The FORE 10-Year Fracture Risk Calculator™ (FORE FRC) Version 2.0 published 12/4/2012 estimates 10-year fracture risk for postmenopausal women and men age 45 and older who are not receiving treatment for osteoporosis. The FORE FRC is a valuable tool for use in counseling patients about the prevention and treatment of osteoporosis because of its patient friendly graphic display of the risk results comparing it to the expected risk, and categorizing it as low, medium, or high.
To understand what has been changed in the version 2.0 FORE FRC and why, please review our recent changes.
For more information about the FORE FRC and fracture risk, please see our FAQ.
If you have more questions about the new 2012 version, call us at 888-266-3015 or email firstname.lastname@example.org.
The FORE FRC closely aligns to the United States FRAX™ from the World Health Organization, with few exceptions. The FORE FRC and FRAX model use the same input variables, the same base fracture rates, and the same relative risks (multipliers).2 Both can be applied to men and 4 different ethnicities. Both output the 10-year risk of hip and any one of 4 fractures (hip, wrist, humerus, clinical spine). Thus, users of these models should get very similar but not identical results.
Because the FORE FRC does not build in the same "mortality offset" found in FRAX, FRC will yield higher rates of fracture in women with conditions associated with shorter life expectancy (e.g. age over 80 years, very low BMD, very low BMI, etc.) Our assumption is that doctors who calculate fracture risk from BMD and who are considering osteoporosis drug therapies assume at least a 10 year life expectancy in their patients.
Both the FORE FRC and FRAX™ models aim to keep the model data entry simple and in an easily understood format by using risk factors whose individual contributions to fracture risk have been estimated in large epidemiologic studies using multivariable models; the risk factors are the same as those used in cost-effectiveness analyses.5
This model is based on fracture rates in untreated women and does not account for osteoporosis treatment effects. Estimates of the fracture risk reduction from regular long-term bisphosphonate therapy are in the order of 25-35%.5,6,10 Recently, Leslie et al11, on the basis of a large population study, conclude that "the FRAX tool can be used to predict fracture probability in women currently or previously treated for osteoporosis". In that study, bisphosphonate use did not substantially change the categorization of women in the population to low, medium, or high risk.
The cost effectiveness study (Tosteson ANA, et al. Osteoporos Int 2008; 19:437-47) was based on Rochester MN population fracture rates and used the WHO model for fracture risks. The primary outcome studied was hip fracture with a secondary outcome as any one of four fractures (vertebra, femur, wrist, or humerus). The researchers looked at a 10-year window and assumed a 35% risk reduction from treatment. Based on those assumptions, the authors determined that HIGH RISK was defined as a 3% chance of hip fracture in the next 10 years or a 20% chance of any of four other fractures.
Based on the cost effectiveness study, the National Osteoporosis Foundation has published treatment guidelines summarized below.
Consider pharmacological therapies based on:
- A vertebral or hip fracture
- A hip or spine T-score below -2.5
- Low bone mineral density (BMD) and a FRAX 10-year risk at least 3% for hip or 20% of any of 4-fractures.
- Patient's preferences
Bruce Ettinger, MD has published an extensive description of the FRC and FRAX models and their use in clinical practice in the journal Menopause (download PDF, 1.0 MB).
- Ettinger B, Black DM, Dawson-Hughes B, et al. Updated fracture incidence rates for the US version of FRAX. Osteoporos Int 21:25-33, 2010.
- Ettinger B, Liu H, Blackwell T, et al. Validation of FRC, a fracture risk assessment tool, in a cohort of older men: The Osteoporotic Fractures in Men Study. J Clin Densitom 2012 [epub ahead of print]
- Benichou J. A computer program for estimating individualized probabilities of breast cancer. Comput Biomed Res 1993; 26:373-82.
- Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. Executive Summary of the third report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). JAMA 2001; 285:2486-97.
- Tosteson ANA, Melton II LJ, Dawson-Hughes B, Baim S, Favus MJ, Khosla S, Lindsay RL. Cost-effective osteoporosis treatment thresholds: the United States perspective. Osteoporos Int 2008; 19:437-47.
- Dawson-Hughes B, Tosteson ANA, Melton III LJ, Baim S, Favus ML, Khosla S, Lindsay RL. Implications of absolute fracture risk assessment for osteoporosis practice guidelines in the USA. Osteoporos Int 2008; 19:449-58.
- Ettinger B, Hillier TA, Pressman AR, Che M, Hanley DA. Simple computer model for calculating 5-year osteoporotic fracture risk in postmenopausal women. J Women's Health 2005; 14:159-171.
- Seeley DG, Browner WS, Nevitt MC, Genant HK, Scott JC, Cummings SR. Which fractures are associated with low appendicular bone mass in elderly women? Study of Osteoporotic Fractures Research Group. Ann Intern Med 1991; 115:837-42.
- Ray NF, Chan JK, Thamer M, Melton LJ 3rd. Medical expenditures for the treatment of osteoporotic fractures in the United States in 1995: report from the National Osteoporosis Foundation. J Bone Miner Res 1997; 12:24-35.
- Kanis JA, Borgstrom F, Zethraeus N, Johnell O, Oden A, Jonsson B. Intervention thresholds for osteoporosis in the UK. Bone 2005; 36: 22-32.
- Leslie WD, Lix LM, Johansson H, et al. Does osteoporosis therapy invalidate FRAX for fracture prediction? J Bone Miner Res 2012; doi 10.1002/jbmr.1582 [epub ahead of print]
- Siminoski K, Leslie WD, Frame H, et al. Recommendations for bone mineral density reporting in Canada. Can Assoc Radiol J 2005; 56:178-88.
- Hux JE, Naylor CD. Communicating the benefits of chronic preventive therapy: does the format of efficacy data determine patients' acceptance of treatment? Med Decis Making 1995; 15:152-7.