Volume 22 Issue 6
Jun.  2024
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Article Navigation >  Chinese Journal of General Practice  > 2024  >  22(6): 991-994
TAN Jie, CHEN Manman, CHEN Lingyun, HUANG Yuli. Study on the influence factors of frailty and the construction of risk prediction model in elderly inpatients with chronic heart failure[J]. Chinese Journal of General Practice, 2024, 22(6): 991-994. doi: 10.16766/j.cnki.issn.1674-4152.003551
Citation: TAN Jie, CHEN Manman, CHEN Lingyun, HUANG Yuli. Study on the influence factors of frailty and the construction of risk prediction model in elderly inpatients with chronic heart failure[J]. Chinese Journal of General Practice, 2024, 22(6): 991-994. doi: 10.16766/j.cnki.issn.1674-4152.003551
shu

Study on the influence factors of frailty and the construction of risk prediction model in elderly inpatients with chronic heart failure

doi: 10.16766/j.cnki.issn.1674-4152.003551
  • 1.

    Department of Cardiovascular Medicine, the Fifth People' s Hospital of Fuyang, Fuyang, Anhui 236000, China

Funds:

 SK2020A0351

  • Received Date: 2023-09-21
    Available Online: 2024-07-22
    Abstract
  •   Objective  To explore the influencing factors of frailty in elderly patients with chronic heart failure, and build a frailty risk prediction model to provide clinical reference for preventing and reducing frailty.  Methods  A total of 198 elderly patients with chronic heart failure who were hospitalized in the cardiovascular Department of the Fifth People ' s Hospital of Fuyang from March 2022 to March 2023 were selected by convenience sampling method. They were divided into a frailty group and a non-frailty group according to whether frailty occurred. The influencing factors of frailty were analyzed by binary Logistic regression. Hosmer-Lemeshow test and ROC curve were used to detect the goodness of fit and prediction effect respectively.  Results  The incidence of frailty in elderly CHF patients was 32.32% (64/198), and fatigue was the main symptom (51.01%, 101/198). Age (OR=1.101, P=0.007), NYHA cardiac function grade (OR=2.550, P=0.011), course of disease (OR=2.210, P=0.014), sleep state (OR=2.215, P=0.010), N-terminal brain natriuretic peptide precursor (OR=1.003, P < 0.001), left ventricular ejection fraction (OR=0.910, P < 0.001) and self-management level (OR=0.229, P < 0.001) were influencing factors in the occurrence of frailty. The regression model had good goodness of fit (χ2=2.915, P=0.940), and the AUC under the ROC curve was 0.913 (P < 0.001, 95% CI: 0.870-0.956), sensitivity was 89.1%, specificity was 81.3%.  Conclusion  The incidence of frailty in elderly patients with chronic heart failure is high. Clinical attention should be paid to elderly patients with poor heart function, long course of disease, poor sleep quality, and low self-management ability. The frailty risk prediction model established in this study is effective in elderly patients with chronic heart failure, which can provide a reference for frailty risk assessment and intervention.

     

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