Frail Status And Risk Factors In Elderly Inpatients With Comorbidity

Background and ObjectiveAs global aging accelerates,geriatric comorbidities have become a world public health problem.Frailty is the reduction of physiological reserves and the disturbance of the balance mechanism during the aging process of the body.Comorbidities and frailty interact with each other and form a vicious circle,which can lead to higher risk of poor prognosis and higher medical burden for the elderly,bringing new challenges to geriatrics.The research on frailty in my country started relatively late.According to the data survey shows that the incidence of frailty in the community is 12.8%,the incidence of frailty in elderly patients in the hospital is 46.6%,and the incidence of frailty in elderly inpatients with comorbidities is as high as 77.6%.At present,there are few studies on the correlation between elderly inpatients with comorbidities and frailty in my country.This study aims to provide more evidence for early individualized interventions for frailty by analyzing the frailty status and risk factors of elderly inpatients with comorbidities.Methods1.The present study was a cross-sectional study.From January 2019 to January 2021,214 inpatients≥60 old years from the geriatric inpatient department of our hospital were enrolled.According to the Frailty Screening Scale（FRAIL）,the patients were divided into frailty group and non-frailty group.The general data of the two groups of patients were collected,including gender,age,height,weight,type of medication and the body mass index was calculated（BMI=kg/m~2）.At the same time,the Charlson comorbidity index（CCI）,nutritional risk,cognitive function of the two groups were evaluated and testing of general clinical biological indicators,such as red blood cells（RBC）,hemoglobin（HGB）,total protein（TP）,prealbumin（PAB）,albumin（ALB）,fibrinogen（FIB）,d-dimer（D-D）,etc.2.To compare whether there were differences in general data and clinical biological indicators between the frail group and the non-frail group to apply SPSS24.0 statistical software for data analysis,t-test,χ~2-test,binary logistic multi-factor regression model and other statistical methods were used to analyze the risk factors affecting the occurrence of frailty in elderly inpatients with comorbidities.Results1.214 elderly patients with comorbidities,the top three chronic diseases from highest to lowest were hypertension 70.6%,stroke 63.6%,and diabetes 45.3%（Showed in Table2）,the incidence of frailty was 56.1%（120/214）,pre-frailty 26.6%（57/214）and no frailty17.3%（37/214）（Showed in Table 3）.2.The differences in age,number of diseases,CCI classification,stroke,type of medication taken,nutritional risk,cognitive impairment,RBC,HGB,TP,PAB,ALB,FIB,and D-D between the two groups were statistically significant（P<0.05）（Showed in Table 4,Table 5）.3.Binary logistic regression analysis showed that CCI classification（OR=4.877,95%CI1.883 to 12.634）,nutritional risk（OR=3.233,95%CI 1.323 to 7.901）,cognitive dysfunction（OR=3.630,95%CI 1.400 to 9.416）,PAB（OR=0.989,95%CI 0.981 to0.997）were independent risk factors for the development of frailty in elderly inpatients with comorbidities（Showed in Table 6）.Conclusions1.High prevalence of railty in elderly inpatients with comorbidities（56.1%）.2.The age,number of diseases,CCI grade,number of stroke cases,type of medication,nutritional risk,and cognitive dysfunction in elderly inpatients with comorbidities gradually increased during the process from non-frailty to frailty.During this process,the levels of RBC,HGB,TP,PAB,ALB decreased,while the levels of FIB and D-D increased.3.High CCI grade,poor nutritional status,cognitive dysfunction,and low PAB are important risk factors for frailty.Therefore,medical staff can use these predict and intervene early frailty in elderly inpatients with comorbidities.