Performance of the Expanded Cardiovascular Risk Prediction Score for Rheumatoid Arthritis in a geographically distant National Register-based cohort: an external validation

Does Adding Single-Nucleotide Polymorphisms to Risk Algorithms Improve Cardiovascular Disease Risk Prediction in Rheumatoid Arthritis? An Internal and External Validation of a Clinical Risk Score

OBJECTIVE

Current risk algorithms do not accurately predict cardiovascular disease (CVD) risk in rheumatoid arthritis (RA). An area of interest is that of single-nucleotide polymorphisms (SNPs), of which several have been associated with CVD in the general population. We investigated whether these SNPs are associated with CVD in RA and whether SNPs could improve CVD risk prediction in RA.

METHODS

Sixty SNPs were genotyped in 353 patients with RA. Logistic and Cox regression analyses were performed to identify SNPs that were associated with CVD (n = 99). A prediction model with clinical variables was made. SNPs were added to investigate the additional predictive value. Both models were internally validated. External validation was done in a separate cohort (n = 297).

RESULTS

rs3184504, rs4773144, rs12190287, and rs445925 were significantly associated with new CVD. The clinical prediction model consisted of age, sex, body mass index, systolic blood pressure, high-density lipoprotein cholesterol (HDLc), and creatinine, with an area under the curve (AUC) of 0.74 (P = 0.03). Internal validation resulted in an AUC of 0.76 (P < 0.01). A new model was made including SNPs and resulted in a model with rs17011666 and rs801426, age, total cholesterol, and HDLc, which performed slightly better with an AUC of 0.77 (P < 0.01). External validation resulted in a good fit for the clinical model, but a poor fit for the SNP model.

CONCLUSION

Several SNPs were associated with CVD in RA. Risk prediction slightly improved after adding SNPs to the models, but the clinical relevance is debatable. However, larger studies are needed to determine more accurately the additional value of these SNPs to CVD risk prediction algorithms.

External validation of a multi-biomarker-based score for predicting risk of cardiovascular disease in patients with rheumatoid arthritis

BACKGROUND

A multi-biomarker disease activity (MBDA)-based cardiovascular disease (CVD) risk score was developed and internally validated in a Medicare cohort to predict 3-year risk for myocardial infarction (MI), stroke or CVD death in patients with rheumatoid arthritis (RA). It combines the MBDA score, leptin, MMP-3, TNF-R1, age and four clinical variables. We are now externally validating it in a younger RA cohort.

METHODS

Claims data from a private aggregator were linked to MBDA test data to create a cohort of RA patients ≥18 years old. A univariable Cox proportional hazards regression model was fit using the MBDA-based CVD risk score as sole predictor of time-to-a-CVD event (hospitalized MI or stroke). Hazard ratio (HR) estimate was determined for all patients and for clinically relevant subgroups. A multivariable Cox model evaluated whether the MBDA-based CVD risk score adds predictive information to clinical data.

RESULTS

49,028 RA patients (340 CVD events) were studied. Mean age was 52.3 years; 18.3% were male. HR for predicting 3-year risk of a CVD event by the MBDA-based CVD risk score in the full cohort was 3.99 (95% CI: 3.51-4.49, p = 5.0×10-95). HR were also significant for subgroups based on age, comorbidities, disease activity, and drug use. In a multivariable model, the MBDA-based CVD risk score added significant information to hypertension, diabetes, tobacco use, history of CVD, age, sex and CRP (HR = 2.27, p = 1.7×10-7).

CONCLUSION

The MBDA-based CVD risk score has been externally validated in an RA cohort that is younger than and independent of the Medicare cohort that was used for development and internal validation.

Biomarkers of Cardiovascular Risk in Patients With Rheumatoid Arthritis: Results From the TARGET Trial

Cardiovascular disease remains an important comorbidity in patients with rheumatoid arthritis (RA), but traditional models do not accurately predict cardiovascular risk in patients with RA. The addition of biomarkers could improve prediction.

METHODS AND RESULTS

The TARGET (Treatments Against RA and Effect on FDG PET/CT) trial assessed whether different treatment strategies in RA differentially impact cardiovascular risk as measured by the change in arterial inflammation on arterial target to background ratio on fluorodeoxyglucose positron emission tomography/computed tomography scans conducted 24 weeks apart. A group of 24 candidate biomarkers supported by prior literature was assessed at baseline and 24 weeks later. Longitudinal analyses examined the association between baseline biomarker values, measured in plasma EDTA, and the change in arterial inflammation target to background ratio. Model fit was assessed for the candidate biomarkers only, clinical variables only, and models combining both. One hundred nine patients with median (interquartile range) age 58 years (53-65 years), RA duration 1.4 years (0.5-6.6 years), and 82% women had biomarkers assessed at baseline and follow-up. Because the main trial analyses demonstrated significant target to background ratio decreases with both treatment strategies but no difference across treatment groups, we analyzed all patients together. Baseline values of serum amyloid A, C-reactive protein, soluble tumor necrosis factor receptor 1, adiponectin, YKL-40, and osteoprotegerin were associated with significant change in target to background ratio. When selected candidate biomarkers were added to the clinical variables, the adjusted R2 improved from 0.20 to 0.33 (likelihood ratio P=0.0005).

CONCLUSIONS

A candidate biomarker approach identified several promising biomarkers that associate with baseline and treatment-associated changes in arterial inflammation in patients with RA. These will now be tested in an external validation cohort.

Are JAKis more effective among elderly patients with RA, smokers and those with higher cardiovascular risk? A comparative effectiveness study of b/tsDMARDs in Sweden

OBJECTIVES

To investigate whether the relative effectiveness of janus kinase inhibitors (JAKis) versus tumour necrosis factor inhibitors (TNFi) or other biological disease-modifying antirheumatic drugs in rheumatoid arthritis differ by the presence or absence of risk factors for cardiovascular (CV) disease, age, sex and smoking.

METHODS

Through Swedish registers, we identified 13 493 individuals with 3166 JAKi, 5575 non-TNFi and 11 286 TNFi treatment initiations 2016-2022. All lines of therapy were included, with the majority in second line or higher. Treatment response was defined as the proportion reaching European Alliance of Associations for Rheumatology (EULAR) good response and Clinical Disease Activity Index (CDAI) remission, respectively, within 6 months. Crude percentage point differences in these proportions (JAKis, and non-TNFis, vs TNFis) overall and by risk factors were observed, and adjusted for confounders using linear regression models. Predicted probabilities of response and remission were estimated from adjusted Poisson models, and presented across CV risk and age.

RESULTS

Overall, adjusted percentage point differences indicated higher response (+5.0%, 95% CI 2.2% to 7.9%) and remission (+5.8%, 95% CI 3.2% to 8.5%) with JAKis versus TNFis. The adjusted percentage point differences for response in those above 65, at elevated CV risk, and smokers were +5.9% (95% CI 2.7% to 9.0%), +8.3% (95% CI 5.3% to 11.4%) and +6.0% (95% CI 3.3% to 8.7%), respectively. The corresponding estimates for remission were +8.0% (95% CI 5.3% to 10.8%), +5.6% (95% CI 3.0% to 8.2%) and +7.6% (95% CI 5.5% to 9.7%).

CONCLUSIONS

As used in clinical practice, response and remission at 6 months with JAKis are higher than with TNFi. Among patients with risk factors of concern, effectiveness is similar or numerically further increased. For individualised benefit-to-risk ratios to guide treatment choice, safety and effectiveness in specific patient segments should be considered.

Clinical prediction models of rheumatoid arthritis and its complications: focus on cardiovascular disease and interstitial lung disease

Rheumatoid arthritis (RA) is a chronic, systemic, autoimmune disease of unknown etiology with erosive, symmetric polyarthritis as the main clinical manifestations. Its basic pathological changes are the formation of synovitis, and patients gradually develop destruction of articular cartilage destruction and bone erosion, which eventually leads to joint deformity, disability, and various extra-articular manifestations. Clinical prediction models (CPMs), also known as risk prediction models or risk scores, are mathematical formulas used to estimate the probability that a given individual will have a disease or an outcome in the future. The models are mainly divided into two categories: diagnostic models and prognostic models, which can be used to provide information on disease diagnosis or prognosis to help make better medical decisions. Currently, there is no cure for RA, but effective early diagnosis and treatment are crucial for limiting the severity of the disease and preventing the occurrence and development of complications. This paper reviews the CPMs associated with RA and its related complications, including cardiovascular disease (CVD) and interstitial lung disease (ILD), in order to provide reference and evidence for the early diagnosis and treatment of these diseases and personalized medicine for patients. In addition, the possible pathogenesis and risk factors of these comorbidities are summarized, and possible directions for future related research are prospected.

Divergence of Cardiovascular Biomarkers of Lipids and Subclinical Myocardial Injury Among Rheumatoid Arthritis Patients With Increased Inflammation

OBJECTIVE

Patients with rheumatoid arthritis (RA) are 1.5 times more likely to develop cardiovascular disease (CVD) attributed to chronic inflammation. A decrease in inflammation in patients with RA is associated with increased low-density lipoprotein (LDL) cholesterol. This study was undertaken to prospectively evaluate the changes in lipid levels among RA patients experiencing changes in inflammation and determine the association with concomitant temporal patterns in markers of myocardial injury.

METHODS

A total of 196 patients were evaluated in a longitudinal RA cohort, with blood samples and high-sensitivity C-reactive protein (hsCRP) levels measured annually. Patients were stratified based on whether they experienced either a significant increase in inflammation (an increase in hsCRP of ≥10 mg/liter between any 2 time points 1 year apart; designated the increased inflammation cohort [n = 103]) or decrease in inflammation (a decrease in hsCRP of ≥10 mg/liter between any 2 time points 1 year apart; designated the decreased inflammation cohort [n = 93]). Routine and advanced lipids, markers of inflammation (interleukin-6, hsCRP, soluble tumor necrosis factor receptor II), and markers of subclinical myocardial injury (high-sensitivity cardiac troponin T [hs-cTnT], N-terminal pro-brain natriuretic peptide) were measured.

RESULTS

Among the patients in the increased inflammation cohort, the mean age was 59 years, 81% were women, and the mean RA disease duration was 17.9 years. The average increase in hsCRP levels was 36 mg/liter, and this increase was associated with significant reductions in LDL cholesterol, triglycerides, total cholesterol, apolipoprotein (Apo B), and Apo A-I levels. In the increased inflammation cohort at baseline, 45.6% of patients (47 of 103) had detectable circulating hs-cTnT, which further increased during inflammation (P = 0.02). In the decreased inflammation cohort, hs-cTnT levels remained stable despite a reduction in inflammation over follow-up. In both cohorts, hs-cTnT levels were associated with the overall estimated risk of CVD.

CONCLUSION

Among RA patients who experienced an increase in inflammation, a significant decrease in routinely measured lipids, including LDL cholesterol, and an increase in markers of subclinical myocardial injury were observed. These findings highlight the divergence in biomarkers of CVD risk and suggest a role in future studies examining the benefit of including hs-cTnT for CVD risk stratification in RA.

Cardiovascular Risk in Rheumatoid Arthritis and Mechanistic Links: From Pathophysiology to Treatment

Rheumatoid arthritis (RA) is an autoimmune inflammatory arthritis. Inflammation, however, can spread beyond the joints to involve other organs. During the past few years, it has been well recognized that RA associates with increased risk for cardiovascular (CV) disease (CVD) compared with the general population. This seems to be due not only to the increased occurrence in RA of classical CVD risk factors and comorbidities like smoking, obesity, hypertension, diabetes, metabolic syndrome, and others but also to the inflammatory burden that RA itself carries. This is not unexpected given the strong links between inflammation and atherosclerosis and CVD. It has been shown that inflammatory cytokines which are present in abundance in RA play a significant role in every step of plaque formation and rupture. Most of the therapeutic regimes used in RA treatment seem to offer significant benefits to that end. However, more studies are needed to clarify the effect of these drugs on various parameters, including the lipid profile. Of note, although pharmacological intervention significantly helps reduce the inflammatory burden and therefore the CVD risk, control of the so-called classical risk factors is equally important. Herein, we review the current evidence for the underlying pathogenic mechanisms linking inflammation with CVD in the context of RA and reflect on the possible impact of treatments used in RA.

Derivation and internal validation of a multi-biomarker-based cardiovascular disease risk prediction score for rheumatoid arthritis patients

Background

Rheumatoid arthritis (RA) patients have increased risk for cardiovascular disease (CVD). Accurate CVD risk prediction could improve care for RA patients. Our goal is to develop and validate a biomarker-based model for predicting CVD risk in RA patients.

Methods

Medicare claims data were linked to multi-biomarker disease activity (MBDA) test results to create an RA patient cohort with age ≥ 40 years that was split 2:1 for training and internal validation. Clinical and RA-related variables, MBDA score, and its 12 biomarkers were evaluated as predictors of a composite CVD outcome: myocardial infarction (MI), stroke, or fatal CVD within 3 years. Model building used Cox proportional hazard regression with backward elimination. The final MBDA-based CVD risk score was internally validated and compared to four clinical CVD risk prediction models.

Results

30,751 RA patients (904 CVD events) were analyzed. Covariates in the final MBDA-based CVD risk score were age, diabetes, hypertension, tobacco use, history of CVD (excluding MI/stroke), MBDA score, leptin, MMP-3 and TNF-R1. In internal validation, the MBDA-based CVD risk score was a strong predictor of 3-year risk for a CVD event, with hazard ratio (95% CI) of 2.89 (2.46–3.41). The predicted 3-year CVD risk was low for 9.4% of patients, borderline for 10.2%, intermediate for 52.2%, and high for 28.2%.

Model fit was good, with mean predicted versus observed 3-year CVD risks of 4.5% versus 4.4%. The MBDA-based CVD risk score significantly improved risk discrimination by the likelihood ratio test, compared to four clinical models. The risk score also improved prediction, reclassifying 42% of patients versus the simplest clinical model (age + sex), with a net reclassification index (NRI) (95% CI) of 0.19 (0.10–0.27); and 28% of patients versus the most comprehensive clinical model (age + sex + diabetes + hypertension + tobacco use + history of CVD + CRP), with an NRI of 0.07 (0.001–0.13). C-index was 0.715 versus 0.661 to 0.696 for the four clinical models.

Conclusion

A prognostic score has been developed to predict 3-year CVD risk for RA patients by using clinical data, three serum biomarkers and the MBDA score. In internal validation, it had good accuracy and outperformed clinical models with and without CRP. The MBDA-based CVD risk prediction score may improve RA patient care by offering a risk stratification tool that incorporates the effect of RA inflammation.

Cardiovascular risk estimation with 5 different algorithms before and after 5 years of bDMARD treatment in rheumatoid arthritis

BACKGROUND

Assessing cardiovascular (CV) risk represents a challenge for clinicians because more variables can impact CV risk. The aim of this study was to evaluate the change of CV risk after 5 years of biological treatment in rheumatoid arthritis (RA) patients and impact of prolonged low disease activity on 5 different CV risk algorithms.

MATERIALS AND METHODS

We estimated the CV risk, at baseline and at 5-year follow-up (FU), with the Systematic COronary Risk Evaluation(SCORE) charts, the algorithm 'Progetto Cuore', the QRISK3-2018 score, the Reynold Risk Score(RRS) and the Expanded Risk Score in RA(ERS-RA). Clinical disease activity index(CDAI) was used to define RA activity. Wilcoxon signed-rank test was used to compare CV risk scores.

RESULTS

In 110 patients with a 5-year FU on biological disease-modifying anti-rheumatic drug treatment, we observed an increase in the 10-year CV risk estimated by SCORE charts [from mean (SD) 0.9% (1.4) to 1.1% (1.5), P < .001], 'Progetto Cuore' [from mean (SD) 5.5% (7.2) to 6.2% (6.8), P < .001], QRISK3-2018 [from mean (SD) 9.3% (10.1) to 11.9% (10.8), P < .001) and RRS [from mean (SD) 5.6% (6.4) to 6.2% (7.5), P < .05], mainly due to age raise. ERS-RA highlighted a significant decrease of estimated CV risk in patients with persistent CDAI ≤ 10[from mean (SD) 9.6% (11.2) to 7.3% (6.4), P < .05], despite age increase and its impact on the CV risk score.

CONCLUSIONS

Algorithms commonly used to estimate 10-year CV risk in RA perform differently. Scores that include specific inflammatory RA-related variables seem to decrease with amelioration of disease activity. Further investigations are warranted to explore the predictive value of their changing over time.

Atherosclerotic cardiovascular disease prevention in rheumatoid arthritis

Patients with rheumatoid arthritis (RA) are at high risk of developing cardiovascular disease (CVD). Inflammation has a pivotal role in the pathogenesis of CVD. RA is an inflammatory joint disease and, compared with the general population, patients with RA have approximately double the risk of atherosclerotic CVD, stroke, heart failure and atrial fibrillation. Although this high risk of CVD has been known for decades, patients with RA receive poorer primary and secondary CVD preventive care than other high-risk patients, and an unmet need exists for improved CVD preventive measures for patients with RA. This Review summarizes the evidence for atherosclerotic CVD in patients with RA and provides a contemporary analysis of what is known and what needs to be further clarified about recommendations for CVD prevention in patients with RA compared with the general population. The management of traditional CVD risk factors, including blood pressure, lipids, diabetes mellitus and lifestyle-related risk factors, as well as the effects of inflammation and the use of antirheumatic medication on CVD risk and risk management in patients with RA are discussed. The main aim is to provide a roadmap of atherosclerotic CVD risk management and prevention for patients with RA.

A Hydrophilic Interaction Liquid Chromatography-Tandem Mass Spectrometry Quantitative Method for Determination of Baricitinib in Plasma, and Its Application in a Pharmacokinetic Study in Rats

Baricitinib, is a selective and reversible Janus kinase inhibitor, is commonly used to treat adult patients with moderately to severely active rheumatoid arthritis (RA). A fast, reproducible and sensitive method of liquid chromatography-tandem mass spectrometry (LC-MS/MS) for the quantification of baricitinib in rat plasma has been developed. Irbersartan was used as the internal standard (IS). Baracitinib and IS were extracted from plasma by liquid-liquid extraction using a mixture of n-hexane and dichloromethane (1:1) as extracting agent. Chromatographic separation was performed using Acquity UPLC HILIC BEH 1.7 µm 2.1 × 50 mm column with the mobile phase consisting of 0.1% formic acid in acetonitrile and 20 mM ammonium acetate (pH 3) (97:3). The electrospray ionization in the positive-mode was used for sample ionization in the multiple reaction monitoring mode. Baricitinib and the IS were quantified using precursor-to-production transitions of m/z 372.15 > 251.24 and 429.69 > 207.35 for baricitinib and IS, respectively. The method was validated according to the recent FDA and EMA guidelines for bioanalytical method validation. The lower limit of quantification was 0.2 ng/mL, whereas the intra-day and inter-day accuracies of quality control (QCs) samples were ranged between 85.31% to 89.97% and 87.50% to 88.33%, respectively. Linearity, recovery, precision, and stability parameters were found to be within the acceptable range. The method was applied successfully applied in pilot pharmacokinetic studies.

Predictive Utility of Cardiovascular Risk Prediction Algorithms in Inflammatory Rheumatic Diseases: A Systematic Review

Objective.

We performed a systematic review of the literature to describe current knowledge of cardiovascular (CV) risk prediction algorithms in rheumatic diseases.

Methods.

A systematic search of MEDLINE, EMBASE, and Cochrane Central databases was performed. The search was restricted to original publications in English, had to include clinical CV events as study outcomes, assess the predictive properties of at least 1 CV risk prediction algorithm, and include patients with rheumatoid arthritis (RA), ankylosing spondylitis (AS), systemic lupus erythematosus (SLE), psoriatic arthritis (PsA), or psoriasis. By design, only cohort studies that followed participants for CV events were selected.

Results.

Eleven of 146 identified manuscripts were included. Studies evaluated the predictive performance of the Framingham Risk Score, QRISK2, Systematic Coronary Risk Evaluation (SCORE), Reynolds Risk Score, American College of Cardiology/American Heart Association Pooled Cohort Equations (PCE), Expanded Cardiovascular Risk Prediction Score for Rheumatoid Arthritis (ERS-RA), and the Italian Progetto CUORE score. Approaches to improve predictive performance of general risk algorithms in patients with RA included the use of multipliers, biomarkers, disease-specific variables, or a combination of these to modify or develop an algorithm. In both SLE and PsA patients, multipliers were applied to general risk algorithms. In studies of RA and SLE patients, efforts to include nontraditional risk factors, disease-related variables, multipliers, and biomarkers largely failed to substantially improve risk estimates.

Conclusion.

Our study confirmed that general risk algorithms mostly underestimate and at times overestimate CV risk in rheumatic patients. We did not find studies that evaluated models for psoriasis or AS, which further demonstrates a need for research in these populations.