Abstract: Objective To develop predictive model of frailty risk prediction in maintenance hemodialysis patients. Methods Prospective study design was adopted,a total of 876 patients who received hemodialysis in a tertiary class A hospital in Zhejiang Province from March 2020 to April 2022 were recruited,including March 2020 to July 2021 as the modeling group（n=491）and August 2021 to April 2022 as the validation group（n=385）. Univariate and multivariate logistic regression were used to analyze the risk factors of frailty in maintenance hemodialysis patients,and we established a risk prediction model and to draw a nomogram. Hosmer-Lemeshow test and area under receiver operating characteristic were used to evaluate the clinical prediction effect of the model. The bootstrap method sampling method was used to internally validate the model. Results 226 patients（25.80%）had frailty,123（25.05%）in the modeling group and 103（26.75%）in the validation group. Six influencing factors,including age（OR=3.553）,activities of daily living（OR=37.804）,history of stroke（OR=16.434）,serum albumin（OR=4.197）,C-reactive protein（OR=2.633）,and serum creatinine（OR=2.201）,were used to construct the prediction model. In the modeling group,Hosmer-Lemeshow goodness-of-fit test showed χ²=5.667,P=0.772,the AUC was 0.955[95%CI（0.936,0.974）],the sensitivity was 89.7%,the specificity was 87.7%,the Youden index was 0.774,and internal validation C-statistic value was 0.950. In the validation group,Hosmer-Lemeshow goodness-of-fit test showed χ²=44.085,P=1.362,the AUC was 0.914[95%CI（0.882,0.946）],the sensitivity was 85.5%,the specificity was 85.4%,the Youden index was 0.709,and the accuracy was 73.5%. Conclusion The risk prediction model of frailty in maintenance hemodialysis patients can better visually predict the occurrence risk of frailty in patients,providing support for the early identification and intervention of medical staff.

Key words: Renal Dialysis, Frailty, Risk Reduction Behavior, Nomogram, Predictive Model, Nursing