Self-assessment Tool to Identify Primary Osteoporosis in Chinese Elderly Males

Current status and dilemmas of osteoporosis screening tools: A narrative review

OBJECTIVE

This review aims to explore the strengths and dilemmas of existing osteoporosis screening tools and suggest possible ways of optimization, in addition to exploring the potential of AI-integrated X-ray imaging in osteoporosis screening, especially its ability to improve accuracy and applicability to different populations. To break through the dilemma of low accessibility, poor clinical translation, complexity of use, and apparent limitations of screening results of existing osteoporosis screening tools.

DATA SOURCES

A comprehensive literature search was performed using PubMed, Web of Science, and CNKI databases. The search included articles published between 2000 and 2023, focusing on studies evaluating osteoporosis screening tools, Artificial intelligence applications in medical imaging, and implementing AI technologies in clinical settings.

STUDY SELECTION

The Osteoporosis Risk Assessment Tool for Asians (OSTA), the Simple Calculated Osteoporosis Risk Estimator (SCORE), age, body size, one or no estrogen ever (ABONE), and the Osteoporosis Risk Index (OSIRIS) are the six commonly used screening tools for osteoporosis that are discussed in this review. In addition, the performance of AI-integrated imaging systems is explored in light of relevant research advances in Artificial intelligence in osteoporosis screening. Studies of the use of these tools in different populations and their advantages and disadvantages were included in the selection criteria.

RESULTS

The results highlight that AI-integrated X-ray imaging technologies offer significant improvements over traditional osteoporosis screening tools. Artificial intelligence systems demonstrated higher accuracy by incorporating complex clinical data and providing personalized assessments for diverse populations. The studies showed that AI-driven imaging could enhance sensitivity and specificity, particularly in detecting early-stage bone density loss in patients with complex clinical profiles. The findings also suggest that Artificial intelligence technologies have the potential to be effectively applied in resource-limited settings through the use of mobile devices and remote diagnostics.

CONCLUSIONS

AI-integrated X-ray imaging technology significantly advances osteoporosis screening, offering more accurate and adaptable solutions than traditional tools. Its ability to incorporate complex clinical data and apply it across various demographic groups makes it particularly promising in diverse and resource-limited environments. Further research is needed to explore the full potential of AI in enhancing screening accessibility and effectiveness, particularly in underserved populations.

Prevalence of osteoporosis and associated factors among people aged 50 years and older in the Madhesh province of Nepal: a community-based cross-sectional study

BACKGROUND

The high prevalence of osteoporosis has increased the economic burden on the health system globally. The burden of osteoporosis and its associated factors have not been adequately assessed in community settings in the Nepalese context thus far. Therefore, this study aimed to assess the prevalence of osteoporosis and its associated factors, lifestyle behaviors, and dietary calcium intake.

METHODS

A community-based cross-sectional study was conducted among 395 people aged 50 years and older in the Madhesh Province of Nepal between July 2022 and August 2023. The Osteoporosis Self-assessment Tools for Asians (OSTA) index was used to measure osteoporosis. A structured questionnaire was used to collect sociodemographic information, anthropometric data, lifestyle behavior, daily dietary calcium intake, and frequency of calcium-rich food consumption. A food frequency questionnaire and 24-hour recall methods were used to assess dietary intake. The chi-square test, binary logistic regression and Mann‒Whitney U test were applied to measure the association between predictors and the outcome of interest.

RESULTS

The prevalence of no risk, moderate risk and high risk of osteoporosis were 38.7%, 39%, and 22.3% respectively. The risk of osteoporosis was higher in females (aOR = 5.18, CI: 2.10-12.75, p < 0.001) and increased risk with advancing age (aOR = 32.49, CI: 14.02-75.28, p < 0.001). Similarly, underweight was associated with increased odds of having osteoporosis (aOR = 13.42, CI = 4.58-39.30, p < 0.001). The incidence of osteoporosis was strongly associated with daily calcium intake of 225 mg (100, 386).

CONCLUSION

This study revealed a high prevalence of osteoporosis among people aged 50 years and older due to the combined effect of being underweight and having inadequate calcium intake. Nutritional counselling services encourage people to consume sufficient calcium-rich food and adopt an appropriate lifestyle behaviours to maintain healthy body weight so that osteoporosis and osteoporotic fractures could be prevented. Further research can explore the impact of socioeconomic status and medical comorbidities on a large scale.

The Assessment of the Osteoporosis Self-Assessment Tool for Asians and Calcaneal Quantitative Ultrasound in Identifying Osteoporotic Fractures and Falls Among Chinese People

The lack of DXA has made the diagnosis and treatment of osteoporosis extremely difficult in the vast rural areas of China, which has the largest population with high risks of osteoporosis. The aims of this cross-sectional study were to evaluate the association between the osteoporosis self-assessment tool for Asians (OSTA) and calcaneus quantitative ultrasound (QUS) in populations residing in Shanghai, China, and their assessment in predicting osteoporotic fractures and falls. A population of 12,033 participants, including 1272 males (average age 68.3 ± 9.8 years, range 28-100 years) and 10,761 females (average 56.8 ± 11.4 years, range 23-99 years), was gathered. OSTA and calcaneus QUS (Sonost 2000, OsteoSys) values were measured. Spearman's correlation and Cohen's kappa were used to determine the association and agreement between the OSTA and QUS. Receiver operating characteristic (ROC) curves were adapted to assess the performance and optimal cutoff values for the OSTA and QUS in osteoporotic fracture and fall screening. In total, the prevalence of osteoporotic fractures (low-trauma fractures including fractures of the spine, hip, forearm, humerus and ribs) was 15.2% in women, and 17.7% reported a history of falls (falling from standing height more than once in the past year). The percentages of men with the same history were 8.4% and 11.7%, respectively. The association between the OSTA and QUS was found to be r_s = 0.393, κ = 0.137, p < 0.001. The OSTA (cutoff < -1) revealed an area under ROC curve (AUC) of 0.590 in identifying female individuals with moderate or high risk of osteoporosis defined by QUS (T-score < -1). The QUS T-score lower than -1.55 or -1.40 in postmenopausal women may lead to an increased risk of falls or osteoporotic fractures, respectively. The agreement between QUS and the OSTA seemed to be limited in determining individuals at risk of osteoporosis. Measuring bone mineral density (BMD) by dual energy X-ray absorptiometry (DXA) may still be necessary in the clinical diagnosis of osteoporosis. OSTA and QUS T-scores less than the respective cutoff values may indicate an increased risk of osteoporotic fractures and falls that individual should be further treated and screened by DXA.

The performance of osteoporosis self-assessment tool for Asians (OSTA) in identifying the risk of osteoporosis among Malaysian population aged 40 years and above

The concordance between osteoporosis self-assessment tool for Asians (OSTA) and dual-energy X-ray absorptiometry (DXA) was fair in the study. Modification of OSTA cutoff values improved its sensitivity to identify subjects at risk for suboptimal bone health (osteopenia/osteoporosis) and osteoporosis.

PURPOSE

Osteoporosis self-assessment tool for Asians (OSTA) is a convenient screening algorithm used widely to identify patients at risk of osteoporosis. Currently, the number of studies validating OSTA in Malaysian population is limited. This study aimed to validate the performance of OSTA in identifying subjects with osteoporosis determined with DXA.

METHODS

This cross-sectional study recruited 786 Malaysians in Klang Valley, Malaysia. Their bone health status was assessed by DXA and OSTA. The association and agreement between OSTA and bone mineral density assessment by DXA were determined by Pearson's correlation and Cohen's kappa, respectively. Receiver operating characteristics (ROC) curves were used to determine the sensitivity, specificity, and area under the curve (AUC) for OSTA.

RESULTS

OSTA and DXA showed a fair association in the study (r = 0.382, κ = 0.159, p < 0.001). OSTA (cutoff < - 1) revealed a sensitivity of 32.3%, specificity of 92.3%, and AUC of 0.618 in identifying subjects with suboptimal bone health. The sensitivity of OSTA (cutoff < - 4) in determining subjects at risk of osteoporosis was better among women (sensitivity = 20%) than men (sensitivity = 0%). Modified OSTA cutoff values improved the sensitivity of OSTA in identifying subjects with suboptimal bone health (men = 81.0% at cutoff 3.4, women = 82.8% at cutoff 2.0) and osteoporosis (men = 81.8% at cutoff 1.8, women = 81.3% at cutoff 0.8).

CONCLUSION

OSTA with its original cutoff values is ineffective in identifying individuals at risk for osteoporosis. Adjusting the cutoff values significantly increases the sensitivity of OSTA, thus highlighting the need to validate this instrument among the local population before using it for osteoporosis screening clinically.

Performance of Osteoporosis Self-Assessment Tool (OST) in Predicting Osteoporosis-A Review

Bone health screening plays a vital role in the early diagnosis and treatment of osteoporosis to prevent fragility fractures among the elderly and high-risk individuals. Dual-energy X-ray absorptiometry (DXA), which detects bone mineral density, is the gold standard in diagnosing osteoporosis but is not suitable for screening. Therefore, many screening tools have been developed to identify individuals at risk for osteoporosis and prioritize them for DXA scanning. The Osteoporosis Self-assessment Tool (OST) is among the first tools established to predict osteoporosis in postmenopausal women. It can identify the population at risk for osteoporosis, but its performance varies according to ethnicity, gender, and age. Thus, these factors should be considered to ensure the optimal use of OST worldwide. Overall, OST is a simple and economical screening tool to predict osteoporosis and it can help to optimize the use of DXA.