Vinayaka Missions Medical College and Hospital, Department of Orthopaedics, Karaikal, Pondy India
Introduction
Advances in medicine and pharmacology have led to a significant increase in global life expectancy, reflected positively in the growing number of elderly people. However, there is a real concern about the quality of life of these aging adults, and especially, regarding how to adequately prevent and treat the complications inherent to this age group. Among these complications are low-energy fractures, or those that are a consequence of associated pathological complications.1–3 Hip fractures have serious impact on elderly patients, especially the very elderly (over 80 years).3 This issue is relevant due to the high morbidity and mortality, high postoperative disability index, and increasing costs to society with less beneficial results related to treatment.4 These fractures are considered one of the largest public health problems in the world.4 According to American statistics, over 250,000 hip fractures occur each year; it is expected that over the next 30 years, there will be an increase of 100% in the number of cases/year. In Brazil, in 2010, the incidence was 100,000 fractures per year, and the mean mortality one year after the fracture was 30%. Femoral fractures, especially proximal fractures, are among the most relevant.4
Adequate surgical treatment is paramount for good prognosis; the method chosen is directly related to the type of hip fracture, specifically the types of femoral fractures (distal or proximal). Proximal fractures can be divided into two types: intracapsular and extracapsular. The first type includes fractures of the femoral neck, and the second type, transtrochanteric fractures. Both have low-energy trauma as the main etiology, and both have great influence in associated pathologies, such as osteoporosis.5–7
Osteoporosis, undoubtedly the most common of bone diseases, has become a burden of considerable economic significance. Factors such as ethnicity, gender, physical activity, and nutrition influence the maximum bone quality achieved by each individual, but are not the only determining factors for fractures. The specialized literature emphasizes that bone mineral density (BMD), an age-related predictor of fracture, is not always consistent: individuals with very low femoral neck BMD may not present fracture, while those with normal BMD might.8 There may be other relevant variables that determine fractures and especially their types, such as bone anatomy.8,9 Bone geometry of the proximal femur has been studied10 as a potential risk factor, and has been positively associated in the prediction of fracture risk. However, most hip fracture studies do not distinguish the predisposition between the two main types of fracture (femoral neck and transtrochanteric), which in clinical practice would be fundamental, since the surgical approach of choice can be different due to the high rate of hip arthroplasty indication in femoral neck fractures, which in turn has financial repercussions and affects patient recovery in the postoperative period.
Thus, this study is aimed at analyzing the influence of proximal femoral bone geometry in the type of femur fracture presented, by measuring standard pelvic radiographs. Material and methods
This was a prospective, cross-sectional study performed in an orthopedic and trauma service in Brazil between August 10, 2015 and September 8, 2015. The study included 30 radiographs of patients with hip fractures, randomly selected as cases were admitted. The study followed the Declaration of Helsinki and was approved by the internal Ethics Committee (No. 1.221.094).
Radiographs were taken in the anteroposterior view, with the X-ray generator located one meter from the chassis. Patients were placed in a horizontal dorsal recumbent position, with the lower limbs rotated internally at 15?. The inclusion criteria were panoramic radiographs of the hip of patients aged over 60 years, of both genders, with femoral neck and transtrochanteric fractures. Exclusion criteria included radiographs of skeletally immature patients; bilateral hip fracture; and presence of tumor, infectious lesions, or metabolic diseases that could alter the hip and proximal femur anatomy.
After classification and selection, the radiographs were anatomically evaluated, according to the following measures:
• Cervicodiaphyseal angle (CDA): angle between the axis of the femoral neck and the diaphysis.
• Femoral neck width (FNW): distance between cortical lines, at the midpoint of the femoral neck, perpendicular to its axis.
• Hip axis length (HAL): the distance in a straight line between the base of the great trochanter to the end of the femoral head, following the line of the axis of the femoral neck.
• Acetabular tear drop distance (ATD): the distance in a straight line between the acetabular tear drops.
The choice of these measurement indexes was based on previous studies that conducted morphometric analyses of the proximal femur.11 All measurements were made by two blinded examiners using a goniometer (MSD, Europe BVBABelgium). The measurements were collected by manual marking of the aforementioned reference points. It was decided not
use computer programs for measuring, as the process of scanning the radiographs could lead to uneven magnification of the images and thus generate calibration bias, since the system available at this medical center is not digital (Fig. 1).
The Kolmogorov–Smirnov test was used to assess the intrinsic parameters of the sample regarding its normality and distribution. Data were expressed as mean, standard deviation, and percentage (SPSS Statistical Software). The variables were analyzed descriptively through the mean, standard deviation, minimum and maximum values, and 95% confidence intervals. Student’s t-test was used to compare the difference between the means of two variables, and Pearson’s correlation coefficient was used to assess the correlation index. The level of significance was set at 5% (ROSNER, B. Fundamentals of Biostatistics. Boston, PWS Publishers, 2nd ed.) Results
The study included 30 patients, male (n = 6; mean age = 76, SD = 3.48) and female (n = 24; mean age = 77.37, SD = 8.53), that were divided into two large groups of fractures with their respective anatomic evaluations, as shown in Table 1. Parametric evaluation of the collected data In order to establish reliable indexes in the comparisons, the normality of the samples was first determined according to the
Kolmogorov–Smirnov test, i.e., it was determined whether two underlying probability distributions would differ in relation to the normality hypothesis, in any one of the cases. The normality hypothesis was not rejected for the variables investigated with p > 5%.
Then, to characterize possible interference biases between the angles measured by the observers of the study regarding gender and age, Pearson’s correlation test was used. No positive association was observed between the variables, as shown in Table 2.
After determining the normality of the sample distribution and ruling out interference bias, the measurements made by the observers were compared to assess the difference between the types of fractures, represented by the measured angles, and whether these values were correlated. Although there were differences between the mean angles (around 3?–7?), Student’s t-test indicated that they were not significant (Table 3, Figs. 2–5).
In order to establish a correlation between the variables measured according to the type of fracture, Pearson’s correlation test was applied, which showed negativity and low correlation indexes, all of which were non-significant (Table 4, Figs. 6–9).
Study limitations A higher and more representative sampling of the population affected by hip fractures, a study in different groups with associated pathologies, and the addition of a healthy control group would be necessary. Discussion In the present study, it was demonstrated that, although radiography is a good method to evaluate bone structures and predict hip fractures, it was not sensitive enough to capture differences between femoral neck and transtrochanteric
fractures when compared with a healthy control group. No significant geometric difference was observed between the groups studied.
The increased risk of bone fractures due to loss of bone mass during a disease or aging process is a major clinical problem, which leads to an estimate of health costs of around US$ 17 billion in the United States alone.12,13 In addition to the economic burden, non-vertebral fractures, especially those of the hip, are an important cause of morbidity and mortality in the aging population.14,15 Over 4% of patients with pelvic fracture die during hospitalization, and 24% die within a year.16 Thus, concentrated efforts are needed to identify treatment strategies that maintain skeletal health as patients age. However, it is of paramount importance to improve accuracy in identifying those at risk for bone fractures.
BMD measurements are widely used to assess bone mineral status, especially in women; they can account for up to 70% of bone strength. Although studies have demonstrated the correlation between BMD (commonly determined through dual-emission X-ray absorptiometry [DXA]) and fracture risk, predictive models based on DXA alone often present low sensitivity in identifying individuals susceptible to fractures, particularly in women of menopause age and in older populations.2,17
The structural integrity of this tissue in any mechanical loading environment is dependent on the spatial distribution of BMD, size, and shape, as well as the properties of bone material.18,19 In literature, several studies have demonstrated the clinical potential of bone texture analysis through pelvic radiographs in predicting the risk of femoral neck fractures. In a retrospective study, Thevenot et al.10 observed a high intraand interobserver reproducibility and reliability, and concluded that the structural analysis of pelvic radiographs allows the identification of patients with risk of femoral neck fractures. This finding corroborates with studies described in the literature, which suggest that the trabecular texture parameters, especially the entropic parameter, allow the separation of individuals at risk from the control individuals; however, no parameter suggests the ability to differentiate among the types of injury, as was studied in the present study.20–22
One of the great foes in bone injury is osteoporosis, the most common bone disease. It has become a burden of considerable economic significance. Factors such as ethnicity, gender, physical activity, and nutrition influence the maximum bone mass quality achieved by each individual. However, bone mass alone is not a determining factor.3,4 A study by Cummengs et al.22 found that Japanese women had lower BMD than their Caucasian peers; however, the former suffered fewer fractures. Likewise, age and BMI may not be directly related to loss in bone mass.23
Wheeler et al.,8 in their study of the cross-sectional geometry of long bone diaphyses that correlated BMD, BMI, and age, demonstrated that bone strength is significantly higher in obese individuals when compared with those with normal BMI. However, the joint dimensions do not differ appreciably; older individuals with a higher BMI are less likely to develop a fracture than younger individuals with normal BMI. In attempting to establish a risk assessment based on DXA, a multifactor tool was developed to determine hip fracture propensity, a method recommended by the World Health Organization. This tool takes into account different factors (anthropometric variables, medical history, and drug use) to evaluate the ten-year risk of fracture, using clinical risk factors with or without BMD values.24 Nonetheless, this method still has low sensitivity for fracture prediction, since it is improved in a generic way, and cannot reflect the complexity of the personalized evaluation of individuals and/or specific populations.25,26
Different imaging methods such, as peripheral quantitative computed tomography and magnetic resonance imaging (MRI), can be used to obtain three-dimensional geometry and bone architecture in vivo. These methods may provide some relevant information in assessing bone quality.27 However, the limited availability and high cost of these methods has led to the development of other types of lowcost analyses that may be clinically applicable, such as radiographs.
At present, the solution for a low-cost study of bone structures has been conventional radiography. It allows the fractures when compared with a healthy control group. No significant geometric difference was observed between the groups studied.
The increased risk of bone fractures due to loss of bone mass during a disease or aging process is a major clinical problem, which leads to an estimate of health costs of around US$ 17 billion in the United States alone.12,13 In addition to the economic burden, non-vertebral fractures, especially those of the hip, are an important cause of morbidity and mortality in the aging population.14,15 Over 4% of patients with pelvic fracture die during hospitalization, and 24% die within a year.16 Thus, concentrated efforts are needed to identify treatment strategies that maintain skeletal health as patients age. However, it is of paramount importance to improve accuracy in identifying those at risk for bone fractures.
BMD measurements are widely used to assess bone mineral status, especially in women; they can account for up to 70% of bone strength. Although studies have demonstrated the correlation between BMD (commonly determined through dual-emission X-ray absorptiometry [DXA]) and fracture risk, predictive models based on DXA alone often present low sensitivity in identifying individuals susceptible to fractures, particularly in women of menopause age and in older populations.2,17
The structural integrity of this tissue in any mechanical loading environment is dependent on the spatial distribution of BMD, size, and shape, as well as the properties of bone material.18,19
In literature, several studies have demonstrated the clinical potential of bone texture analysis through pelvic radiographs in predicting the risk of femoral neck fractures. In a retrospective study, Thevenot et al.10 observed a high intraand interobserver reproducibility and reliability, and concluded that the structural analysis of pelvic radiographs allows the identification of patients with risk of femoral neck fractures. This finding corroborates with studies described in the literature, which suggest that the trabecular texture parameters, especially the entropic parameter, allow the separation of individuals at risk from the control individuals; however, no parameter suggests the ability to differentiate among the types of injury, as was studied in the present study.20–22
One of the great foes in bone injury is osteoporosis, the most common bone disease. It has become a burden of considerable economic significance. Factors such as ethnicity, gender, physical activity, and nutrition influence the maximum bone mass quality achieved by each individual. However, bone mass alone is not a determining factor.3,4 A study by Cummengs et al.22 found that Japanese women had lower BMD than their Caucasian peers; however, the former suffered fewer fractures. Likewise, age and BMI may not be directly related to loss in bone mass.23
Wheeler et al.,8 in their study of the cross-sectional geometry of long bone diaphyses that correlated BMD, BMI, and age, demonstrated that bone strength is significantly higher in obese individuals when compared with those with normal BMI. However, the joint dimensions do not differ appreciably; older individuals with a higher BMI are less likely to develop a fracture than younger individuals with normal BMI.
In attempting to establish a risk assessment based on DXA, a multifactor tool was developed to determine hip fracture propensity, a method recommended by the World Health Organization. This tool takes into account different factors (anthropometric variables, medical history, and drug use) to evaluate the ten-year risk of fracture, using clinical risk factors with or without BMD values.24 Nonetheless, this method still has low sensitivity for fracture prediction, since it is improved in a generic way, and cannot reflect the complexity of the personalized evaluation of individuals and/or specific populations.25,26
Different imaging methods such, as peripheral quantitative computed tomography and magnetic resonance imaging (MRI), can be used to obtain three-dimensional geometry and bone architecture in vivo. These methods may provide some relevant information in assessing bone quality.27 However, the limited availability and high cost of these methods has led to the development of other types of lowcost analyses that may be clinically applicable, such as radiographs.
At present, the solution for a low-cost study of bone structures has been conventional radiography. It allows the evaluation of the geometry, the structure, and, eventually, the risk of bone fracture. Nonetheless, new prospective studies with geometric measurements are still needed to confirm the clinical capability of the bone texture analysis through this tool, as well as the possibility of predicting and defining risk groups for specific types of hip fractures, especially transtrochanteric and those of the femoral head. Conclusion In the present study, it was demonstrated that although radiography is a good method to evaluate bone structures and predict hip fractures, it was not sensitive enough to capture differences between femoral neck and transtrochanteric fractures when compared with a healthy control group. Further prospective studies are needed to establish parameters capable of measuring such differences.
Conflicts of interest
The authors declare no conflicts of interest
1. Johnell O, Kanis JA, Odén A, Sernbo I, Redlund-Johnell I, Petterson C, et al. Mortality after osteoporotic fractures. Osteoporos Int. 2004;15(1):38–42. 2. Kanis JA. Diagnosis of osteoporosis and assessment of fracture risk. Lancet. 2002;359(9321):1929–36. 3. Schuit SC, van der Klift M, Weel AE, de Laet CE, Burger H, Seeman E, et al. Fracture incidence and association with bone mineral density in elderly men and women: the Rotterdam Study. Bone. 2004;34(1):195–202. 4. Loures FB, Chaoubah A, Oliveira VM, Almeida AM, Campos EM, Paiva EP. Economic analysis of surgical treatment of hip fracture in older adults. Rev Saúde Pública. 2015;49:12. 5. Daniachi D, Santos Netto A, Ono NK, Guimarães RP, Polesello GC, Honda EK. Epidemiology of fractures of the proximal third of the femur in elderly patients. Rev Bras Ortop. 2015;50(4):371–7. 6. Formosa MM, Xuereb-Anastasi A. Biochemical predictors of low bone mineral density and fracture susceptibility in maltese postmenopausal women. Calcif Tissue Int. 2016;98(1):28–41. 7. Palm H, Teixidor J. Proximal femoral fractures: can we improve further surgical treatment pathways? Injury. 2015;46 Suppl. 5:S47–51. 8. Wheeler RL, Hampton AD, Langley NR. The effects of body mass index and age on cross-sectional properties of the femoral neck. Clin Anat. 2015;28(8):1048–57. 9. Fritz J, Cöster ME, Nilsson JÅ, Rosengren BE, Dencker M, Karlsson MK. The associations of physical activity with fracture risk-a 7-year prospective controlled intervention study in 3534 children. Osteoporos Int. 2016;27(3):915–22. 10. Thevenot J, Hirvasniemi J, Pulkkinen P, Määttä M, Korpelainen R, Saarakkala S, et al. Assessment of risk of femoral neck fracture with radiographic texture parameters: a retrospective study. Radiology. 2014;272(1):184–91. 11. Pires RE, Prata EF, Gibram AV, Santos LE, Lourenc¸o PR, Belloti JC. Radiographic anatomy of the proximal femur: correlation with the occurrence of fractures. Acta Ortop Bras. 2012;20(2):79–83. 12. Burge R, Dawson-Hughes B, Solomon DH, Wong JB, King A, Tosteson A. Incidence and economic burden of osteoporosis-related fractures in the United States, 2005–2025. J Bone Miner Res. 2007;22(3):465–75. 13. Kanis JA, Johnell O, Oden A, Sembo I, Redlund-Johnell I, Dawson A, et al. Long-term risk of osteoporotic fracture in Malmö. Osteoporos Int. 2000;11(8):669–74. 14. Zhou Z, Redaelli A, Johnell O, Willke RJ, Massimini G. A retrospective analysis of health care costs for bone fractures in women with early-stage breast carcinoma. Cancer. 2004;100(3):507–17. 15. Kayan K, Kanis J, McCloskey E. Osteoporosis management by geriatricians in the UK. Age Ageing. 2003;32(5):553. 16. Khosla S, Melton LJ 3rd, Dekutoski MB, Achenbach SJ, Oberg AL, Riggs BL. Incidence of childhood distal forearm fractures over 30 years: a population-based study. JAMA. 2003;290(11):1479–85. 17. Kanis JA, Black D, Cooper C, Dargent P, Dawson-Hughes B, De Laet C, et al. A new approach to the development of assessment guidelines for osteoporosis. Osteoporos Int. 2002;13(7):527–36. 18. Jepsen KJ, Hu B, Tommasini SM, Courtland HW, Price C, Terranova CJ, et al. Genetic randomization reveals functional relationships among morphologic and tissue-quality traits that contribute to bone strength and fragility. Mamm Genome. 2007;18(6–7):492–507. 19. Tommasini SM, Nasser P, Hu B, Jepsen KJ. Biological co-adaptation of morphological and composition traits contributes to mechanical functionality and skeletal fragility. J Bone Miner Res. 2008;23(2):236–46. 20. Chappard D, Baslé MF, Legrand E, Audran M. Trabecular bone microarchitecture: a review. Morphologie. 2008;92(299):162–70. 21. Pulkkinen P, Saarakkala S, Nieminen MT, Jämsä T. Standard radiography: untapped potential in the assessment of osteoporotic fracture risk. Eur Radiol. 2013;23(5):1375–82. 22. Cummings SR, Cauley JA, Palermo L, Ross PD, Wasnich RD, Black D, et al. Racial differences in hip axis lengths might explain racial differences in rates of hip fracture. Study of Osteoporotic Fractures Research Group. Osteoporos Int. 1994;4(4):226–9. 23. Kanis JA, Johnell O, Oden A, Johansson H, McCloskey E. FRAX and the assessment of fracture probability in men and women from the UK. Osteoporos Int. 2008;19(4):385–97. 24. Trémollieres FA, Pouillès JM, Drewniak N, Laparra J, Ribot CA, Dargent-Molina P. Fracture risk prediction using BMD and clinical risk factors in early postmenopausal women: sensitivity of the WHO FRAX tool. J Bone Miner Res. 2010;25(5):1002–9. 25. Korthoewer D, Chandran M, Endocrine and Metabolic Society of Singapore. Osteoporosis management and the utilization of FRAX®: a survey amongst health1. Johnell O, Kanis JA, Odén A, Sernbo I, Redlund-Johnell I, Petterson C, et al. Mortality after osteoporotic fractures. Osteoporos Int. 2004;15(1):38–42. 2. Kanis JA. Diagnosis of osteoporosis and assessment of fracture risk. Lancet. 2002;359(9321):1929–36. 3. Schuit SC, van der Klift M, Weel AE, de Laet CE, Burger H, Seeman E, et al. Fracture incidence and association with bone mineral density in elderly men and women: the Rotterdam Study. Bone. 2004;34(1):195–202. 4. Loures FB, Chaoubah A, Oliveira VM, Almeida AM, Campos EM, Paiva EP. Economic analysis of surgical treatment of hip fracture in older adults. Rev Saúde Pública. 2015;49:12. 5. Daniachi D, Santos Netto A, Ono NK, Guimarães RP, Polesello GC, Honda EK. Epidemiology of fractures of the proximal third of the femur in elderly patients. Rev Bras Ortop. 2015;50(4):371–7. 6. Formosa MM, Xuereb-Anastasi A. Biochemical predictors of low bone mineral density and fracture susceptibility in maltese postmenopausal women. Calcif Tissue Int. 2016;98(1):28–41. 7. Palm H, Teixidor J. Proximal femoral fractures: can we improve further surgical treatment pathways? Injury. 2015;46 Suppl. 5:S47–51. 8. Wheeler RL, Hampton AD, Langley NR. The effects of body mass index and age on cross-sectional properties of the femoral neck. Clin Anat. 2015;28(8):1048–57. 9. Fritz J, Cöster ME, Nilsson JÅ, Rosengren BE, Dencker M, Karlsson MK. The associations of physical activity with fracture risk-a 7-year prospective controlled intervention study in 3534 children. Osteoporos Int. 2016;27(3):915–22. 10. Thevenot J, Hirvasniemi J, Pulkkinen P, Määttä M, Korpelainen R, Saarakkala S, et al. Assessment of risk of femoral neck fracture with radiographic texture parameters: a retrospective study. Radiology. 2014;272(1):184–91. 11. Pires RE, Prata EF, Gibram AV, Santos LE, Lourenc¸o PR, Belloti JC. Radiographic anatomy of the proximal femur: correlation with the occurrence of fractures. Acta Ortop Bras. 2012;20(2):79–83. 12. Burge R, Dawson-Hughes B, Solomon DH, Wong JB, King A, Tosteson A. Incidence and economic burden of osteoporosis-related fractures in the United States, 2005–2025. J Bone Miner Res. 2007;22(3):465–75. 13. Kanis JA, Johnell O, Oden A, Sembo I, Redlund-Johnell I, Dawson A, et al. Long-term risk of osteoporotic fracture in Malmö. Osteoporos Int. 2000;11(8):669–74. 14. Zhou Z, Redaelli A, Johnell O, Willke RJ, Massimini G. A retrospective analysis of health care costs for bone fractures in women with early-stage breast carcinoma. Cancer. 2004;100(3):507–17. 15. Kayan K, Kanis J, McCloskey E. Osteoporosis management by geriatricians in the UK. Age Ageing. 2003;32(5):553. 16. Khosla S, Melton LJ 3rd, Dekutoski MB, Achenbach SJ, Oberg AL, Riggs BL. Incidence of childhood distal forearm fractures over 30 years: a population-based study. JAMA. 2003;290(11):1479–85. 17. Kanis JA, Black D, Cooper C, Dargent P, Dawson-Hughes B, De Laet C, et al. A new approach to the development of assessment guidelines for osteoporosis. Osteoporos Int. 2002;13(7):527–36. 18. Jepsen KJ, Hu B, Tommasini SM, Courtland HW, Price C, Terranova CJ, et al. Genetic randomization reveals functional relationships among morphologic and tissue-quality traits that contribute to bone strength and fragility. Mamm Genome. 2007;18(6–7):492–507. 19. Tommasini SM, Nasser P, Hu B, Jepsen KJ. Biological co-adaptation of morphological and composition traits contributes to mechanical functionality and skeletal fragility. J Bone Miner Res. 2008;23(2):236–46. 20. Chappard D, Baslé MF, Legrand E, Audran M. Trabecular bone microarchitecture: a review. Morphologie. 2008;92(299):162–70. 21. Pulkkinen P, Saarakkala S, Nieminen MT, Jämsä T. Standard radiography: untapped potential in the assessment of osteoporotic fracture risk. Eur Radiol. 2013;23(5):1375–82. 22. Cummings SR, Cauley JA, Palermo L, Ross PD, Wasnich RD, Black D, et al. Racial differences in hip axis lengths might explain racial differences in rates of hip fracture. Study of Osteoporotic Fractures Research Group. Osteoporos Int. 1994;4(4):226–9. 23. Kanis JA, Johnell O, Oden A, Johansson H, McCloskey E. FRAX and the assessment of fracture probability in men and women from the UK. Osteoporos Int. 2008;19(4):385–97. 24. Trémollieres FA, Pouillès JM, Drewniak N, Laparra J, Ribot CA, Dargent-Molina P. Fracture risk prediction using BMD and clinical risk factors in early postmenopausal women: sensitivity of the WHO FRAX tool. J Bone Miner Res. 2010;25(5):1002–9. 25. Korthoewer D, Chandran M, Endocrine and Metabolic Society of Singapore. Osteoporosis management and the utilization of FRAX®: a survey amongst health
