Objective To investigate the current status of health-promoting lifestyle and frailty in elderly patients with osteoporosis,and to analyze the influence of health-promoting lifestyle on frailty.Methods A total of 286 elderly patients with osteoporosis treated in the 904th Hospital of the Joint Logistics Support Force from December 2022 to December 2023 were selected as research objects.Health-promoting lifestyle and frailty levels in these patients were assessed using the Health Promotion Lifestyle Profile Ⅱ(HPLP-Ⅱ)and Frailty Comprehensive Assessment Scale,respectively.Pearson correlation analysis and multiple linear regression were used to analyze the effect of health-promoting lifestyle on frailty.Results The mean frailty score was 52.83±16.42(range,31 to 92),and the health-promoting lifestyle score of was 110.64±14.37(range,60 to 127).Univariate analysis showed that self-care ability,hypertension,diabetes mellitus and depression were related to the frailty in elderly osteoporosis patients(P<0.05).The score of health-promoting lifestyle in elderly patients with osteoporosis was negatively correlated with the scores of various dimensions of frailty(r=-0.512 to-0.214,P<0.01).Multiple linear regression analysis showed that hypertension,diabetes mellitus,depression,and health-promoting lifestyle score were all independent risk factors for frailty in elderly osteoporosis patients(P<0.05).Conclusion Elderly patients with osteoporosis have severe frailty.The hypertension,diabetes mellitus and depression affect the frailty in elderly osteoporosis patients.Interventions should be taken to help elderly osteoporosis patients improve their healthy lifestyle and reduce their frailty.

Objective To screen the influencing factors associated with adverse clinical out-comes in hospitalized elderly patients with Alzheimer's disease(AD)using LASSO-Logistic regression analysis and to construct a nomogram prediction model.Methods A retrospective selection of 214 hospitalized elderly patients with AD who visited the Department of Geriatric Medicine in the hospital from February 2021 to March 2023 was conducted,and clinical data of all patients were collected.Patients were divided into adverse events group(n=53)and non-adverse events group(n=161)based on the occurrence of adverse clinical outcomes.After variable screening using LASSO regres-sion,multivariate Logistic regression analysis was performed to identify independent factors influen-cing adverse clinical outcomes in hospitalized elderly patients with AD.A Nomogram model for pre-dicting adverse clinical outcomes in these patients was established based on the results of multivariate analysis.The predictive performance,calibration,and clinical utility of the Nomogram model were evaluated using the concordance index,calibration curve,and decision curve analysis(DCA).The diagnostic performance of the Nomogram model for adverse clinical outcomes in hospitalized elderly patients with AD was assessed using the receiver operating characteristic(ROC)curve and the area under the curve(AUC).Results LASSO-Logistic regression analysis revealed that the Mini-Men-tal State Examination(MMSE)score was an independent protective factor against adverse clinical outcomes in hospitalized elderly patients with AD(P＜0.05),while the Charlson Comorbidity In-dex(CCI score),creatinine,urea,and fasting blood glucose(FBG)levels were all independent risk factors for adverse clinical outcomes in these patients(P＜0.05).The Nomogram model con-structed based on the influencing factors screened by LASSO-Logistic regression analysis showed a concordance index of 0.994(95％CI,0.958 to 1.000)for predicting adverse clinical outcomes in hospitalized elderly patients with AD.The Hosmer-Lemeshow test results indicated x2=1.909,P=0.984,suggesting good model fit.The DCA result demonstrated that the model had favorable threshold probabilities and net clinical benefits.Conclusion The Nomogram model for predicting clinical outcomes in elderly inpatients with AD constructed based on LASSO-Logistic regression anal-ysis exhibits high predictive value,and can be used to forecast the occurrence of adverse clinical outcomes in these patients.