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Measuring Frailty in HIV-infected Individuals. Identification of Frail Patients is the First Step to Amelioration and Reversal of Frailty
Published on: July 24, 2013
Development and validation of a predictive risk model for frailty in elderly patients with multimorbidity
Fengmei Huang1, Xiaoling Yang1, Li Yuan1
1West China School of Nursing, Sichuan University/Department of Endocrinology and Metabolism, West China Hospital, Sichuan University, Chengdu, China.
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.
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