RESUMO
Objective:To analyze the current status of frailty in elderly patients undergoing cardiac surgery, explore its risk factors, and establish a predictive model of frailty in order to provide targeted and predictive nursing programs.Methods:A total of 205 cardiac surgery patients admitted to the first affiliated hospital of Xinjiang Medical University from March 2015 to January 2019 were selected as the study subjects. Patients were divided into 2 groups according to whether they were frail: the frailty group ( n=78) and the control group ( n=127). Logistic regression was used to analyze the risk factors that affect the frailty of elderly patients undergoing cardiac surgery. The receiver operating characteristic curve (ROC) was used to evaluate the effectiveness of model X [consisting of body mass index (BMI), Mini-Mental State Examination (MMSE) score, number of diseased patients, and number of drugs] in diagnosing frailty in elderly patients undergoing cardiac surgery. Results:Of the 205 patients, 78 (38.05%) showed frailty. The proportion of high school education level and above, Tinetti Gaitassessment (TGA) score≥24 and MMSE score≥27 in the frailty group was lower than that in the control group, and the proportion of Geriatric Depression Scale-15 (GDS-15) score≥7, the number of diseases≥3 and the number of drugs≥5 in the frailty group was higher than that in the control group ( χ2 value was 9.254-26.061, P<0.05). Logistic regression analysis showed that BMI, MMSE score, number of diseased, and number of medications were independent risk factors for frailty in elderly patients undergoing cardiac surgery ( OR value was 0.032-5.275, P<0.05). The area under the ROC curve, sensitivity and specificity of frailty in elderly cardiac surgery patients assessed by model X were 0.913, 75.61% and 96.77%, respectively. Conclusion:The incidence of frailty is higher in elderly patients undergoing cardiac surgery. Model X can diagnose the frailty of elderly patients undergoing cardiac surgery and help clinical nurses to carry out targeted care.