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Challenges of developing a cardiovascular risk calculator for patients with rheumatoid arthritis
Cynthia S Crowson1, Silvia Rollefstad2, George D Kitas3
1Division of Biomedical Statistics and Informatics, Department of Health Sciences Research and Division of Rheumatology, Department of Medicine, Mayo Clinic, Rochester, Minnesota, United States of America.
Insights
Developing a cardiovascular disease (CVD) risk calculator for rheumatoid arthritis (RA) patients proved challenging. New models incorporating RA characteristics did not outperform general population calculators for predicting CVD risk.
Area of Science:
- Rheumatology
- Cardiology
- Epidemiology
Background:
- Patients with rheumatoid arthritis (RA) face a higher risk of cardiovascular disease (CVD).
- Existing CVD risk calculators for the general population are inadequate for RA patients.
- Developing accurate risk prediction models for RA patients presents unique challenges.
Purpose of the Study:
- To develop a novel cardiovascular disease (CVD) risk calculator specifically for patients with rheumatoid arthritis (RA).
- To identify key traditional CVD risk factors and RA-specific characteristics for improved risk prediction.
Main Methods:
- Combined data from thirteen RA patient cohorts across ten countries.
- Collected baseline CVD risk factors, RA characteristics, and CVD outcomes.
- Utilized Cox models and 10-fold cross-validation to develop and assess risk prediction models.
Main Results:
- Included 5638 RA patients (mean age 55 years, 76% female) without prior CVD.
- Observed 389 CVD events during a mean follow-up of 5.8 years.
- Developed two models using RA disease activity scores (DAS28ESR or HAQ) alongside traditional risk factors; however, their performance was similar to general population calculators.
Conclusions:
- Specific CVD risk calculators for RA patients, incorporating disease characteristics, did not demonstrate superior performance over general population tools.
- The study highlights significant challenges in developing accurate CVD risk prediction models for RA patients.
- Lessons learned from this effort are detailed for future research in RA CVD risk assessment.
Objective:
Cardiovascular disease (CVD) risk calculators designed for use in the general population do not accurately predict the risk of CVD among patients with rheumatoid arthritis (RA), who are at increased risk of CVD. The process of developing risk prediction models involves numerous issues. Our goal was to develop a CVD risk calculator for patients with RA.
Methods:
Thirteen cohorts of patients with RA originating from 10 different countries (UK, Norway, Netherlands, USA, Sweden, Greece, South Africa, Spain, Canada and Mexico) were combined. CVD risk factors and RA characteristics at baseline, in addition to information on CVD outcomes were collected. Cox models were used to develop a CVD risk calculator, considering traditional CVD risk factors and RA characteristics. Model performance was assessed using measures of discrimination and calibration with 10-fold cross-validation.
Results:
A total of 5638 RA patients without prior CVD were included (mean age: 55 [SD: 14] years, 76% female). During a mean follow-up of 5.8 years (30139 person years), 389 patients developed a CVD event. Event rates varied between cohorts, necessitating inclusion of high and low risk strata in the models. The multivariable analyses revealed 2 risk prediction models including either a disease activity score including a 28 joint count and erythrocyte sedimentation rate (DAS28ESR) or a health assessment questionnaire (HAQ) along with age, sex, presence of hypertension, current smoking and ratio of total cholesterol to high-density lipoprotein cholesterol. Unfortunately, performance of these models was similar to general population CVD risk calculators.
Conclusion:
Efforts to develop a specific CVD risk calculator for patients with RA yielded 2 potential models including RA disease characteristics, but neither demonstrated improved performance compared to risk calculators designed for use in the general population. Challenges encountered and lessons learned are discussed in detail.
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