Related Experiment Video
Updated: Sep 10, 2025
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 evaluation of cardiovascular disease-specific frailty index: a machine learning based analysis of the
Jiatang Xu1,2, Zhensheng Hu2,3, Kai Huang2,4
1Department of Cardiovascular Surgery, The First Affiliated Hospital of Sun Yat-sen University, Sun Yat-sen University, No. 58 Zhongshan Second Road, Guangzhou 510030, China.
Insights
A new Cardiovascular Disease-specific Frailty Index (FICVD) was developed using machine learning. FICVD improves cardiovascular disease risk prediction and stratification, especially when combined with genetic risk factors.
Area of Science:
- Cardiology
- Gerontology
- Biostatistics
- Genetics
Background:
- Frailty assessments for cardiovascular disease (CVD) risk lack specificity.
- Optimizing frailty assessment strategies for CVD risk is crucial for early intervention.
- Machine learning algorithms offer novel approaches to enhance risk prediction models.
Purpose of the Study:
- To develop and validate a CVD-specific Frailty Index (FICVD) using machine learning.
- To assess the predictive performance of FICVD compared to traditional Frailty Index (FI) for incident CVD.
- To evaluate the combined utility of FICVD and polygenic risk score (PRS) for CVD risk stratification.
Main Methods:
- Utilized a prospective cohort of 366,622 participants from the UK Biobank.
- Employed elastic net regression to construct the FICVD from health-related items.
- Generated a polygenic risk score (PRS) for CVD to assess genetic susceptibility.
Main Results:
- FICVD demonstrated higher hazard ratios for incident CVD risk compared to traditional FI.
- The area under the curve for FICVD in predicting 10-year CVD was significantly higher than FI (0.649 vs. 0.605).
- Combining high FICVD and high genetic risk identified individuals with a 2.84-fold increased risk of CVD.
Conclusions:
- The developed FICVD shows superior performance in predicting and stratifying CVD risk.
- Integrating FICVD with genetic susceptibility enhances the identification of high-risk individuals for CVD.
- FICVD offers a novel approach to optimize individualized CVD risk assessment and guide early intervention strategies.
Aims:
Frailty assessments targeting cardiovascular diseases (CVD) risk lack specificity. Our study aims to develop and validate a CVD-specific FI (FICVD) using machine learning algorithms.
Methods And Results:
366 622 included participants from the prospective cohort of UK Biobank were split to the development, temporal validation and spatial validation cohorts. Elastic net regression was conducted in the development cohort to obtain the coefficients for each health-related items of frailty index (FI) to construct a CVD-specific FI (FICVD). A polygenic risk score (PRS) for CVD was generated to quantify participants' genetic susceptibility to CVD. Results showed the hazard ratios for a 1 SD increase in FICVD and FI for the risk of incident CVD were 1.40 (95% CI: 1.37-1.43) and 1.26 (95% CI: 1.24-1.27) in the development cohort, respectively. The area under the curve value for FICVD was significantly higher than that for FI in predicting 10-year CVD (0.649 vs. 0.605) in the development cohort. Similar patterns were observed in both the temporal validation and the spatial validation cohort. In addition, participants with high FICVD and high genetic risk have a 2.84-fold (95%CI: 2.79-2.96) risk of CVD, compared with participants with low FICVD and low genetic risk.
Conclusion:
Newly developed FICVD demonstrated better performance in prediction and risk stratification of CVD. Also, combining FICVD with genetic susceptibility can assist in the identification of high-risk individuals for CVD. Our findings collectively emphasize the novel FICVD can optimize individualized` CVD risk assessment, and provide potential strategies for CVD prediction and early intervention.
Lay Summary:
This study conducted based on a prospective cohort and machine learning algorithms to optimize frailty assessment strategies for CVD risk. Key findings in our research are as follows:Construction of FICVD requires fewer health-related items than the construction of traditional FI, which may substantially reduce the burden of data collection.FICVD demonstrated better performance in both prediction and risk stratification of CVD than traditional FI.Combination of FICVD and genetic susceptibility can further enhance the identification of high-risk individuals for CVD.
