依赖=1，轻度依赖=2，中度依赖=3
血液透析自我管理能力原值输入
述情障碍原值输入
家庭关怀度原值输入
社会支持原值输入
), ArticleFig(id=1241050842099413038, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241034445931737562, language=CN, label=表2, caption=

自变量赋值情况

, figureFileSmall=null, figureFileBig=null, tableContent=
变量赋值
文化程度小学及以下=1，初中=2，高中或中专=3，
大专及以上=4
离退休前职业
婚姻状况		企事业单位职员作参照设置哑变量；X1=
农民(1, 0, 0, 0)，X2=工人(0, 1, 0, 0)，X3=个
体(0, 0, 1, 0)，X4=其他(0, 0, 0, 1)
已婚=1，未婚/离婚/丧偶=2
居住地城市作参照设置哑变量；X1=城镇(1, 0)，X2
=农村(0, 1)
家庭人均月收入(元)<1 000=1，1 000～3 000=2，3 001～5000=
3，>5 000=4
医疗费用支付方式城镇职工医保作参照设置哑变量；X1=城镇
居民医保(1, 0)，X2=新农合(0, 1)
合并症数量(种)≤1=1，2=2，≥3=3
近一年跌倒史无=0，有=1
主观睡眠质量好=1，一般=2，差=3
自理能力无需依赖=1，轻度依赖=2，中度依赖=3
血液透析自我管理能力原值输入
述情障碍原值输入
家庭关怀度原值输入
社会支持原值输入
), ArticleFig(id=1241050842216853555, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241034445931737562, language=EN, label=Table 3, caption=

Binary Logistic regression analysis of influencing factors with social frailty in elderly MHD patients

, figureFileSmall=null, figureFileBig=null, tableContent=
变量β Waldχ2值P值OR值(95%CI)
近一年跌倒史：有(以无为参照)1.1370.4675.9380.0153.117(1.249～7.778)
自理能力1.4010.36115.089<0.0014.058(2.002～8.228)
述情障碍0.0710.0315.0810.0241.073(1.009～1.141)
家庭关怀度-0.2960.0998.9090.0030.744(0.612～0.903)
社会支持-0.1210.0525.4720.0190.886(0.801～0.981)
), ArticleFig(id=1241050842355265590, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241034445931737562, language=CN, label=表3, caption=

老年MHD患者社会衰弱影响因素的二分类logistic回归分析

, figureFileSmall=null, figureFileBig=null, tableContent=
变量β Waldχ2值P值OR值(95%CI)
近一年跌倒史：有(以无为参照)1.1370.4675.9380.0153.117(1.249～7.778)
自理能力1.4010.36115.089<0.0014.058(2.002～8.228)
述情障碍0.0710.0315.0810.0241.073(1.009～1.141)
家庭关怀度-0.2960.0998.9090.0030.744(0.612～0.903)
社会支持-0.1210.0525.4720.0190.886(0.801～0.981)
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老年维持性血液透析患者社会衰弱影响因素分析及风险预测模型的构建
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王鑫 1 , 王晓虹 2 , 齐加芹 2 , 张艳 3 , 沈源 4 , 杜红霞 2, 1
现代预防医学 | 流行病与统计方法 2025,52(11): 1957-1962
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现代预防医学 | 流行病与统计方法 2025, 52(11): 1957-1962
老年维持性血液透析患者社会衰弱影响因素分析及风险预测模型的构建
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王鑫1, 王晓虹2, 齐加芹2, 张艳3, 沈源4, 杜红霞2, 1
作者信息
  • 1.山东第一医科大学(山东省医学科学院)护理学院，山东 济南 250117
  • 2.山东第一医科大学附属中心医院护理部，山东 济南 250013
  • 3.山东第一医科大学附属中心医院肾脏病/血液净化科
  • 4.潍坊市人民医院急诊科

    • 王鑫（1998—），女，硕士在读，研究方向：老年护理

通讯作者:

杜红霞，E-mail:
Analysis of influencing factors of social frailty in elderly patients on maintenance hemodialysis and construction of a risk prediction model
Xin WANG1, Xiao-hong WANG2, Jia-qin QI2, Yan ZHANG3, Yuan SHEN4, Hong-xia DU2, 1
Affiliations
  • School of Nursing, Shandong First Medical University (Shandong Academy of Medical Sciences), Jinan, Shandong 250117,China
出版时间: 2025-06-10 doi: 10.20043/j.cnki.MPM.202406026
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摘要
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目的

探究老年维持性血液透析(maintenance hemodialysis, MHD)患者社会衰弱现状及影响因素并构建列线图预测模型。

方法

采用一般资料调查表、社会衰弱量表、血液透析自我管理量表、多伦多述情障碍量表、家庭关怀度指数问卷、社会支持评定量表对340名老年MHD患者进行调查，筛选独立影响因素，构建并评估列线图预测模型。

结果

老年MHD患者社会衰弱患病率为28.82%。二分类logistic回归分析结果显示，近一年跌倒史(OR=3.117, 95%CI:1.249～7.778)、自理能力(OR=4.058, 95%CI:2.002～8.228)、述情障碍(OR=1.073, 95%CI:1.009～1.141)、家庭关怀度(OR=0.744,95%CI:0.612～0.903)及社会支持(OR=0.886, 95%CI:0.801～0.981)是老年MHD患者发生社会衰弱的影响因素(均P<0.05)。所构建模型的受试者工作特征曲线下面积为0.921(95%CI:0.893～0.950)，约登指数最大值为0.688，最佳截断值为0.330，灵敏度为83.7%，特异度为85.1%，说明模型区分度较高。Hosmer-Lemeshow检验结果为χ2=10.465，P=0.234，说明模型拟合度较好。内部验证校准曲线示模型校准度较好。决策曲线分析示模型的临床实用性较高。

结论

本研究构建的列线图预测模型是一种实用、方便的工具，有助于预测老年MHD患者社会衰弱的发生风险。

老年人  /  维持性血液透析  /  社会衰弱  /  列线图预测模型
Objective

To investigate the status and influencing factors of social frailty in elderly patients on Maintenance Hemodialysis (MHD) and to construct a nomogram prediction model.

Methods

A survey was conducted among 340 elderly MHD patients using a general information questionnaire, social frailty scale, hemodialysis self-management scale, Toronto Alexithymia Scale, Family Care Index, and Social Support Rating Scale. Independent influencing factors were identified, and a nomogram prediction model was constructed and evaluated.

Results

The prevalence of social frailty among elderly MHD patients was 28.82%. Binary logistic regression analysis revealed that a history of falls in the past year (OR=3.117, 95% CI: 1.249-7.778), self-care ability (OR=4.058, 95% CI: 2.002-8.228), alexithymia (OR=1.073, 95% CI: 1.009-1.141), family care level (OR=0.744, 95%CI: 0.612-0.903), and social support (OR=0.886, 95% CI: 0.801-0.981) were significant influencing factors for social frailty in elderly MHD patients (all P<0.05). The area under the receiver operating characteristic curve for the constructed model was 0.921(95% CI: 0.893-0.950), with the maximum Youden index being 0.688, an optimal cutoff value of 0.330, sensitivity of 83.7%, and specificity of 85.1%, indicating high discrimination of the model. The Hosmer-Leeshawn test yielded χ2=10.465, P=0.234,suggesting good model fit. Internal validation calibration curves indicated satisfactory calibration of the model. Decision curve analysis demonstrated high clinical utility of the model.

Conclusion

The nomogram prediction model developed in this study is a practical and convenient tool that aids in predicting the risk of social frailty in elderly MHD patients.

Elderly  /  Maintenance hemodialysis  /  Social frailty  /  Nomogram prediction model
王鑫, 王晓虹, 齐加芹, 张艳, 沈源, 杜红霞. 老年维持性血液透析患者社会衰弱影响因素分析及风险预测模型的构建. 现代预防医学, 2025 , 52 (11) : 1957 -1962 . DOI: 10.20043/j.cnki.MPM.202406026
Xin WANG, Xiao-hong WANG, Jia-qin QI, Yan ZHANG, Yuan SHEN, Hong-xia DU. Analysis of influencing factors of social frailty in elderly patients on maintenance hemodialysis and construction of a risk prediction model[J]. Modern Preventive Medicine, 2025 , 52 (11) : 1957 -1962 . DOI: 10.20043/j.cnki.MPM.202406026
正文
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随着全球老龄化加速，老年维持性血液透析(maintenance hemodialysis, MHD)患者群体显著扩大[1]，成为公共卫生关注焦点。MHD虽能延长终末期肾脏病患者生命，但也会严重影响其生活质量，加速社会功能退化[2]。社会衰弱作为衰弱的重要维度，是指个体处于失去或已经失去对于满足一生中一种或多种基本社会需求至关重要的社会资源、行为、活动及自我管理能力的风险状态，与认知障碍、抑郁、残疾、死亡等不良结局紧密相关[3]。国外研究显示[4]，老年MHD患者社会衰弱问题尤为突出，且常与其他类型衰弱并存。然而，国内对此领域的研究有限，且缺乏有效的社会衰弱风险评估工具。列线图作为整合多因素风险的可视化工具，能够量化个体化预测概率，便于临床应用[5]。研究发现[5]，社会衰弱受身体、心理、家庭及社会等多因素影响，因此本研究除收集社会人口学特征外，还纳入血液透析自我管理能力、述情障碍、家庭关怀度及社会支持等变量，旨在从个体心理行为、家庭功能及社会资源等多维视角剖析老年MHD患者社会衰弱的影响因素，进而构建风险预测模型，以助力医务人员早期识别高危个体并实施针对性干预策略。
1　对象与方法
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1.1　研究对象
采用便利抽样法，于2023年9月—2024年2月在济南市某三甲医院血液透析室进行调查。纳入标准：（1）年龄≥60岁；（2）根据《慢性肾脏病早期筛查、诊断及防治指南(2022年版)》进行血液透析治疗[6]；（3）规律性透析治疗≥3个月；（4）言语表达清楚；（5）自愿参与本研究。排除标准：（1）合并活动性恶性肿瘤；（2）存在听力、视力障碍；（3）合并严重精神疾病或认知功能障碍；（4）生活完全不能自理者(Barthel指数评分≤40分[7])。本研究采用10EPV(events per variable)法[8]估算样本量，即在构建最终预测模型时每纳入1个预测因子至少需要10个阳性结局事件样本。样本量=EPV值×预测因子数量÷患病率÷(1-无效率)，本研究预计纳入5～7个预测因子，根据预调查测得50例老年MHD患者社会衰弱的患病率为38%，考虑20%无效率，经计算所需最小样本量为10×(5～7)÷38%÷(1-20%)≈164～230例。本研究最终纳入样本量为340例。本研究已通过山东第一医科大学附属中心医院科研伦理审查(R20231226004)。
1.2　研究工具
（1）一般资料调查表，包括年龄、性别、文化程度、自理能力(采用Barthel指数评价，100分为无需依赖、61～99分为轻度依赖、41～60分为中度依赖、0～40分为重度依赖[7])等。（2）社会衰弱量表(help participation loneliness financial talk scale,HALFT)，由Ma等[9]开发，总分为0～5分，≥3分界定为社会衰弱[9]。本研究中测得Cronbach α系数为0.706。（3）血液透析自我管理量表(hemodialysis self-management instrument, HD-SMI)，由李慧等[10]引进，总分20～80分，得分越高则自我管理水平越好[10]。本研究中测得Cronbach α系数为0.947。（4）多伦多述情障碍量表(Toronto alexithymia scale, TAS-20)，由蚁金瑶等[11]汉化，总分20～100分，得分越高则述情障碍越严重[11]。本研究中测得Cronbach α系数为0.871。（5）家庭关怀度指数问卷(family APGAR index,APGAR)，由吕繁等[12]引入我国，总分0～10分，得分越高则家庭功能越好[12]。本研究中测得Cronbach α系数为0.914。（6）社会支持评定量表(social support rating scale, SSRS)，由肖水源[13]编制，总分12～66分，得分越高则社会支持水平越好[13]。本研究中测得Cronbach α系数为0.813。
1.3　资料收集及质量控制方法
采用面对面访谈法收集资料。调查前对两名研究者进行统一培训与考核，合格后方可参与数据收集。征得患者或其法定代理人签署知情同意书后，于血液透析期间开展问卷调查，由研究者逐项复述并代为填写，现场核对后回收。数据由两名研究者分别录入并交叉核对，确保准确性。共发放问卷342份，剔除无效问卷2份，回收有效问卷340份，有效回收率为99.42%。
1.4　统计学方法
采用SPSS 26.0进行统计分析，组间比较采取Mann-Whitney U检验、χ2检验，对单因素分析中有统计学意义的变量进行共线性诊断，方差膨胀因子(variance inflation factor, VIF)<5为无多重共线性；采用二分类logistic回归分析筛选独立影响因素。使用R(4.3.3)构建列线图预测模型。绘制受试者工作特征(receiver operating characteristic, ROC)曲线，其中曲线下面积(area under curve, AUC)>0.75表明模型区分能力较好；Hosmer-Lemeshow检验P>0.05说明模型拟合度较好；内部验证采用bootstrap重抽样法并绘制校准曲线以评估模型校准度，平均绝对误差(mean absolute error, MAE)在0.05以内为可接受；决策曲线分析(decision curve analysis, DCA)评价模型的临床效能。检验水准α=0.05。
2　结果
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2.1　老年MHD患者社会衰弱影响因素的单因素分析
340名老年MHD患者中无社会衰弱组242人，社会衰弱组98人，社会衰弱患病率为28.82%。单因素分析结果显示，两组在文化程度、离退休前职业、婚姻状况、居住地、家庭人均月收入、医疗费用支付方式、合并症数量、近一年跌倒史、主观睡眠质量、自理能力、血液透析自我管理能力、述情障碍、家庭关怀度、社会支持之间差异具有统计学意义(P<0.05)。见表1。
2.2　老年MHD患者社会衰弱影响因素的二分类logistic回归分析
将社会衰弱作为因变量(否=0，是=1)，纳入单因素分析中14项有意义的因素作为自变量(即P<0.05)，赋值见表2。各变量VIF值为1.130～3.684，表示无多重共线性。二分类logistic回归分析结果显示，近一年跌倒史、自理能力、述情障碍、家庭关怀度及社会支持是老年MHD患者发生社会衰弱的独立影响因素，见表3。
2.3　老年MHD患者社会衰弱风险预测模型的建立
基于老年MHD患者社会衰弱的影响因素构建并绘制列线图预测模型。图1中5个预测因子都对应一定分数，将各因子值上投至第一行获得相应分值，累加得总分后向下做垂线，即可估算个体社会衰弱风险。
2.4　老年MHD患者社会衰弱预测模型的评价与验证
ROC曲线显示，AUC为0.921(95%CI: 0.893～0.950)，约登指数最大值为0.688，最佳截断值为0.330，灵敏度为83.7%，特异度为85.1%，说明模型区分能力较好，见图2。Hosmer-Lemeshow检验χ2=10.465, P=0.234，说明模型拟合度较好。内部验证(bootstrap重抽样1 000次)校准曲线显示，MAE为0.017，预测曲线与校准曲线拟合良好且接近理想曲线，说明模型校准度较高，见图3。DCA曲线显示，在0～1.0风险阈值范围内，模型净获益高于“全干预”与“全不干预”，表明该模型具有较高临床实用价值，见图4。
3　讨论
收起
3.1　老年MHD患者社会衰弱发生现状
本研究显示，老年MHD患者社会衰弱患病率为28.82%，显著低于日本同类研究结果[4] (57.3%)。可能与评估工具使用、研究对象年龄构成及国情、社会保障制度、生活环境、文化观念等存在差异有关[14]。本研究结果高于社区老年人社会衰弱患病率[3](20.0%)。可能是因为老年MHD患者因毒素积累和代谢紊乱，易出现炎症、肌肉萎缩和认知障碍等，加之透析相关低血压、疲劳、营养流失等症状，导致活动能力下降，此外，长期透析还加重经济负担，使生活方式改变[2]，因而较易发生社会衰弱。
3.2　老年MHD患者社会衰弱影响因素分析
本研究发现，近一年有跌倒史的患者社会衰弱发生风险更高。与Sawa等[15]研究一致。可能是因为跌倒不仅造成身体损伤，还易引发恐惧、焦虑及信心丧失，导致患者减少社会活动[16]。即使身体功能恢复，心理障碍仍可能导致持续的社交退缩。为避免跌倒，患者常采取久坐少动的生活方式，从而可能出现功能退化、肌力降低和依赖性增加，提升社会衰弱风险[17]。因此，医护人员应积极引导患者加强功能锻炼，及时疏导负面情绪，以预防跌倒的发生。
本研究发现，自理能力越差的患者社会衰弱发生风险越高，与吴和梅等[18]研究一致。可能是因为自理能力减弱常伴随躯体功能和独立性退化，增加户外活动风险，患者为规避潜在危险，往往减少社交活动[19]，导致社会衰弱。Zhang等[20]研究也指出，良好的自理能力有助于延缓社会参与水平的下降。因此，医护人员应为透析患者制定康复计划，并根据实际情况适时调整方案，提高患者自理水平。
本研究发现，述情障碍水平越高的患者社会衰弱发生风险越高，与Li等[21]研究一致。可能是因为高述情障碍患者因情感识别和表达受限，负性情绪难以释放，易导致社会互动能力下降，甚至拒绝参与社交活动[22]。研究发现[23]，述情障碍患者常表现为疏离、冷漠，人际亲密感下降，社交焦虑加重，社会功能受损。老年MHD患者常因多重压力诱发情绪困扰并发展为述情障碍。医务人员应关注患者情绪变化，采取措施引导其有效表达情绪，改善患者心理状况。
本研究发现，家庭关怀度越好的患者社会衰弱发生风险越低。与杨青建等[24]研究一致。高家庭功能水平的患者通常获得更多关爱与精神支持，有助于改善心理状态，增强生存信念，从而降低社会衰弱风险。本研究社会衰弱组家庭关怀度均分为4(3,6)分，低于鲜圆圆等[5]的研究结果。可能是因为疾病影响促使患者家庭角色变化，增加心理压力，阻碍情感交流，同时，长期透析依赖限制患者自理能力，家庭照护者面临沉重的照护任务与经济负担，导致家庭亲密度下降[25]。医务人员应促进家庭成员间建立稳固支持体系，以提升患者希望水平，缓解家庭压力。
本研究发现，社会支持水平越高的患者社会衰弱发生风险越低。与卢静等[26]研究一致。充分的社会支持可为患者提供情感慰藉与力量，缓冲压力事件的负面冲击，增强其管理病情的信心及治疗依从性，提升主观幸福感与社会适应能力[19]。从患者视角来看，物质与情感援助有助于弥补因疾病带来的自我价值缺失，增强自我认同感，并通过双向互动建立支持网络[26]。Liu等[27]研究也指出，良好的社会支持能有效缓解社会衰弱患者的心理困扰，满足其基本社会需求，激励其积极参与社会生活。医务人员应帮助患者构建紧密的社会互助体系，利用科技手段促进信息流通与资源共享，完善患者社会支持系统。
综上所述，本研究构建了老年MHD患者社会衰弱列线图风险预测模型，经验证该模型具有良好的区分度、校准度及临床适用性，有助于医护人员早期识别高风险人群，为制定预防和干预措施提供科学依据。但本研究样本来源单一、缺乏外部验证，且为横断面设计。未来应开展多中心、大样本、前瞻性队列研究，以增强模型代表性与推广性。
基金
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  • 济南市卫生健康委员会科技发展计划项目(2024105001)
文献
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doi: 10.20043/j.cnki.MPM.202406026
  • 接收时间：2024-06-02
  • 首发时间：2026-03-18
  • 出版时间：2025-06-10
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  • 收稿日期：2024-06-02
基金
济南市卫生健康委员会科技发展计划项目(2024105001)
作者信息
    1.山东第一医科大学(山东省医学科学院)护理学院，山东 济南 250117
    2.山东第一医科大学附属中心医院护理部，山东 济南 250013
    3.山东第一医科大学附属中心医院肾脏病/血液净化科
    4.潍坊市人民医院急诊科

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小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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