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Development and validation of frailty risk prediction model for elderly patients with coronary heart disease
Siqin Liu1,2, Xiaoli Yuan3, Heting Liang2
1Department of Neurology, Affiliated Hospital of Zunyi Medical University, Zunyi, China.
Insights
Frailty is prevalent in elderly patients with coronary heart disease (CHD). Poor health status, advanced age, and impaired daily living abilities are key risk factors, necessitating early intervention strategies.
Area of Science:
- Gerontology
- Cardiology
- Public Health
Background:
- Frailty is a significant concern in elderly populations, particularly those with chronic conditions like coronary heart disease (CHD).
- Understanding the factors contributing to frailty in CHD patients is crucial for developing targeted interventions.
Purpose of the Study:
- To identify influential factors of frailty in elderly patients with CHD.
- To develop and validate a nomogram-based risk prediction model for frailty in this population.
Main Methods:
- A cohort of 592 elderly CHD patients was assessed using general information questionnaires, the Frail scale, and instrumental activities of daily living scales.
- Logistic regression and χ² tests identified frailty risk factors. A nomogram prediction model was developed and validated using ROC curves, Hosmer-Lemeshow tests, and Bootstrap resampling.
Main Results:
- The prevalence of frailty among elderly CHD patients was 30.07%.
- Independent risk factors for frailty included poor health status (OR=28.169), general health status (OR=18.120), age (OR=1.046), and impaired instrumental ability of daily living (OR=2.384).
- The nomogram model demonstrated strong predictive performance with an AUC of 0.847 and good consistency between predicted and actual values (C-index=0.839).
Conclusions:
- Frailty is common in elderly patients with CHD and is associated with health status, age, social participation, and daily living abilities.
- The developed nomogram model effectively predicts frailty, enabling early identification and intervention for at-risk individuals.
Objective:
To analyze the influential factors of frailty in elderly patients with coronary heart disease (CHD), develop a nomogram-based risk prediction model for this population, and validate its predictive performance.
Methods:
A total of 592 elderly patients with CHD were conveniently selected and enrolled from 3 tertiary hospitals, 5 secondary hospitals, and 3 community health service centers in China between October 2022 and January 2023. Data collection involved the use of the general information questionnaire, the Frail scale, and the instrumental ability of daily living assessment scale. And the patients were categorized into two groups based on frailty, and χ2 test as well as logistic regression analysis were used to identify and determine the influencing factors of frailty. The nomograph prediction model for elderly patients with CHD was developed using R software (version 4.2.2). The Hosmer-Lemeshow test and the area under the receiver operating characteristic (ROC) curve were employed to assess the predictive performance of the model. Additionally, the Bootstrap resampling method was utilized to validate the model and generate the calibration curve of the prediction model.
Results:
The prevalence of frailty in elderly patients with CHD was 30.07%. The multiple factor analysis revealed that poor health status (OR = 28.169)/general health status (OR = 18.120), age (OR = 1.046), social activities (OR = 0.673), impaired instrumental ability of daily living (OR = 2.384) were independent risk factors for frailty (all P < 0.05). The area under the ROC curve of the nomograph prediction model was 0.847 (95% CI: 0.809 ~ 0.878, P < 0.001), with a sensitivity of 0.801, and specificity of 0.793; the Hosmer- Lemeshow χ2 value was 12.646 (P = 0.125). The model validation results indicated that the C value of 0.839(95% CI: 0.802 ~ 0.879) and Brier score of 0.139, demonstrating good consistency between predicted and actual values.
Conclusion:
The prevalence of frailty is high among elderly patients with CHD, and it is influenced by various factors such as health status, age, lack of social participation, and impaired ability of daily life. These factors have certain predictive value for identifying frailty early and intervention in elderly patients with CHD.
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