Accuracy of FRAX® in People With Multiple Sclerosis

Abstract

People with multiple sclerosis (MS) have a higher risk of low bone mineral density (BMD), osteoporosis, and osteoporotic fractures than healthy adults. The Fracture Risk Assessment tool (FRAX ® ) has been reported to underestimate fracture risk in people with MS when BMD is unknown. We tested FRAX performance for people with MS when BMD is known, and determined if MS is a risk factor for fracture independent of FRAX score. Using population-based databases in Manitoba, Canada, we identified people with MS who underwent BMD screening after MS diagnosis (n ¼ 744) and controls matched on age, sex, and first BMD screening date (n ¼ 3721). We calculated FRAX 10-year probabilities at the BMD screening date, and ascertained incident major osteoporotic fractures (MOF). Using Cox proportional hazards modeling we assessed the effect of MS on the hazard of MOF, adjusting for FRAX 10-year probabilities. MS cases had a higher mean FRAX 10-year probability of MOF calculated with BMD (8.32 AE 7.53) than controls (6.98 AE 5.18; p < 0.01). MS increased the risk for MOF after controlling for FRAX 10-year probability without BMD (HR 1.67; 95% confidence interval [CI], 1.29 to 2.16), and after controlling for FRAX individual risk factors (HR 1.45; 95% CI, 1.12 to 1.89). MS remained a risk factor for MOF even when controlling for FRAX 10-year probability of MOF with BMD (HR 1.48; 95% CI, 1.14 to 1.92). The FRAX 10-year probability with and without BMD underestimated the observed 10-year MOF risk in MS cases by 3% to 5%. Calibration improved when secondary osteoporosis was used to calculate FRAX without BMD. Calibration was best when the rheumatoid arthritis input was used to calculate FRAX probability along with BMD. Using secondary osteoporosis or rheumatoid arthritis as proxies for MS improves performance of FRAX and accurately predicts MOF outcomes in those with MS. This provides clinicians with a readily available approach to improve the accuracy of fracture prediction in MS.

Efficiency and Patient-Reported Outcome Measures From Clinic to Home: The Human Empowerment Aging and Disability Program for Digital-Health Rehabilitation

“…Parkinson Disease (PD), Multiple Sclerosis (MS), and Stroke are the more frequent chronic Neurological Diseases (CNDs) that can lead to significant motor and cognitive disability: worldwide data report 2.5 million people with MS (1), 7.9 to 19 individuals with PD per 100,000 person-year (2) and 5.5 million deaths due to stroke in 2016 (3). In recent years, new models of digital health (DH) enabling continuity of care are increasingly explored as new solutions to the long-term patient maintenance.…”

Fracture prediction from FRAX for Canadian ethnic groups: a registry-based cohort study

“…Ten-year probability of a major osteoporotic fracture was calculated using the World Health Organization fracture risk assessment tool, Canadian version (FRAX ® Desktop Multi-Patient Entry, version 3.7) (12,13). Briefly, age, body mass index (BMI), femoral neck BMD and other data required for calculating fracture probability with FRAX were assessed from measurements (height and weight) and information collected directly from subjects through the intake questionnaire which is reviewed at the time of DXA scanning (14). Questionnaire information was supplemented with population-based healthcare data (hospital discharge abstracts, medical claims diagnoses, province-wide retail pharmacy database) as previously described, thereby ensuring complete information in virtually all subjects (15)(16)(17).…”

The Effect of Fracture Recency on Observed 10-Year Fracture Probability: A Registry-Based Cohort Study

“…(24,25) Briefly, age, body mass index (BMI), femoral neck BMD, and other data required for calculating fracture probability with FRAX were assessed from measurements (height and weight) and information collected directly from subjects through the intake questionnaire, which was reviewed at the time of DXA scanning. (26) Questionnaire information was supplemented with population-based health care data (hospital discharge abstracts, medical claims diagnoses, province-wide retail pharmacy database) as previously described, thereby ensuring complete information in virtually all subjects. (27)(28)(29) Prior fracture was included as the traditional FRAX input variable, without considering site or time since fracture.…”