Frailty Predicts Incident Osteoporotic Fractures in Veterans with Rheumatoid Arthritis.

OBJECTIVE

Rheumatoid arthritis (RA) is associated with an increased risk of frailty and osteoporosis, but the relationship between frailty and incident osteoporotic fractures in RA is underexplored.

METHODS

Data were from the Veterans Affairs (VA) Rheumatoid Arthritis Registry. Frailty was measured using the VA Frailty Index (VAFI). Incident osteoporotic fractures were identified by administrative codes and validated by physician chart review. Multivariable Cox proportional hazard models quantified the relationship between frailty category and incident osteoporotic fracture. Models adjusted for baseline age, sex, race, anti-CCP positivity, prior fracture, body mass index, smoking status, disease duration, disease activity, prednisone use, and RA and osteoporosis medication use. Secondary analyses evaluated the relationships between each individual VAFI components and incident fracture.

RESULTS

Participants (n = 2,912) had a mean age of 64.3 (11.0) years, 88% were men, 76% were White, and 248 (9%) had incident osteoporotic fractures. At baseline, 30% were robust, 38% prefrail, 18% mildly frail, 7% moderately frail, and 3% severely frail. Baseline frailty levels were associated with a significant stepwise increased risk in osteoporotic fracture risk when robust was used as the reference category (mild: adjusted hazard ratio [aHR] 2.02, 95% confidence interval [CI] 1.37-2.97; moderate: aHR 2.99, 95% CI 1.83-4.87; severe: aHR 4.67, 95% CI 2.41-9.03; P value for trend <0.001). Functional deficits were the three of the top five contributors to the relationship between frailty and incident fracture: failure to thrive (aHR 4.57, 95% CI 1.10-18.94), muscular issues (aHR 2.42, 95% CI 1.69-3.4), and gait abnormality (aHR 2.01, 95% CI 1.39-2.93; all P < 0.05).

CONCLUSION

Frailty is associated with an increased risk of incident osteoporotic fracture in RA. Comparing the predictive ability of VAFI with other established fracture prediction models is needed to determine if it can be deployed to improve osteoporosis screening and treatment in this high-risk population.