| ObjectivesIn order to understand the current situation of frailty in maintenance hemodialysis patients,analyze the influence factors of frailty in maintenance hemodialysis patients.The risk prediction model of frailty in maintenance hemodialysis patients was constructed.It was hoped that the high-risk population of frailty in maintenance hemodialysis can be screened out,so that the population can be intervened,reducing the incidence of frailty,resulting in the further improvement of the life quality for the patients.MethodsA cross-sectional study was conducted to collect 260 patients with maintenance hemodialysis who met the inclusion and exclusion criteria in The Second Affiliated Hospital of Dalian Medical University in Liaoning province Dalian city from March 2022 to October 2022.FRAIL scale was used to evaluate 260 maintenance hemodialysis patients.The Nutritional Risk Screening Tool 2002,Connor-Davidson Resilience Scale,Self-Efficacy for Managing Chronic Disease,Hospital Anxiety and Depression Scale were used to collect patient data as modeling data.SPSS 25.0 was used for statistical analysis of the data.Univariate analysis was conducted on the collected influence factor data,logistic regression analysis was conducted on the significant influence factors(P<0.05)in the analysis,and variable screening was conducted backward LR,with the inclusion criterion of 0.05 and the exclusion criterion of 0.1.The value corresponding to the maximum Yoden index was taken as the best diagnostic threshold,and the Area Under Curve(AUC)of Receiver Operating Characteristic(ROC)was used to evaluate the resolution of the risk prediction model.The calibration degree of the risk prediction model equation was tested and evaluated by the Hosmer-Lemeshow goodness of fit.Results1.Current status of frailtyIn this study,through the investigation of the frailty status of all 260 maintenance hemodialysis patients who met the standard,it was found that 65 patients had frailty,and the incidence of frailty in maintenance hemodialysis patients was 25.0%(65/260).While the number of FRAIL table items in turn from large to small is 118 cases of fatigue(45.4%),86 cases of resistance(33.1%),72 cases of walking capacity(27.7%),58 cases of weight loss(22.3%),and 28 cases of diseases>5 kinds(10.8%).2.Analysis of risk factors for frailtyUnivariate analysis showed that there were statistically significant differences in gender,age,working status,education level,self-rated health status,exercise status,vascular access,heart disease,cerebrovascular disease,peripheral vascular disease,albumin,nutritional risk,mental resilience,self-efficacy,anxiety and depression between the two groups of maintenance hemodialysis patients with frailty and non-frailty(P<0.05).There were no significant differences in body mass index(BMI),medical insurance,marital status,living alone,smoking,drinking,multiple medication,per capita monthly income of households,dialysis age,frequent-intradialytic hypotension,hypertension,diabetes,hemoglobin,parathyroid hormone and blood urea nitrogen between the two groups(P>0.05)Multivariate Logistic regression analysis was performed on 16 variables with statistical differences in univariate Logistic regression analysis.A total of 7 variables were entered into the final prediction model,including gender,self-rated health status,exercise,peripheral vascular disease,nutritional risk,psychological resilience and depression were independent predictors of patients with frailty in maintenance hemodialysis(P <0.05).No correlation was found between age,job status,education level,vascular access,combined heart disease,combined cerebrovascular disease,albumin,self-efficacy,anxiety in maintenance hemodialysis patients with frailty.3.Establishment of frailty risk prediction model for maintenance hemodialysis patientsThe prediction model of frailty in maintenance hemodialysis patients is as follows:Logit(P)=-2.706+0.875×sex(female)+1.205×self-rated general health status+3.203×self-rated poor health status+0.996×occasional exercise,once or twice a week+1.776× inactivity+3.136×combined peripheral vascular disease+1.629×nutritional risk +0.951×depression-0.045×psychological resilience score.AUC=0.928(P<0.001,95%CI 0.891～0.996),Hosmer-Lemeshow goodness of fit test P=0.530>0.05,suggesting a high degree of calibration and a good degree of fit.According to the Yoden index,the optimal cut-off value of the model was determined to be 0.248.The sensitivity was 87.7%,and the specificity was 86.2%.ConclusionIn this study,the risk prediction model suitable for the maintenance hemodialysis patients with frailty.The model includes 7 independent predictors,including gender,selfrated health status,exercise status,peripheral vascular disease,nutritional risk,mental resilience score and depression.The model is convenient,feasible and has good prediction performance.It has a good ability to distinguish and predict the risk of frailty in maintenance hemodialysis patients,which can provide guidance for clinical staff in predicting frailty in maintenance hemodialysis patients. |
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