Yue Xu,1,* Shuhong Zhang,1,* Ping Wang,1,* Yuanyuan Chen,1 Yazhan Mao,2 Xina Yuan,3 Yinyi Li,4 Mingming Li,1 Li Sheng,1 Xiaoqian Li,1 Na Gao1 

1Department of Cardiology, The Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, 100853, People’s Republic of China; 2Department of Respiratory, The Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, 100853, People’s Republic of China; 3College of Pulmonary and Critical Care Medicine, The Eighth Medical Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, 100853, People’s Republic of China; 4Department of Outpatient, The Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, 100853, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Na Gao; Xiaoqian Li, Department of Cardiology, The Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, 100853, People’s Republic of China, Tel +86 18515165409, Email gaona301s@163.com; 41705763@qq.com

Objective: This study aims to explore the correlates of frailty in hospitalized elderly hypertensive patients and its impact on clinical prognosis, and to construct a predictive model for the occurrence of frailty in this population.
Methods: A cross-sectional and prospective observational cohort study was conducted, involving 312 elderly hypertensive patients diagnosed at the institution from January to June 2022. Frailty was diagnosed using the Fried Frailty Phenotype (FP), while the Charlson Comorbidities Index (CCI) assessed the presence of chronic conditions. Data analysis was performed using SPSS 22.0. Binary logistic regression analysis was conducted with frailty as the dependent variable to identify risk factors. Patients were followed for one year to monitor readmission rates and all-cause mortality.
Results: Multivariate logistic regression identified CCI grade (P=0.030), gender (OR=21.618, 95% CI: 4.062– 115.061, P < 0.001), age (OR=1.147, 95% CI: 1.086– 1.211, P < 0.001), bedridden state (OR=11.620, 95% CI: 3.282– 41.140, P < 0.001), arrhythmia (OR=14.414, 95% CI: 4.558– 45.585, P < 0.001), heart failure (OR=5.439, 95% CI: 1.029– 28.740, P < 0.05), along with several biochemical markers, as independent predictors of frailty. A predictive model was developed, demonstrating a robust discriminative ability with an area under the receiver operating characteristic curve (AUC) of 0.915. Statistically significant differences in readmission rates and all-cause mortality were observed among the frail, pre-frail, and non-frail groups (P< 0.001), with the frail group exhibiting the highest incidence of these adverse outcomes. Notably, frailty emerged as a significant predictor of readmission (P< 0.05) but not of all-cause mortality in this cohort.
Conclusion: This study establishes a robust frailty prediction model for elderly hypertensive patients, highlighting the influence of CCI grade, gender, age, and other clinical and biochemical factors on frailty. The model offers a valuable tool for healthcare providers to identify at-risk elderly individuals, facilitating targeted intervention strategies for cardiovascular disease management.

Keywords: frailty, hypertension, older people, observational study, prediction model