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Updated: Dec 6, 2025

An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Lifetime risk predictions for cardiovascular diseases: Competing risks analyses on a population-based cohort in
Anna Stenling1, Christel Häggström2, Margareta Norberg1
1Department of Epidemiology and Global Health, Umeå University, 901 87, Umeå, Sweden.
Insights
This study developed lifetime cardiovascular disease (CVD) risk models. The models accurately estimate individual risks for specific CVD events based on modifiable risk factors.
Area of Science:
- Cardiology
- Public Health
- Epidemiology
Background:
- Current cardiovascular disease (CVD) risk models primarily estimate short-term risks.
- There is a need for models that assess lifetime CVD risk for a comprehensive approach.
Purpose of the Study:
- To develop and validate lifetime risk models for specific cardiovascular events including coronary heart disease, stroke, and heart failure.
- To provide diagnosis-specific lifetime risk estimates for various risk factor profiles.
Main Methods:
- Utilized data from 92,915 individuals participating in a lifestyle intervention program.
- Employed parametric multivariable survival regression with a competing risks approach.
- Modeled cause-specific hazards and translated them into cumulative incidence functions for gender-age specific analyses.
Main Results:
- Men exhibited a higher risk of cardiovascular events occurring at younger ages compared to women.
- Modifying key risk factors like cholesterol, smoking, and blood pressure significantly impacted CVD event rates in simulated risk profiles.
- The developed models demonstrated the substantial influence of modifiable risk factors on long-term CVD risk.
Conclusions:
- The developed models enable accurate estimation of lifetime risk for individual cardiovascular events.
- These diagnosis-specific predictions offer a more precise tool for personalized cardiovascular risk assessment.
- The findings underscore the importance of addressing modifiable risk factors for long-term cardiovascular health.
Background And Aims:
There are guideline discussions on a lifetime approach to cardiovascular risk. Many of the available risk models estimate the short-term, usually 10-year risk of non-fatal and fatal cardiovascular diseases (CVD) grouped together. We aimed to develop lifetime risk models for non-fatal coronary heart disease, stroke, heart failure and death from CVD and non-CVD.
Methods:
We included 92,915 individuals who had participated in a community-based lifestyle intervention programme at 40, 50 and/or 60 years of age. Their collected data on selected risk factors were linked to register data on hospitalizations and death. Parametric multivariable survival regression with a competing risks approach was employed to model cause-specific hazards, which were translated into cumulative incidence functions to provide the risk of experiencing each event separately. All analyses were performed gender-age wise. For illustrative purposes, "better" and "worse" risk profiles were created by setting three modifiable risk factors to the best and worst levels, respectively.
Results:
Most of the risk factors qualified for inclusion in the regressions. Men had a higher risk of cardiovascular events and the events occurred at a younger age than women. In the created risk profiles, where serum total cholesterol, smoking status and blood pressure were modified, an excessive number of CVD events were observed in the worse profiles.
Conclusions:
Using these models, the lifetime risk of each of the first CVD events can be estimated for different risk factor profiles. Since the predictions are diagnosis specific, the estimates are more accurate.
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