Xu Yue, Zhang Shuhong, Wang Ping, Chen Yuanyuan, Mao Yazhan, Yuan Xina, Li Yinyi, Li Mingming, Sheng Li, Li Xiaoqian, Gao Na
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.
Department 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.
Int J Gen Med. 2024 Aug 2;17:3373-3385. doi: 10.2147/IJGM.S465973. eCollection 2024.
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.
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.
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.
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.
本研究旨在探讨老年住院高血压患者虚弱的相关因素及其对临床预后的影响，并构建该人群虚弱发生的预测模型。
进行了一项横断面和前瞻性观察队列研究，纳入了2022年1月至6月在该机构确诊的312例老年高血压患者。采用弗里德虚弱表型（FP）诊断虚弱，而查尔森合并症指数（CCI）评估慢性病的存在情况。使用SPSS 22.0进行数据分析。以虚弱为因变量进行二元逻辑回归分析以确定危险因素。对患者进行一年的随访，监测再入院率和全因死亡率。
多因素逻辑回归确定CCI分级（P = 0.030）、性别（OR = 21.618，95%CI：4.062 - 115.061，P < 0.001）、年龄（OR = 1.147，95%CI：1.086 - 1.211，P < 0.001）、卧床状态（OR = 11.620，95%CI：3.282 - 41.140，P < 0.001）、心律失常（OR = 14.414，95%CI：4.558 - 45.585，P < 0.001）以及一些生化标志物为虚弱的独立预测因素。开发了一个预测模型，其在受试者工作特征曲线下面积（AUC）为0.915，显示出强大的判别能力。在虚弱、pre - 虚弱和非虚弱组之间观察到再入院率和全因死亡率存在统计学显著差异（P < 0.001），虚弱组这些不良结局的发生率最高。值得注意的是，在该队列中，虚弱是再入院的显著预测因素（P < 0.05），但不是全因死亡率的预测因素。
本研究为老年高血压患者建立了一个强大的虚弱预测模型，突出了CCI分级、性别、年龄以及其他临床和生化因素对虚弱的影响。该模型为医疗保健提供者提供了一个有价值的工具，以识别有风险的老年人，促进心血管疾病管理的针对性干预策略。
