Dr Tuan V NGUYEN Bone and Mineral Research Program, Garvan Institute of Medical Research, Sydney NSW Mapping Translational Research into Individualised Prognosis of Fracture Risk From the age of 60, one in two women and one in four men will sustain a fracture during their remaining lifetime [1]. During the past three decades, several clinical risk factors have been shown to be associated with fracture risk (Table 1*), with modest strengths of association (Table 2*). A major priority in osteoporosis research at present is the translation of these risk factors into simple and accurate prognostic models to identify individuals who are at high risk of fractures in the future and to treat them appropriately so that their fracture risk will be reduced [2 ]. This presentation argues that an individualized risk assessment model based on multiple risk factors is superior to one based on threshold of bone mineral density (BMD) values. The assessment of fracture risk has until now been largely based on the measurement of bone mineral density (BMD) and a history of prior fracture. This is logical, since there is a strong and graded association between BMD and the risk of fracture [3-5 ], such that the magnitude of the association is equivalent to that of between blood pressure and cardiovascular disease. Furthermore, a history of postmenopausal fracture is also a strong risk factor of subsequent fracture [6]. More importantly, the effect of prior fracture on subsequent fracture is independent of BMD. Measurement of BMD is used to make a diagnosis of osteoporosis. The World Health Organization recommends that if a woman has a BMD measurement less than or equal to 2.5 standard deviations below the young adult standard (a T score of < 2.5) the diagnosis of osteoporosis can be made. Furthermore, women with BMD between 1 and 2.5 SD below average (T-score = -1 to -2.5) are said to have osteopenia [7]. Using these criteria, the prevalence of osteoporosis among postmenopausal Asian populations ranged between 10% and 21%. Although the T-score criteria were criticized as a flawed approach [8], they have been widely used in clinical practice to initiate treatment. Indeed, the National Osteoporosis Foundation guidelines recommend treatment in the following clinical situations: women with T-scores below -2 with no risk factors; women T-scores below -1.5 with one or more risk factors for fracture (including a prior fracture); and women with a prior vertebral or hip fracture [9]. Australian experts suggested that treatment should be initiated for any postmenopausal woman with osteoporosis or a prior fracture [10]. It has now been evident that although low BMD (eg osteoporosis) is the best predictor of fracture risk, it can not account for all fractures in the elderly population. Even at the lowest BMD range, only some individuals will sustain a fracture; on the other hand, a high BMD does not confer total protection against a fracture. Indeed, in individuals aged 60+ years, 55% and 74% of fracture cases occurred in nonosteoporotic women and men, respectively [11]. *Note: Where is refers to table see appendix
As a result, treatment of individuals with a BMD-based threshold (eg osteoporosis) can reduce only a modest number of fractures in the general population. Therefore, important changes in thinking are needed for that majority of individuals whose BMD measurements are at or near, on both sides, the current threshold of osteoporosis. The risk of fracture is directly related to BMD at all levels, and there is no threshold value for BMD that accurately separates those who will from those who will not sustain a fracture. However, clinicians often practise as if it were possible to dichotomize patients into osteoporosis and non-osteoporosis by applying a threshold. This practice perhaps resulted from and shaped by randomized clinical trials that have included patients on the basis of osteoporosis and the presence of a pre-existing vertebral fracture. It should be recognized that BMD, like other risk factors, provides quantitative and not qualitative information of fracture. Osteoporosis or low BMD is only one of many risk factors of fracture. At any given level of BMD, fracture risk varied widely in relation to the burden of other risk factors (some are modifiable), such as advancing age, gender, genetics, family history of fracture, increased bone loss, low body weight, fall, and smoking behavior. Thus, for any one individual, the likelihood of fracture or re-fracture will occur depends on a combination of those risk factors (Figure 1*). This means that two individuals, both with osteoporosis, can have different risks of fracture because they have different non-bmd risk profile, and likewise, an osteoporotic individual can have the same risk of fracture as a nonosteoporotic individual. It seems logical that the need to apply therapy to an individual should be based on the absolute risk of fracture or risk of post-fracture mortality for the individual, and not by any particular BMD level. In the context of fracture, absolute risk is the expected incidence of fractures in a specific group, or by extension, the actual odds for an individual to sustain a fracture within a specified period. An individual s absolute risk of fracture is the summation of the individual s entire constellation of risk factors, which can be estimated with considerable precision. Recently, we have developed a number of prognostic models, in which an individual s multiple risk factors are simultaneously considered in a multivariable model and represented by a nomogram [12-13]. An advantage of this nomogram-based approach is that it treats all continuous risk factors in their original units of measurement, and as a result, it obviates the need for grouping individuals by some arbitrary thresholds which is inefficient and has poor predictive power. The use of continuous measurements and multiple risk factors increases the uniqueness of an individual and allows the risk of fracture to be individualized. Thus, the nomogram-based model recognizes the fact that there are different ways two individuals can attain the same risk level. For example, a 60 year-old woman with BMD T-score=-2.5 and a history of fracture is predicted to have the same 5-year risk of fracture as an 80 year-old woman with a T-score=-1 without a previous fracture (Figure 2*). These nomograms have also been implemented in a dedicated website: www.fractureriskcalculator.com. *Note: Where is refers to table see appendix 2
The individualization of fracture risk can help select patients suitable for intervention. The critical question of who should be treated can only be answered by a complete evaluation of an individual s risk profile, and to this end, the nomogram-based estimate can be helpful. This leads to the need to set absolute risk levels that treatment can be cost-effective. In a recent analysis, it was suggested that treatment is cost-effective (based on the criteria of 30,000 per quality-adjusted life year gained) if an individual s 10-year risk of hip fracture is between 1.2% and 9.0%, dependent on age [14]. Results from several randomized clinical trials (eg alendronate, risedronate, raloxifene, strontium and PTH) indicate that the number of patients needed to be treated (NNT) to prevent one vertebral fracture compared to the control ranged between 8 and 83. For hip fracture, the NNT ranged between 91 and 250. The large variability in the NNTs among trials was due to the variability in fracture rates among the study samples, despite the fact that patients were selected on the basis of having osteoporosis and/or a prevalent vertebral fracture. However, the variability is expected given the multiple risk factors that affect the incidence of fractures. In the presence of such variability, selecting patients based on their absolute risk of fracture (rather than based on a BMD threshold value) may improve the consistency of therapeutic efficacy and efficiency of trials. Although trials specifically testing the efficacy of multivariable risk based therapy have not been done, it seems likely that such an approach would prove more cost-effective and would yield a more consistent NNT. Accurate estimates of fracture risk are critically important for informed decision-making. Since fracture risk is determined by multiple factors, any unidimensional risk assessment is unlikely to be helpful. A multivariable-based nomogram can be an effective tool for individualizing short-term and long-term absolute risks of fracture, which can help patient counseling and selecting appropriate patients for intervention to maximize the benefit of fracture reduction in the general population. 3
References Nguyen ND, Ahlborg HG, Center JR, Eisman JA, Nguyen TV. Residual lifetime risk of fractures in women and men. J Bone Miner Res. 2007;22(6):781-8. Raisz LG. Clinical practice. Screening for osteoporosis. N Engl J Med. 2005;353(2):164-71. Johnell O, Kanis JA, Oden A, Johansson H, De Laet C, Delmas P, Eisman JA, Fujiwara S, Kroger H, Mellstrom D, Meunier PJ, Melton LJ 3rd, O'Neill T, Pols H, Reeve J, Silman A, Tenenhouse A. Predictive value of BMD for hip and other fractures. J Bone Miner Res. 2005; 20(7):1185-94. Nguyen ND, Pongchaiyakul C, Center JR, Eisman JA, Nguyen TV. Identification of high-risk individuals for hip fracture: a 14-year prospective study. J Bone Miner Res. 2005;20(11):1921-8. Leslie WD, Lix LM, Tsang JF, Caetano PA; Manitoba Bone Density Program. Single-site vs multisite bone density measurement for fracture prediction. Arch Intern Med. 2007;167(15):1641-7. Kanis JA, Johnell O, De Laet C, Johansson H, Oden A, Delmas P, Eisman J, Fujiwara S, Garnero P, Kroger H, McCloskey EV, Mellstrom D, Melton LJ, Pols H, Reeve J, Silman A, Tenenhouse A. A meta-analysis of previous fracture and subsequent fracture risk. Bone. 2004;35(2):375-82. Kanis JA, Melton, LJ, 3rd, Christiansen, C, Johnston, CC, Khaltaev, N (1994) The diagnosis of osteoporosis. J Bone Miner Res 9:1137-1141. Wasnich RD. Consensus and the T-score fallacy. Clin Rheumatol 1997; 16:337-339. National Osteoporosis Foundation Physician s Guide to Prevention and Treatment of Osteoporosis. In. National Osteoporosis Foundation, Washington DC. Seeman E, Eisman, JA. Treatment of osteoporosis: why, whom, when and how to treat. The single most important consideration is the individual's absolute risk of fracture. Med J Aust 2004; 180:298-303. Nguyen ND, Eisman JA, Center JR, Nguyen TV. Risk factors for fracture in nonosteoporotic men and women. J Clin Endocrinol Metab. 2007;92(3):955-62. Nguyen ND, Frost SA, Center JR, Eisman JA, Nguyen TV. Development of a nomogram for individualizing hip fracture risk in men and women. Osteoporos Int. 2007;18(8):1109-17. Nguyen ND, Frost SA, Center JR, Eisman JA, Nguyen TV. Development of Development of Prognostic Nomograms for Individualizing 5-year and 10-year Risks of Fracture. Osteoporos Int 2008 Mar 7. Kanis JA, Borgstrom F, Zethraeus N, Johnell O, Oden A, Jonsson B. Intervention thresholds for osteoporosis in the UK. Bone. 2005;36(1):22-32. 4
Table 1. Risk factors for osteoporotic fracture in postmenopausal women Non-modifiable risk factors A history of fracture as an adult A family history of fracture (first-degree relative) Being caucasian Advanced age Being woman Dementia Potentially modifiable risk factors Cigarette smoking Low body weight (<65 kg) Estrogen deficiency (early menopause < age 45, bilateral ovariectomy, prolonged premenopausal amenorrhea > 1 year) Low calcium intake Excessive alcohol intakes Impaired vision Multiple falls Low levels of physical activity Poor health/frailty Notes: The 4 risk factors highlighted in bold-faced letters are identified as major risk factors of hip fracture. Reference: Eddy DM, Johnston CC, Cummings SR, Dawson-Hughes B, Lindsay R, Melton LJ III and Slemenda CW. Osteoporosis: review of the evidence for prevention, diagnosis and treatment and cost-effectiveness analysis. Osteoporosis Int 1998;8:S1-S88. 5
Table 2. Magnitude of association between major risk factors and fracture: summary from meta-analyses Risk ratio and 95% confidence interval associated with Risk factor Any fracture Osteoporotic fracture Hip fracture BMD (per SD) 1 1.45 (1.39 1.51) 1.55 (1.47 1.62) 2.07 (1.91 2.24) Prior fracture 2 1.77 (1.64 1.91) 1.76 (1.60 1.93) 1.62 (1.30 2.01) Family history of fx 3 1.19 (1.09 1.30) 1.22 (1.10 1.34) 1.48 (1.18 1.85) Corticosteroid use 4 1.57 (1.37 1.80) 1.66 (1.42 1.92) 2.25 (1.60 3.15) Current smoking 5 1.13 (1.01 1.25) 1.13 (1.00 1.28) 1.60 (1.27 2.02) Body mass index 6 0.98 (0.90 1.08) a a 1.02 (0.92 1.13) a 1.42 (1.23 1.65) 1.01 (0.91 1.11) b 0.96 (0.86 1.08) b 1.00 (0.82 1.21) b Milk intake 7 NA 1.06 (0.95 1.19) 1.10 (0.83 1.47) 1 Johnell et al, J Bone Miner 2005; 2 Kanis et al, Bone 2004; 3 Kanis et al, Bone 2004; 4 Kanis et al, J Bone Miner Res 2004; 2 Kanis et al, Osteoporosis Int 2005; 6 De Laet et al, Osteoporosis Int 2005; 7 Kanis et al, Osteoporosis Int 2004. a :These are risk ratios were calculated for individuals with BMI = 20 comparing to those with BMI = 25 (as the reference level); b :These are risk ratios were calculated for individuals with BMI = 30 comparing to the reference level. All risk ratios for prior fracture, family history (first degree relative), corticosteroid use, current smoking, BMI and milk intake were adjusted for BMD. 6
Figure 1: Incidence of hip fracture (per 1000 person-years) stratified by femoral neck BMD T-score and number of risk factors. For any given BMD level, the incidence of hip fracture increases exponetially with the number of risk factors. Reference: Nguyen ND, Pongchaiyakul C, Center JR, Eisman JA, Nguyen TV. Identification of high-risk individuals for hip fracture: a 14-year prospective study. J Bone Miner Res. 2005;20(11):1921-8 7
Figure 2: Nomogram for predicting the 5-year and 10-year probability of hip fracture for a woman. Instruction for usage: Mark the age of an individual on the Age axis and draw a vertical line to the Point axis to determine how many points toward the probability of hip fracture the individual receives for his/her age value. Repeat the process for each additional risk factor. Sum the points of the risk factors. Locate the final sum on the Total points axis. Draw a vertical line down to the 5-year or 10-year risk line to find the individual s probability of sustaining a hip fracture within next 5 or 10 years. Example: Mrs. Smith, 70 years old, has a BMD T-score of -2.5, had a prior fracture and a fall in the past 12 months; her points for age is approximately 10, her BMD points is 65; prior fracture point is 8 and fall point is 3. Her total points is therefore 10+65+8+3=86, and her probability of having a hip fracture is around 0.091 in the next 5 years and 0.174 in the next 10 years. In other words, in 100 women like her, one would expect 9 and 17 of them will have a hip fracture in the next 5 years and next 10 years, respectively. Reference: Nguyen ND, Frost SA, Center JR, Eisman JA, Nguyen TV. Development of a nomogram for individualizing hip fracture risk in men and women. Osteoporos Int. 2007;18(8):1109-17 8