Prediction of Vertebral Fractures Is Specific for Gender and Site of Bone Mineral Density Measurement

Jacobs, Johannes W G; Silva, José António Pereira da; Armbrecht, Gabriele; Bijlsma, J. W. J.; Verstappen, Suzanne M M

doi:10.3899/jrheum.090731

Cited by 7 publications

(5 citation statements)

References 21 publications

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“…Nevertheless, male osteoporosis has been much less studied than postmenopausal osteoporosis, and its epidemiology is worse known (1,3). Notably, studies on the prevalence of male densitometric osteoporosis have often led to conflicting results (4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15).…”

Section: Introductionmentioning

confidence: 99%

Prevalence of vertebral fracture and densitometric osteoporosis in Spanish adult men: The Camargo Cohort Study

et al. 2017

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The aim of this study was to assess the prevalence of densitometric osteoporosis and vertebral fractures in Spanish men aged ≥50 years, and to study how the relationship between them may change depending on how osteoporosis is diagnosed. A community-based population of 1003 men aged ≥50 years was studied. Bone mineral density (BMD) was measured by DXA at the lumbar spine, femoral neck and total hip. Vertebral fractures were assessed by lateral thoracic and lumbar spine radiographs. The prevalence of osteoporosis was estimated with both the World Health Organization (WHO) (T-score of <-2.5 at the femoral neck, calculated using the young white female normal reference database) and the National Osteoporosis Foundation (NOF) criteria (T-score of <-2.5 at the femoral neck, total hip or lumbar spine, calculated using the young white male normal reference database). The prevalence of osteoporosis using the WHO criterion was 1.1% and using the NOF criterion was 13%, while that of vertebral fractures was 21.3%. The area under the curve (AUC) for the relationship between BMD and vertebral fracture prevalence was 0.64. The odds ratio for osteoporosis using the WHO definition was 2.57 (p = 0.13), and 1.78 (p = 0.007) using the NOF definition. Vertebral fracture prevalence rose with age. The prevalence of osteoporosis increased only moderately in men aged >70 years with the WHO criterion, and showed no change using the NOF definition. The prevalence of osteoporosis in Spanish men using the WHO definition is too small to have any meaningful clinical use. Although the figure is higher using the NOF definition, it would seem that population-based studies of BMD in men are of questionable value.

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Section: Introductionmentioning

confidence: 99%

Prevalence of vertebral fracture and densitometric osteoporosis in Spanish adult men: The Camargo Cohort Study

et al. 2017

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“…The lack of association between BMD and fragility fractures in our study may be partly attributed to the location differences between fracture and examination sites. 11 We clearly identified patients with existing fragility fractures who had significantly lower serum levels of albumin. Serum albumin is not only an acute phase protein in inflammation, but also an indicator of protein malnutrition and sarcopenia.…”

Section: Discussionmentioning

confidence: 91%

“…A decrease in vertebral height of >20% on lateral T-L spine X-ray film was interpreted as positive for compression fracture, as previously described. 11 Pathological fractures or fractures caused by major trauma were excluded from the study. Demographic characteristics such as age, sex, height, weight, body mass index, BMD, underlying comorbidity, blood biochemistry, and fracture sites were carefully recorded.…”

Section: Study Subjectsmentioning

confidence: 99%

Associative factors of existing fragility fractures among elderly medical inpatients: A hospital-based study

Chen

Liu

Peng

et al. 2012

Journal of Clinical Gerontology and Geriatrics

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a b s t r a c tBackground: Fragility fractures are important in public health because of the increase of morbidity, mortality and cost worldwide. Most fragility fractures occur in patients who have no osteoporosis, but the association with frailty syndrome and cerebrovascular disease is stronger. The main purpose of this study was to evaluate comorbid conditions associated with elderly medical inpatients admitted to the Geriatric Evaluation and Management Unit with existing fragility fractures. Methods: This was a retrospective study screening all medical records of patients admitted to the Geriatric Evaluation and Management Unit of a tertiary medical center from March, 2010 to April, 2011. Patients who had bone mineral density examinations of the hips in the past 5 years were enrolled. Pathological fractures or fractures caused by major trauma were excluded. Demographic characteristics, such as age, sex, height, weight, body mass index, bone mineral density, underlying comorbidity, blood biochemistry, and fracture sites were carefully recorded. Results: Overall, 169 patients (mean age: 84 years, range: 66e96 years; 62.1% male) were enrolled, and fragility fractures were identified in 130 patients, including 115 vertebral fractures, 12 hip fractures and three pelvic fractures. Patients with existing fragility fractures were significantly lower in T score (À2.33 AE 1.10 vs. À1.87 AE 1.23, p ¼ 0.030), serum albumin (3.6 AE 0.4 mg/dL vs. 3.9 AE 0.4 mg/dL, p ¼ 0.003), total protein (6.4 AE 0.9 mg/dL vs. 6.9 AE 0.7 mg/dL, p ¼ 0.007), blood urea nitrogen (20.4 AE 11.6 mg/dL vs. 26.3 AE 13.9 mg/dL, p ¼ 0.014), and creatinine (1.0 AE 0.6 mg/dL vs. 1.4 AE 0.7 mg/dL, p < 0.001), but not in bone mineral density (0.59 AE 0.25 g/cm 3 vs. 0.66 AE 0.16 g/cm 3 , p ¼ 0.120) and diagnosis of osteoporosis (41.5% vs. 25.6%, p ¼ 0.073). A multivariate logistic regression model showed that serum levels of albumin<3.5 mg/dL (odds ratio: 4.6, 95% confidence interval: 1.1e18.2, p ¼ 0.032) and creatinine <0.8 mg/dL (odds ratio: 10.8, 95% confidence interval: 1.2e97.3, p ¼ 0.033) were significantly associated with existing fragility fractures. Conclusion: For medical patients, fragility fractures were significantly associated with low serum levels of albumin and creatinine, which may be suggestive of malnutrition and low muscle mass. A further prospective study is needed to clarify the sequential effect of fragility fracture, malnutrition and sarcopenia.

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“…After removing duplicates and screening titles and abstracts, 378 articles remained. According to a full-text review, 53 articles13–67 were included. Fifty-three articles presented the development of one or more prediction models for osteoporotic fracture, while 26 articles described the validation of one or more models.…”

Section: Resultsmentioning

confidence: 99%

Predictive value of machine learning on fracture risk in osteoporosis: a systematic review and meta-analysis

Wu,

Chao,

Bao

et al. 2023

BMJ Open

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ObjectivesEarly identification of fracture risk in patients with osteoporosis is essential. Machine learning (ML) has emerged as a promising technique to predict the risk, whereas its predictive performance remains controversial. Therefore, we conducted this systematic review and meta-analysis to explore the predictive efficiency of ML for the risk of fracture in patients with osteoporosis.MethodsRelevant studies were retrieved from four databases (PubMed, Embase, Cochrane Library and Web of Science) until 31 May 2023. A meta-analysis of the C-index was performed using a random-effects model, while a bivariate mixed-effects model was used for the meta-analysis of sensitivity and specificity. In addition, subgroup analysis was performed according to the types of ML models and fracture sites.ResultsFifty-three studies were included in our meta-analysis, involving 15 209 268 patients, 86 prediction models specifically developed for the osteoporosis population and 41 validation sets. The most commonly used predictors in these models encompassed age, BMI, past fracture history, bone mineral density T-score, history of falls, BMD, radiomics data, weight, height, gender and other chronic diseases. Overall, the pooled C-index of ML was 0.75 (95% CI: 0.72, 0.78) and 0.75 (95% CI: 0.71, 0.78) in the training set and validation set, respectively; the pooled sensitivity was 0.79 (95% CI: 0.72, 0.84) and 0.76 (95% CI: 0.80, 0.81) in the training set and validation set, respectively; and the pooled specificity was 0.81 (95% CI: 0.75, 0.86) and 0.83 (95% CI: 0.72, 0.90) in the training set and validation set, respectively.ConclusionsML has a favourable predictive performance for fracture risk in patients with osteoporosis. However, most current studies lack external validation. Thus, external validation is required to verify the reliability of ML models.PROSPERO registration numberCRD42022346896.

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Prediction of Vertebral Fractures Is Specific for Gender and Site of Bone Mineral Density Measurement

Abstract: Prediction of fractures is specific for gender and site of BMD measurement. This challenges the use of similar algorithms for men and women as well as the use of hip BMD data to accurately estimate future vertebral fracture risk.

Cited by 7 publications

References 21 publications

Prevalence of vertebral fracture and densitometric osteoporosis in Spanish adult men: The Camargo Cohort Study

Prevalence of vertebral fracture and densitometric osteoporosis in Spanish adult men: The Camargo Cohort Study

Associative factors of existing fragility fractures among elderly medical inpatients: A hospital-based study

Predictive value of machine learning on fracture risk in osteoporosis: a systematic review and meta-analysis

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