FRI0553 DEVELOPMENT AND VALIDATION OF A BIOMARKER-BASED CARDIOVASCULAR RISK PREDICTION SCORE IN RHEUMATOID ARTHRITIS. (2nd June 2020)

• Record Type: Journal Article
• Authors: Curtis, J.; Xie, F.; Crowson, C. S.; Mabey, B.; Flake, D.; Bamford, R.; Chin, C.; Sasso, E.; Hitraya, E.; Ben-Shachar, R.; Gutin, A.; Lanchbury, J.
• Is Part Of: Annals of the rheumatic diseases. Volume 79(2020)Supplement 1
• Page Start: 878
• Page End: 879
• Publication Date: 2020-06-02
• Subjects: Rheumatism -- Periodicals; 616.723005
• DOI: 10.1136/annrheumdis-2020-eular.2350
• Languages: English
• ISSNs: 0003-4967

Abstract

Abstract : Background: Rheumatoid arthritis (RA) patients are at elevated risk for cardiovascular (CV) events, but risk stratification based on CV prediction models is not part of routine rheumatology practice. Objectives: To develop and validate a biomarker-based CV risk prediction model and compare it to alternative risk prediction models. Methods: We constructed a cohort of RA patients - age ≥40 with ≥1 RA diagnosis from a rheumatologist, excluding patients with malignancy, past myocardial infarction (MI) or stroke - by linking Medicare administrative data from 2006-2016 to multi-biomarker disease activity (MBDA) test results obtained as part of routine care. The cohort was split 2:1 to create training and internal validation datasets. The composite CV outcome was MI, stroke or CV death occurring within 3 years. Clinical predictors examined were: age, sex, race, traditional CV risk factors (e.g. diabetes, hypertension, hyperlipidemia, high-risk CV conditions [e.g. angina]), RA-related factors (e.g. glucocorticoid use, MTX, number of prior biologics), adjusted MBDA score 1 and its 12 biomarkers, log-transformed. Backward elimination was used to remove predictors with p ≥0.05. The resulting CV risk score was compared to four prediction models (age+sex; age+sex+CRP; age+sex+diabetes+hypertension+ smoking+high risk CV [±CRP]) in the validation dataset. We evaluated: 1) incremental improvement in the likelihood ratio test (LRT) statistic, 2) discrimination (AUROC), and 3) goodness-of-fit (predicted vs. observed, based on Kaplan-Meier estimates). Validation analyses were prespecified. Results: 30, 751 RA patients with 904 CV events were linked to MBDA test results and eligible for analysis. Patient characteristics were mean (SD) age 68.7 (9.5) years; 23.4% age <65; 82% women. Comorbidities included diabetes (39%), hypertension (78%), smoking (24%) and history of high-risk CV condition (37%). RA-related features included use of glucocorticoids (58%), MTX (60%), TNFi (33%) and other biologics (16%). Mean (SD) MBDA score was 41 (14). The final covariates included in the MBDA-based CV risk score were age, diabetes, hypertension, smoking, history of high-risk CV conditions, adjusted MBDA score, leptin, TNFRI and MMP-3. Median (IQR) of the predicted 3-year CV risk was 3.4% (2.1%, 5.6%). Based on extrapolation to 10-year risk, 9.4% of patients would be considered low, 10.2% borderline, 52.2% intermediate, and 28.2% high risk per 2019 ACC/AHA guidelines. Compared to four simpler CV prediction models, significant improvement in the LRT statistic was observed with the addition of the biomarker-based CV risk score (Figure 1 ). Model fit was good across deciles (Figure 2 ). The AUROC was 0.70. The MBDA-based model reclassified 28.5% of patients vs. the model based on age+sex+diabetes+hypertension +smoking+high risk CV+CRP. Conclusion: A biomarker-based prediction score incorporating a few clinical risk factors appears to have good accuracy to predict CV risk in RA. Additional validation in independent cohorts will help verify its performance characteristics. References: [1] Curtis et al., Rheumatology 2018;58:874. Disclosure of Interests: Jeffrey Curtis Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB, Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB, Fenglong Xie: None declared, Cynthia S. Crowson Grant/research support from: Pfizer research grant, Brent Mabey Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Darl Flake Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Richard Bamford Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Cheryl Chin Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Eric Sasso Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Elena Hitraya Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Rotem Ben-Shachar Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Alexander Gutin Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc., Jerry Lanchbury Shareholder of: Myriad Genetics, Inc., Employee of: Myriad Genetics, Inc.