Challenges of developing a cardiovascular risk calculator for patients with rheumatoid arthritis.
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.


CONCLUSIONS
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.
Division 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.
Cynthia S
http://orcid.org/0000-0001-5847-7475
CS
Crowson
Preventive Cardio-Rheuma Clinic, Department of Rheumatology, Diakonhjemmet Hospital, Oslo, Norway.
Silvia
S
Rollefstad
Dudley Group NHS Foundation Trust, West Midlands, United Kingdom.
George D
GD
Kitas
Department of Rheumatic Diseases, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands.
Piet L C M
PL
van Riel
Division 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.
Sherine E
SE
Gabriel
Preventive Cardio-Rheuma Clinic, Department of Rheumatology, Diakonhjemmet Hospital, Oslo, Norway.
Anne Grete
AG
Semb
D000328:Adult; D000368:Aged; D000465:Algorithms; D001172:Arthritis, Rheumatoid / Q000150:complications*; D002318:Cardiovascular Diseases / Q000453:epidemiology* / Q000209:etiology*; D016208:Databases, Factual; D005260:Female; D006801:Humans; D015994:Incidence; D008297:Male; D008875:Middle Aged; D018570:Risk Assessment / Q000379:methods; D012307:Risk Factors