Objective:To analyze the current status and influencing factors of balance ability in elderly inpatients, and to explore the correlation between balance function and the tendency to suffer from geriatric syndromes.Methods:A total of 262 elderly patients hospitalized from April to August 2023 were selected as the research objects by convenience sampling method.A systematic health assessment was performed by professional evaluators using the "Edge Intelligent Geriatric Assessment System" software of the Jiangsu Province Hospital within one week after admission.According to the results of Performance Oriented Mobility Assessment(POMA), the subjects were divided into normal balance group(n=188), POMA score 19 to 24 group(n=36)and less than 19 score group(n=38), the differences in the tendency of the three groups of patients to develop geriatric syndromes were compared.Logistic regression analysis was used to screen the related factors and construct the regression equation.Receiver operating characteristic(ROC)curve was drawn to evaluate the predictive value of regression equation.Results:A total of 262 patients, of which 156(59.54%)were males, with an age range of 60 to 100 years(mean age 74.11±8.77 years)were included in the study.The total POMA score of 262 patients was 23.69±6.00, of which 74 cases(28.24%)had balance dysfunction.Univariate analysis showed that there were significant differences in age( t=20.356, P<0.001), serum albumin( t=3.999, P=0.019), proportions of people suffering from depression, frailty, sarcopenia, sleep disorders, nutrition risk and high fall risk between patients with different balance ability( χ2=10.250, 76.763, 101.728, 37.805, 22.472, 75.095, all P<0.05).Binary logistic regression model showed that age, sarcopenia, suspected insomnia, insomnia, and nutritional risk were independent predictors of balance ability in elderly patients(OR=1.071, 12.424, 6.719, 8.321, 3.440, all P<0.05).The above related variables were included in the regression equation: Logit(P)=-8.792+ 0.069×age+ 2.520×sarcopenia+ 1.905×suspected insomnia+ 2.119×insomnia+ 1.236×nutritional risk.ROC curve analysis showed that the area under the curve(AUC)was 0.902(95% CI: 0.857-0.946, P<0.001), the predictive specificity was 86.17% and the sensitivity was 85.14%. Conclusions:Age≥75.5 years, sarcopenia, sleep disorders, and nutritional risk could be used as predictors of balance disorders in elderly inpatients.The regression model constructed based on these indicators has a good predictive value.The establishment of the edge intelligent geriatric assessment system promotes the improvement of medical information level.

Objective:To analyze the current status and influencing factors of balance ability in elderly inpatients, and to explore the correlation between balance function and the tendency to suffer from geriatric syndromes.Methods:A total of 262 elderly patients hospitalized from April to August 2023 were selected as the research objects by convenience sampling method.A systematic health assessment was performed by professional evaluators using the "Edge Intelligent Geriatric Assessment System" software of the Jiangsu Province Hospital within one week after admission.According to the results of Performance Oriented Mobility Assessment(POMA), the subjects were divided into normal balance group(n=188), POMA score 19 to 24 group(n=36)and less than 19 score group(n=38), the differences in the tendency of the three groups of patients to develop geriatric syndromes were compared.Logistic regression analysis was used to screen the related factors and construct the regression equation.Receiver operating characteristic(ROC)curve was drawn to evaluate the predictive value of regression equation.Results:A total of 262 patients, of which 156(59.54%)were males, with an age range of 60 to 100 years(mean age 74.11±8.77 years)were included in the study.The total POMA score of 262 patients was 23.69±6.00, of which 74 cases(28.24%)had balance dysfunction.Univariate analysis showed that there were significant differences in age( t=20.356, P<0.001), serum albumin( t=3.999, P=0.019), proportions of people suffering from depression, frailty, sarcopenia, sleep disorders, nutrition risk and high fall risk between patients with different balance ability( χ2=10.250, 76.763, 101.728, 37.805, 22.472, 75.095, all P<0.05).Binary logistic regression model showed that age, sarcopenia, suspected insomnia, insomnia, and nutritional risk were independent predictors of balance ability in elderly patients(OR=1.071, 12.424, 6.719, 8.321, 3.440, all P<0.05).The above related variables were included in the regression equation: Logit(P)=-8.792+ 0.069×age+ 2.520×sarcopenia+ 1.905×suspected insomnia+ 2.119×insomnia+ 1.236×nutritional risk.ROC curve analysis showed that the area under the curve(AUC)was 0.902(95% CI: 0.857-0.946, P<0.001), the predictive specificity was 86.17% and the sensitivity was 85.14%. Conclusions:Age≥75.5 years, sarcopenia, sleep disorders, and nutritional risk could be used as predictors of balance disorders in elderly inpatients.The regression model constructed based on these indicators has a good predictive value.The establishment of the edge intelligent geriatric assessment system promotes the improvement of medical information level.

Objective:To investigate the correlation between comorbidity and fall injury degree in elderly inpatients with falls.Methods:Using the method of retrospective study, 212 elderly patients with fall comorbidity in a tertiary general hospital from January 2017 to March 2020 were selected as the research objects. The clinical data were collected according to the degree of injury. The severity of comorbidity was evaluated by age adjusted Charlson comorbidity index (ACCI), and the relationship between the comorbidity and the degree of fall injury was analyzed.Results:The overall acci index of 212 patients was (7.21 ± 2.69) points, and the average number of comorbidities was (4.74 ± 2.20). Univariate analysis showed that there were significant differences in CCI ( F value was 10.806, P<0.001)、aCCI ( F value was 15.231, P<0.001), comorbid circulatory system diseases ( χ2 value was 9.580, P=0.022) , urinary system diseases ( χ2 value was 10.127, P=0.018) , endocrine system diseases ( χ2 value was 7.978, P=0.046) and comorbidities( F value was 3.093, P=0.028) among elderly patients with different injury degrees. Spearman correlation analysis showed that age, CCI, aCCI and the number of comorbidities were correlated with the degree of fall injury ( P<0.05). Logistic regression model showed that aCCI ( B value was 0.234, OR value was 1.264) was an independent predictor of fall injury. The area under the ROC curve was 0.652 (95% CI 0.579-0.725, P<0.001). Conclusions:The aCCI index can predict the degree of fall injury.