AB0607 DO HIP STRUCTURAL ANALYSIS MEASUREMENTS PREDICT FRACTURE RISK IN PATIENTS WITH POLYMYALGIA RHEUMATICA?

Background:Polymyalgia Rheumatica (PMR) is an inflammatory condition which commonly affects the elderly. Risk of fracture is higher in this group of patients compared to the general population and can lead to increased morbidity and mortality (1). Hip structural analysis (HSA) is a technique that uses dual-energy X-ray absorptiometry (DEXA) images to assess hip bone structure (2).Objectives:To identify whether HSA measurements help predict fracture in patients with PMR.Methods:Data were collected from June 2004 to October 2010 from PMR patients who had a DEXA scan at a District General Hospital. This included hip axis length (HAL), cross-sectional area (CSA), cross-sectional moment of inertia (CSMI), distance from centre of femoral head to centre of femoral neck (D1) and to inter-trochanteric line (D2), mean femoral neck diameter (D3), shaft angle (A) neck/shaft angle (Θ) and proximal femur strength index (SI) and distance from centre of mass of femoral neck to superior neck margin (Y). Fracture was predicted by a series of binomial logistic regression models, adjusted for age and sex. Odds ratios with 95% confidence intervals and area under the receiver operating characteristic curve (AUC) were calculated.Results:714 patients with PMR were identified, 182 were male and the mean age was 70.5. HAL, CSMI, D1, D2, D3, A, Θ, SI and Y were not significant predictors of fracture in regression models; odds ratios are included in Table 1. CSA predicted fracture risk; odds ratio was 0.988 with a 95% confidence interval of 0.980-0.997. The AUC for the CSA regression model was 0.6739.Table 1.Odds ratios of fracture for different HSA parametersHSA ParameterOdds Ratio (95% confidence interval)HAL1.008 (0.982 - 1.035)CSMI1.000 (0.999 - 1.000)CSA0.988 (0.980 - 0.997)D11.029 (0.972 - 1.089)D21.010 (0.981 - 1.040)D31.033 (0.962 - 1.109)Y1.087 (0.966 - 1.223)A0.983 (0.940 - 1.029)Θ1.007 (0.975 - 1.039)SI0.683 (0.406 - 1.150)Conclusion:These data suggest that CSA helps predict the risk of fracture in patients with PMR. HAL, CSMI, D1, D2, D3, A, Θ, SI and Y do not predict fracture risk. Limitations of the study are that it was retrospective and only studied patients who underwent DEXA scans. The study may have been underpowered to detect the impact of some HSA measurements on fracture risk.References:[1]Chatzigeorgiou C, Mackie SL. Comorbidity in polymyalgia rheumatica. Reumatismo. 2018; 70 1:35-43. Available from: http://eprints.whiterose.ac.uk/132109/\\.[2]Kaptoge S, Beck TJ, Reeve J, Stone KL, Hillier TA, Cauley JA, et al. Prediction of Incident Hip Fracture Risk by Femur Geometry Variables Measured by Hip Structural Analysis in the Study of Osteoporotic Fractures. Journal of Bone and Mineral Research. 2008; 23 (12): 1892-1904. Available from: doi: https://doi.org/10.1359/jbmr.080802Disclosure of Interests:None declared.

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POS0511 USING HIP STRUCTURAL ANALYSIS MEASUREMENTS TO PREDICT FRACTURE IN RHEUMATOID ARTHRITIS

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2021-eular.802 ◽

2021 ◽

Vol 80 (Suppl 1) ◽

pp. 488.2-489

Author(s):

R. Ark ◽

M. Bukhari

Keyword(s):

Rheumatoid Arthritis ◽

Structural Analysis ◽

Femoral Neck ◽

Fracture Risk ◽

Regression Models ◽

Hip Structural Analysis ◽

Odds Ratios ◽

Mineral Density ◽

Cross Sectional ◽

Femur Strength Index

Background:Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease and increases the risk of developing osteoporosis. Incidence of fracture is higher in this group of patients compared to the general population and can lead to increased morbidity (1). Bone strength of the proximal femur is not only linked to bone mineral density; it also depends on the geometric properties of the bone mass (2). Hip structural analysis (HSA) is a technique used to assess hip bone structure that takes geometric measurements of the femur from dual-energy X-ray absorptiometry (DEXA) images (3).Objectives:To determine whether HSA measurements help predict fracture in patients with RA.Methods:Data were collected from June 2004 to August 2017 from RA patients who underwent a DEXA scan at a District General Hospital. This included hip axis length (HAL), cross-sectional area (CSA), cross-sectional moment of inertia (CSMI), distance from centre of femoral head to centre of femoral neck (D1) and to inter-trochanteric line (D2), mean femoral neck diameter (D3), shaft angle (A) neck/shaft angle (Θ) and proximal femur strength index (SI) and distance from centre of mass of femoral neck to superior neck margin (Y). Fracture was predicted by a series of binomial logistic regression models, adjusted for sex, age and bone mineral density (BMD). Odds ratios with 95% confidence intervals and area under the receiver operating characteristic curve (AUC) were calculated.Results:2077 patients with RA were identified, 1632 were female and the mean age was 66.7. HAL, D1, D2, D3, A, Θ and Y were not significant predictors of fracture in regression models; odds ratios are included in table 1. CSA, CSMI and SI predicted fracture risk. The AUC for CSA, CSMI and SI regression models were 0.632, 0.609 and 0.625 respectively.Table 1.Odds ratios of fracture for different HSA parameters in RA patientsHSA ParameterOdds Ratio (95% Confidence Interval)HAL1.01410 (0.99958 - 1.02883)CSMI0.99994 (0.99990 - 0.99998)CSA0.98523 (0.98065 - 0.98982)D11.01683 (0.98925 - 1.04518)D21.01286 (0.99886 - 1.02705)D31.00664 (0.96958 - 1.04511)Y1.04580 (0.98633 - 1.10886)A1.00898 (0.98878 - 1.02959)Θ1.00276 (0.98672 - 1.01906)SI0.56769 (0.43400 - 0.74258)Figure 1.Receiver operating characteristic curves for CSA (red), CSMI (green) and SI (blue). AUC for CSA was 0.632, CSMI-0.609 and SI-0.625.Conclusion:These data suggest that CSA, CSMI and SI help predict the fracture risk in patients with RA. HAL, D1, D2, D3, A, Θ and Y do not predict risk of fracture. The CSA regression model was the strongest predictor of fracture. HSA measurements can therefore help predict risk of fracture in conjunction with other factors. Limitations of the study are that it was retrospective and only studied patients who had a DEXA scan.References:[1]Xue A, Wu S, Jiang L, Feng A, Guo H, Zhao P. Bone fracture risk in patients with rheumatoid arthritis: A meta-analysis. Medicine. 2017; 96 (36): e6983. Available from: doi: 10.1097/MD.0000000000006983.[2]Faulkner KG, Wacker WK, Barden HS, Simonelli C, Burke PK, Ragi S, Del Rio L. Femur strength index predicts hip fracture independent of bone density and hip axis length. Osteoporos Int. 2006;17(4):593-9. doi: 10.1007/s00198-005-0019-4.[3]Kaptoge S, Beck TJ, Reeve J, Stone KL, Hillier TA, Cauley JA, et al. Prediction of Incident Hip Fracture Risk by Femur Geometry Variables Measured by Hip Structural Analysis in the Study of Osteoporotic Fractures. Journal of Bone and Mineral Research. 2008; 23 (12): 1892-1904. Available from: doi: https://doi.org/10.1359/jbmr.080802.Disclosure of Interests:None declared

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The Association Between Femoral Neck-Shaft Angle and Aging and Its Influence on the Performance of Functional Activity: A Cross-Sectional Investigation

Journal of Aging and Physical Activity ◽

10.1123/japa.2019-0008 ◽

2020 ◽

Vol 28 (2) ◽

pp. 250-254

Author(s):

Ibrahim M. Altubasi

Keyword(s):

Femoral Neck ◽

Regression Models ◽

Walking Speed ◽

Functional Performance ◽

Hierarchical Regression ◽

Performance Tests ◽

Cross Sectional ◽

Neck Shaft Angle ◽

Functional Activities ◽

Shaft Angle

The purposes of this study were first to examine the association between aging and both the magnitude and asymmetry in the femoral neck-shaft angle (NSA). The second purpose was to determine the effects of both the magnitude and NSA asymmetry on the performance of functional activities in healthy individuals. Fifty-one subjects participated in this study. The femoral NSA was measured on computed tomography scout images. The participants performed four performance tests. Four hierarchical regression models were constructed to explore the effect of each predictor on the outcomes. Aging was associated with NSA asymmetry, but not with the degree of NSA. Age contributed significantly to the variability of all functional performance tests except the 10-m walking speed. The degree of the NSA did not contribute to the prediction of the functional performance tests. However, asymmetry in the NSA added significantly to the prediction of all functional performance tests except the 10-m walking speed.

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Sexual dimorphism of femoral neck cross-sectional bone geometry in athletes and non-athletes: a hip structural analysis study

Journal of Bone and Mineral Metabolism ◽

10.1007/s00774-011-0339-8 ◽

2011 ◽

Vol 30 (4) ◽

pp. 454-460 ◽

Cited By ~ 6

Author(s):

Karen Hind ◽

Lisa Gannon ◽

Emma Whatley ◽

Carlton Cooke

Keyword(s):

Sexual Dimorphism ◽

Structural Analysis ◽

Femoral Neck ◽

Bone Geometry ◽

Hip Structural Analysis ◽

Cross Sectional ◽

Analysis Study

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Poor social support and loneliness in chronic headache: Prevalence and effect modifiers

Cephalalgia ◽

10.1177/03331024211020392 ◽

2021 ◽

pp. 033310242110203

Author(s):

Maria Lurenda Westergaard ◽

Cathrine Juel Lau ◽

Karen Allesøe ◽

Anne Helms Andreasen ◽

Rigmor Højland Jensen

Keyword(s):

Social Support ◽

Odds Ratio ◽

Chronic Headache ◽

Medication Overuse ◽

Odds Ratios ◽

Cross Sectional Survey ◽

Cross Sectional ◽

Self Rated Health ◽

Poor Social Support ◽

Very High

Objective To explore the prevalence of poor social support and loneliness among people with chronic headache, and how these might be effect modifiers in the relationships between chronic headache and stress, medication overuse, and self-rated health. Background Poor social support and loneliness are consistently linked to worse health outcomes. There are few epidemiologic studies on their effect on headache. Methods The Danish Capital Region Health Survey, a cross-sectional survey, was conducted in 2017. Participants were asked about headache, pain medication use, social support, loneliness, perceived stress, and self-rated health. Data were accessed from sociodemographic registers. Logistic regression analyses were performed to test for effect modification. Results The response rate was 52.6% (55,185 respondents) and was representative of the target population. People with chronic headache were more likely to report poor social support and loneliness compared to those without chronic headache ( p < 0.0001 for both). Odds ratios for the combination of chronic headache and poor social support were very high for stress (odds ratio 8.1), medication overuse (odds ratio 21.9), and poor self-rated health (odds ratio 10.2) compared to those without chronic headache and with good social support. Those who reported both chronic headache and loneliness had a very high odds ratio for stress (odds ratio 14.4), medication overuse (odds ratio 20.1), and poor self-rated health (odds ratio 15.9) compared to those without chronic headache and low loneliness score. When adjusted for sociodemographic factors, poor social support and loneliness were not significant effect modifiers in almost all these associations. Loneliness was a significant effect modifier in the association between chronic headache and medication overuse, but exerted greater effect among those who did not report they were lonely. Conclusion Poor social support and loneliness were prevalent among people with chronic headache. The combination of chronic headache and poor social support or loneliness showed higher odds ratios for stress, medication overuse, and poor self-rated health compared to those with good social support and low loneliness scores. The effect of loneliness in the relationship between chronic headache and medication overuse warrants further study.

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Sexual Variations of Femoral Neck-Shaft Angle in the Population of Odisha

Journal of Evolution of Medical and Dental Sciences ◽

10.14260/jemds/2021/293 ◽

2021 ◽

Vol 10 (19) ◽

pp. 1387-1390

Author(s):

Rajeev Kumar Kanchan ◽

Sonali Subhadarsini ◽

Dharma Niranjan Mishra ◽

Chinmayi Mohapatra

Keyword(s):

Femoral Neck ◽

Ventral Surface ◽

Cross Sectional Study ◽

Cross Sectional ◽

Legal Cases ◽

Male And Female ◽

Neck Shaft Angle ◽

Femur Neck ◽

Shaft Angle ◽

The Right

BACKGROUND The peculiar waddling gait of a female attracts most of the anatomists to measure and compare the neck-shaft angle (NSA) of the femora. The femoral neck shaft angle is greater in women due to greater pelvic breadth and shorter femur. Neck-shaft angle was measured by the angle that the neck makes with the shaft of the femur and it is measured on the ventral surface of femur taking the long axis of femoral neck with the long axis of the proximal end of the femur. It gives information about the race it belongs to and normally measures 120° to 140°. The intention of this study was to establish the femoral neck shaft angle variations in adult male and female for medico-legal and anthropometric studies. METHODS This cross-sectional study was conducted on 84 adult human femora of unknown sex in the anatomy department, Sriram Chandra Bhanja (SCB) Medical College, Cuttack, Odisha, from May 2018 to April 2019. We included all the femora which were free of damage or deformity and fully ossified indicating adult bone. Femora with any pathological changes i.e., cortical bone deterioration, extreme osteophyte activity, osteoarthritis and fracture etc. were excluded from the study. The NSA was measured by protractor and goniometer. RESULTS In the present study the NSA range for the right femur of male was 108° - 135° and for the right femur of females 117° - 135°. The left male femur was 118° - 135° and the left female femur was 120° - 135°. The mean neck shaft angle of male femora was 125.9° and the female femora was 125.7°. These measurements show that the values were more in males than the females. The value of the present study was statistically significant between male and female i.e., P < 0.0001. CONCLUSIONS These measurements are important in the medico-legal cases, reconstructive orthopaedic surgeries, hip replacement surgeries and also while constructing suitable prosthesis. This will also be helpful in the detection of sex by anatomists and forensic experts. KEY WORDS Neck Shaft Angle, Medico-Legal, Anthropometry and Goniometer

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Hip Structural Analysis Reveals Impaired Hip Geometry in Girls With Type 1 Diabetes

The Journal of Clinical Endocrinology & Metabolism ◽

10.1210/clinem/dgaa647 ◽

2020 ◽

Vol 105 (12) ◽

pp. e4848-e4856

Author(s):

Taïsha V Joseph ◽

Signe Caksa ◽

Madhusmita Misra ◽

Deborah M Mitchell

Keyword(s):

Type 1 Diabetes ◽

Hip Fracture ◽

Structural Analysis ◽

Bone Age ◽

Sectional Area ◽

Hip Structural Analysis ◽

Hip Geometry ◽

Cross Sectional ◽

Skeletal Fragility

Abstract Context Among patients with type 1 diabetes (T1D), the risk of hip fracture is up to 6-fold greater than that of the general population. However, the cause of this skeletal fragility remains poorly understood. Objective To assess differences in hip geometry and imaging-based estimates of bone strength between youth with and without T1D using dual-energy x-ray absorptiometry (DXA)-based hip structural analysis. Design Cross-sectional comparison. Participants Girls ages 10 to 16 years, including n = 62 with T1D and n = 61 controls. Results The groups had similar age, bone age, pubertal stage, height, lean mass, and physical activity. Bone mineral density at the femoral neck and total hip did not differ in univariate comparisons but was lower at the femoral neck in T1D after adjusting for bone age, height, and lean mass. Subjects with T1D had significantly lower cross-sectional area, cross-sectional moment of inertia, section modulus, and cortical thickness at the narrow neck, with deficits of 5.7% to 10.3%. Cross-sectional area was also lower at the intertrochanteric region in girls with T1D. Among those T1D subjects with HbA1c greater than the cohort median of 8.5%, deficits in hip geometry and strength estimates were more pronounced. Conclusions DXA-based hip structural analysis revealed that girls with T1D have unfavorable geometry and lower estimates of bone strength at the hip, which may contribute to skeletal fragility and excess hip fracture risk in adulthood. Higher average glycemia may exacerbate effects of T1D on hip geometry.

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Osteoporotic Hip Fracture Prediction: Is T-Score-Based Criterion Enough? A Hip Structural Analysis-Based Model

Journal of Biomechanical Engineering ◽

10.1115/1.4040586 ◽

2018 ◽

Vol 140 (11) ◽

Cited By ~ 14

Author(s):

Alessandra Aldieri ◽

Mara Terzini ◽

Giangiacomo Osella ◽

Adriano M. Priola ◽

Alberto Angeli ◽

...

Keyword(s):

Hip Fracture ◽

Structural Analysis ◽

Fracture Risk ◽

Proximal Femur ◽

Patient Specific ◽

Multivariate Linear Regression Analysis ◽

Hip Structural Analysis ◽

Mineral Density ◽

Neck Shaft Angle ◽

Adopted Model

At present, the current gold-standard for osteoporosis diagnosis is based on bone mineral density (BMD) measurement, which, however, has been demonstrated to poorly estimate fracture risk. Further parameters in the hands of the clinicians are represented by the hip structural analysis (HSA) variables, which include geometric information of the proximal femur cross section. The purpose of this study was to investigate the suitability of HSA parameters as additional hip fracture risk predictors. With this aim, twenty-eight three-dimensional patient-specific models of the proximal femur were built from computed tomography (CT) images and a sideways fall condition was reproduced by finite element (FE) analyses. A tensile or compressive predominance based on minimum and maximum principal strains was determined at each volume element and a risk factor (RF) was calculated. The power of HSA variables combinations to predict the maximum superficial RF values was assessed by multivariate linear regression analysis. The optimal regression model, identified through the Akaike information criterion (AIC), only comprises two variables: the buckling ratio (BR) and the neck-shaft angle (NSA). In order to validate the study, the model was tested on two additional patients who suffered a hip fracture after a fall. The results classified the patients in the high risk level, confirming the prediction power of the adopted model.

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