Background As patients age, the frailty of those with multimorbidity increases, often resulting in adverse health outcomes. The current study investigated the frailty status and the factors which influence it in elderly patients with multimorbidity in Chinese hospitals. The relationship between the frailty of patients with multimorbidity and adverse outcomes was explored. Methods The current prospective cohort study investigated inpatients in the internal medicine department of 5 tertiary hospitals in Sichuan Province, China. A total of 3836 elderly patients with multimorbidity were enrolled. Frailty was assessed using the FRAIL scale and adverse outcome events occurring during hospitalization were tracked. Descriptive statistics and logistic regressions were used for data analysis. Results The prevalence of frailty was 27.2% and of pre-frailty, 58.9%. Logistic regression analysis showed that increasing age, low BMI, low education level, lack of exercise, multiple types of medications and multiple numbers of chronic diseases were the main risk factors for frailty in elderly patients with multimorbidity (OR values: 1.020, 1.469, 2.350, 2.836, 1.156 and 1.308, respectively). The incidence of adverse outcomes was 13.9% among the cohort with the most common being deep vein thrombosis (42.4%), followed by pressure injury (38.8%). Regression analysis showed a significant correlation of frailty with adverse outcome (OR: 1.496; p < 0.01). Conclusions The prevalence of frailty and pre-frailty in hospitalized elderly patients with multimorbidity was high. Increasing age, low BMI, low education level, lack of exercise, multiple types of medications and multiple numbers of chronic diseases were factors which influenced frailty and frailty was an important factor in the occurrence of adverse outcomes. The most common adverse outcome of elderly multimorbidity patients during hospitalization was deep vein thrombosis.

Aims This study aimed to investigate the influencing factors of frailty in elderly patients with multimorbidity and to develop a predictive risk model for frailty in elderly patients with multimorbidity. Methods In total, 3836 elderly patients with multimorbidity who were admitted to the medical wards of five grade A tertiary hospitals in Sichuan Province from March 2020 to June 2021 were selected. Based on the general data of patients with multimorbidity, the independent risk factors for frailty were obtained using logistic analysis, and a risk prediction model of frailty was developed. Results Independent risk factors for frailty in patients with multimorbidity were age, types of medication, and comorbidity with chronic heart failure (CHF), chronic obstructive pulmonary disease (COPD) and chronic cerebrovascular disease (CCVD); and the protective factors for frailty were body mass index (BMI), exercise and education level. The expression of the model was Z = −2.054 + 0.016 × age − 0.029 × BMI − 0.153 × education level‐1.059 × exercise + 0.203 × types of medication + 0.788 × comorbidity with CHF + 0.950 × comorbidity with COPD + 0.363 × comorbidity with CCVD. Conclusion Age, BMI, education level, exercise, types of medication, and comorbidity with CHF, COPD and CCVD can affect frailty risk in elderly patients with multimorbidity, which may be helpful to predict the frailty risk of elderly patients with multimorbidity. Geriatr Gerontol Int 2022; 22: 471–476.

Background. The current study analyzed the status and the factors of foot ulcers in diabetic patients and developed a nomogram and web calculator for the risk prediction model of diabetic foot ulcers. Methods. This was a prospective cohort study that used cluster sampling to enroll diabetic patients in the Department of Endocrinology and Metabolism in a tertiary hospital in Chengdu from July 2015 to February 2020. The risk factors for diabetic foot ulcers were obtained by logistic regression analysis. Nomogram and web calculator for the risk prediction model were constructed by R software. Results. The incidence of foot ulcers was 12.4% (302/2432). Logistic stepwise regression analysis showed that BMI (OR: 1.059; 95% CI 1.021-1.099), abnormal foot skin color (OR: 1.450; 95% CI 1.011-2.080), foot arterial pulse (OR: 1.488; 95% CI: 1.242-1.778), callus (OR: 2.924; 95%: CI 2.133-4.001), and history of ulcer (OR: 3.648; 95% CI: 2.133-5.191) were risk factors for foot ulcers. The nomogram and web calculator model were developed according to risk predictors. The performance of the model was tested, and the testing data were as follows: AUC (area under curve) of the primary cohort was 0.741 (95% CI: 0.7022-0.7799), and AUC of the validation cohort was 0.787 (95% CI: 0.7342-0.8407); the Brier score of the primary cohort was 0.098, and the Brier score of the validation cohort was 0.087. Conclusions. The incidence of diabetic foot ulcers was high, especially in diabetic patients with a history of foot ulcers. This study presented a nomogram and web calculator that incorporates BMI, abnormal foot skin color, foot arterial pulse, callus, and history of foot ulcers, which can be conveniently used to facilitate the individualized prediction of diabetic foot ulcers.

Aims and Objectives:This study investigated the relationship between frailty and diabetes complicated with comorbidities.Background: Frailty is a common geriatric syndrome, and older adults with diabetes are prone to frailty. Patients with diabetes and comorbidities might be at increased risk of developing frailty. Design:A multicenter cross-sectional study. Methods:A cross-sectional study was conducted to identify older patients with diabetes and comorbidities in the internal medicine departments of five tertiary general hospitals in Sichuan Province, China, from March 2020 to June 2021. We used the FRAIL scale to identify frailty, and multinomial logistic regression was used to compare sociodemographic characteristics and comorbidities of frail or pre-frail participants with robust participants. The STROBE checklist was used for this crosssectional study.Results: A total of 1652 patients (883 males, 53.5%) were included, and the prevalence of frailty was 26.5%. Multinomial logistic regression analysis revealed that compared with robust patients, diabetic patients with hypertension, coronary heart disease, chronic cardiac failure, COPD, cerebrovascular diseases, osteoarticular diseases, chronic renal diseases, chronic gastrointestinal diseases and cancer were more likely to be frail. In addition, patients who engaged in less exercise, presented more comorbidities, were older and had lower education levels, were more prone to frailty. Conclusion:There was a clear correlation between diabetes complicated with comorbidities and the development of frailty. Appropriate personalised care levels for patients with diabetes and comorbidities, and early screening for frailty might reduce the prevalence of frailty in these patients. Relevance to clinical practice:This study provided information for healthcare providers to identify circumstances that increase the risk of frailty and more effectively support patients with diabetes and comorbidities.