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Analysis of influencing factors of social frailty in elderly patients on maintenance hemodialysis and construction of a risk prediction model
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Xin WANG1, Xiao-hong WANG2, Jia-qin QI2, Yan ZHANG3, Yuan SHEN4, Hong-xia DU2, 1
Modern Preventive Medicine | 2025, 52(11) : 1957 - 1962
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Modern Preventive Medicine | 2025, 52(11): 1957-1962
Epidemiology and Statistical Methods
Analysis of influencing factors of social frailty in elderly patients on maintenance hemodialysis and construction of a risk prediction model
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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
Published: 2025-06-10 doi: 10.20043/j.cnki.MPM.202406026
Outline
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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
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
Year 2025 volume 52 Issue 11
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Article Info
doi: 10.20043/j.cnki.MPM.202406026
  • Receive Date:2024-06-02
  • Online Date:2026-03-18
  • Published:2025-06-10
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  • Received:2024-06-02
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    School of Nursing, Shandong First Medical University (Shandong Academy of Medical Sciences), Jinan, Shandong 250117,China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
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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