Prevalence, Characteristics, and Associated Risk Factors of Wrist Fractures in Americans Above 50: The Cross-Sectional NHANES Study

Distal Radius Fracture with Dislocation of the Radioulnar Joint

The authors report on the case of a 30-year-old male who sustained a wrist injury following a motorcycle accident. Emergency department evaluation revealed a left open distal radius fracture with distal radioulnar joint dislocation. The patient underwent surgical fixation to restore full mobility of his wrist.

Association of distal radial fracture with comorbidities: model development and validation

BACKGROUND

To better assess the risk of distal radial fracture in the general population, we need models that take into account a wide range of risk factors other than osteoporosis. The objective was to develop and validate a model for association of patients' characteristics with distal radial fracture that effectively incorporates multifactorial aspects and includes comorbidities.

METHOD

We analyzed data from a large Longitudinal Health Insurance Database between 2000 and 2013. The outcome of the study was the occurrence of distal radial fracture and the predictors were demographic and comorbidity data. Two machine learning models were developed and validated for patients ≥50 (N = 2745) and <50 (N = 1587) years of age.

RESULTS

For patients aged ≥50 years, selected characteristics included sex, age, urbanization level, osteoarthritis, carpal tunnel syndrome, obesity, hyperlipidemia, trigger finger, hypertension, hypothyroidism, diabetes, hyperthyroidism, and rheumatoid arthritis. For patients <50 years old, selected characteristics included age, sex, diabetes mellitus, urbanization level, carpal tunnel syndrome, hyperlipidemia, osteoarthritis, obesity, and hypertension. Accuracy, sensitivity, specificity, area under the curve, and likelihood ratio were 0.77, 0.83, 0.72, 0.77, and 2.92 for age ≥50 years and 0.73, 0.79, 0.67, 0.73, and 2.41 for age <50 years.

CONCLUSION

The study models can serve as reliable screening tools to assess the risk of distal radial fracture in the general population before bone mineral density testing. In addition, they can be integrated into decision support systems to help healthcare providers identify high-risk patients for additional evaluation and education, ultimately improving the quality of care.

Trends in prevalence of fractures among adults in the United States, 1999-2020: a population-based study

BACKGROUND

Population data that examines recent national trends in the prevalence of fractures are lacking in the United States (US).

MATERIALS AND METHODS

Analyses were based on 1999-2020 data from the National Health and Nutrition Examination Survey (NHANES). Primary outcomes included the prevalence of hip, wrist, and vertebral fractures among adults aged greater than or equal to 50 years. Changes in the prevalence over time were determined by joinpoint regression analysis. The authors also described the variation by fracture subtypes, sociodemographic characteristics, and their combination.

RESULTS

For adults aged greater than or equal to 50 years in NHANES 2017-March 2020, the authors estimated that there was 2.6 million Americans with hip fractures, 14.6 million Americans with wrist fractures, and 5.2 million Americans with vertebral fractures. The prevalence of wrist fractures significantly increased from 8.7% (7.4-9.9%) in 1999-2000 to 12.8% (11.6-14.1%) in 2017-March 2020 among adults aged greater than or equal to 50 years ( P for trend=0.04); significant increases were also observed in fractures that occurred at age less than 50 years, non-Hispanic White, high family income groups, and several combination subgroups (e.g. fractures occurred at age <50 years among women). The prevalence of vertebral fractures increased from 2.2% (1.7-2.8%) in 1999-2000 to 4.6% (3.7-5.5%) in 2017-March 2020 among adults aged greater than or equal to 50 years ( P for trend=0.02); significant increases were also observed in 50-64 years, women, non-Hispanic White, high family income groups and several combination subgroups (e.g. fractures that occurred at age <50 years among women). The authors did not observe significant trend changes in the prevalence of hip fractures among adults aged greater than or equal to 50 years between 1999 and 2020.

CONCLUSION

The estimated prevalence of wrist and vertebral fractures significantly increased among US adults aged greater than or equal to 50 years from 1999 to 2020, although hip fractures did not significantly change.

Detection of osteoporotic-related bone changes and prediction of distal radius strength using Raman spectra from excised human cadaver finger bones

While osteoporosis is reliably diagnosed using dual energy X-ray absorptiometry (DXA), screening rates are alarmingly low, contributing to preventable fractures. Raman spectroscopy (RS) can detect biochemical changes that occur in bones transcutaneously and can arguably be more accessible than DXA as a fracture risk assessment. A reasonable approach to translate RS is to interrogate phalangeal bones of human hands, where the soft tissues covering the bone are less likely to hamper transcutaneous measurements. To that end, we set out to first determine whether Raman spectra obtained from phalangeal bones correlate with distal radius fracture strength, which can predict subsequent osteoporotic fractures at the spine and hip. We performed RS upon diaphyseal and epiphyseal regions of exposed proximal phalanges from 12 cadaver forearms classified as healthy (n = 3), osteopenic (n = 4), or osteoporotic (n = 5) based on wrist T-scores measured by DXA. We observed a significant decrease in phosphate to matrix ratio and a significant increase in carbonate substitution in the osteoporotic phalanges relative to healthy and osteopenic phalanges. Multivariate regression models produced wrist T-score estimates with significant correlation to the DXA-measured values (r = 0.79). Furthermore, by accounting for phalangeal RS parameters, body mass index, and age, a multivariate regression significantly predicted distal radius strength measured in a simulated-fall biomechanical test (r = 0.81). These findings demonstrate the feasibility of interrogating the phalanges using RS for bone quality assessment of distant clinical sites of fragility fractures, such as the wrist. Future work will address transcutaneous measurement challenges as another requirement for scale-up and translation.

Fracture Risk in Patients with Hypoparathyroidism

PURPOSE OF REVIEW

To summarize the recently published scientific evidence on fracture risk in hypoparathyroidism.

RECENT FINDINGS

Hypoparathyroidism is characterized by a low bone turnover and a high bone mineral density. Data on fracture risk are sparse and due to the rarity of the disease, available studies have only been able to include relatively few patients. Risk of non-vertebral fractures does not seem to be affected to any major degree, although epidemiological studies suggest a decreased risk of fractures at the humerus in postsurgical hypoparathyroidism, whereas an increased risk of fractures at the upper arm has been shown in non-surgical hypoparathyroidism. Several, but not all, studies have also pointed towards an increased risk of vertebral fractures, especially in non-surgical hypoparathyroidism. Fractures at the appendicular skeleton do not seem to be of specific concern in hypoparathyroidism, but emerging data suggest an increased risk of vertebral fractures, which needs to be clarified further in upcoming studies.

Associations between weight-adjusted waist index and fractures: a population-based study

INTRODUCTION

The weight-adjusted waist circumference index (WWI) is a novel obesity evaluation indicator that appears to be superior to body mass index (BMI) and waist circumference (WC) in evaluating muscle and fat mass. The purpose of this study was to investigate the association between WWI and fractures among adults.

METHODS

In this cross-sectional study, multivariate logistic regression and smoothed curve fitting were used to investigate linear and nonlinear associations between WWI and fractures, based on data from 28,679 adult participants in the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2018.

RESULTS

After adjusting for all covariates, the prevalence of hip/wrist/spine fractures among all participants was 1.09%, 8.87%, and 1.97%, respectively. A 1-unit increase in WWI was associated with a 5% increase in the odds of hip fractures [1.05 (1.01, 1.10)], and a 9% increase in the odds of spine fractures [1.09 (1.06, 1.13)], but not with the prevalence of wrist fractures [0.97 (0.94, 1.06)].

CONCLUSIONS

Higher WWI was associated with an increased prevalence of hip fracture and spine fracture, but not wrist fracture.

Association of apolipoprotein A1 with osteoporosis: a cross-sectional study

Higher levels of apolipoprotein A1 (ApoA1) were associated with higher risk of osteoporosis, which supports the argument that lipid metabolism is involved in bone metabolism.

BACKGROUND

Although the current evidence shows that lipid metabolism and osteoporosis are closely related to cardiovascular disease, the association between ApoA1 and osteoporosis is still unknown. Therefore, the purpose of this study was to explore the relationship between ApoA1 and osteoporosis.

METHODS

In this cross-sectional study, we included 7743 participants in the Third National Health and Nutrition Examination Survey. ApoA1 was regarded as an exposure variable and osteoporosis was considered as an outcome variable. Multivariate logistic regression analysis, sensitivity analysis, and receiver operator characteristic (ROC) were used to assess the association of ApoA1 with osteoporosis.

RESULTS

Participants with higher ApoA1 had higher rates of osteoporosis compared to participants with lower ApoA1 (P <  0.05). Individuals with osteoporosis had higher levels of ApoA1 than individuals without osteoporosis (P <  0.05). In multivariate logistic regression analysis adjusted for age, sex, race, hypertension, diabetes, gout, hypotensive drugs, hypoglycemic drugs, systolic blood pressure, total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, apolipoprotein B, blood urea nitrogen, albumin, uric acid, hemoglobin A1c, alkaline phosphatase and total calcium, higher ApoA1 was strongly associated with higher risk of osteoporosis, whether as a continuous variable or a categorical variable [Model 3, OR (95% CI), P value: 2.289 (1.350, 3.881), 0.002 and 1.712 (1.183, 2.478), 0.004]. And after excluding individuals with gout, the correlation between them remained and was significant (P <  0.01). And ROC analysis also showed that ApoA1 could predict the development of osteoporosis (AUC = 0.650, P <  0.001).

CONCLUSION

ApoA1 was closely associated with osteoporosis.

Artificial intelligence vs. radiologist: accuracy of wrist fracture detection on radiographs

OBJECTIVE

To compare the performances of artificial intelligence (AI) to those of radiologists in wrist fracture detection on radiographs.

METHODS

This retrospective study included 637 patients (1917 radiographs) with wrist trauma between January 2017 and December 2019. The AI software used was a deep neuronal network algorithm. Ground truth was established by three senior musculoskeletal radiologists who compared the initial radiology reports (IRR) made by non-specialized radiologists, the results of AI, and the combination of AI and IRR (IR+AI) RESULTS: A total of 318 fractures were reported by the senior radiologists in 247 patients. Sensitivity of AI (83%; 95% CI: 78-87%) was significantly greater than that of IRR (76%; 95% CI: 70-81%) (p < 0.001). Specificities were similar for AI (96%; 95% CI: 93-97%) and for IRR (96%; 95% CI: 94-98%) (p = 0.80). The combination of AI+IRR had a significantly greater sensitivity (88%; 95% CI: 84-92%) compared to AI and IRR (p < 0.001) and a lower specificity (92%; 95% CI: 89-95%) (p < 0.001). The sensitivity for scaphoid fracture detection was acceptable for AI (84%) and IRR (80%) but poor for the detection of other carpal bones fracture (41% for AI and 26% for IRR).

CONCLUSIONS

Performance of AI in wrist fracture detection on radiographs is better than that of non-specialized radiologists. The combination of AI and radiologist's analysis yields best performances.

KEY POINTS

• Artificial intelligence has better performances for wrist fracture detection compared to non-expert radiologists in daily practice. • Performance of artificial intelligence greatly differs depending on the anatomical area. • Sensitivity of artificial intelligence for the detection of carpal bones fractures is 56%.