Evaluation of community-based screening tools for the early screening of osteoporosis in postmenopausal Vietnamese women

Osteoporosis screening using X-ray assessment and osteoporosis self-assessment tool for Asians in hip surgery patients

OBJECTIVES

As many patients with osteoporosis remain undiagnosed, we aimed to develop a simple method to efficiently screen for osteoporosis using a combination of anteroposterior hip X-ray assessment and the Osteoporosis Self-Assessment Tool for Asians (OSTA), which is calculated as (body weight - age) × 0.2.

METHODS

One hundred Japanese women (age: 73 ± 11 years, body weight: 54.4 ± 11.1 kg) who underwent hip surgery, anteroposterior hip X-ray, and DXA were included. Based on the DXA results of the total proximal femur, 35 cases were diagnosed with osteoporosis. Fifteen orthopaedic surgeons visually inspected the hip X-ray images and scored the suspicion of osteoporosis on a scale of 1-4 (1: very unlikely, 4: very suspicious), which is referred to as "pred-score." In addition, OSTA was calculated as a continuous variable (OSTA score). Osteoporosis was screened using the pred-score and OSTA score, and both scores were analyzed using the receiver operating characteristic curves.

RESULTS

The area under the curves (AUCs) of the pred-score and OSTA score were 0.626-0.875 and 0.817 across surgeons, respectively. When both scores were used, the AUC for screening osteoporosis ranged from 0.821 to 0.915 across surgeons. Significant improvement from AUCs calculated with the pred-score or OSTA score was found in 11 surgeons (73.3%).

CONCLUSION

The combination of X-ray assessment and OSTA can be used to screen for osteoporosis and has the potential to be used as a new simple screening tool in daily clinical practice.

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.

Assessing the utility of osteoporosis self-assessment tool for Asians in patients undergoing hip surgery

Objectives

Diagnosis and treatment of osteoporosis are instrumental in obtaining good outcomes of hip surgery. Measuring bone mineral density (BMD) using dual-energy X-ray absorptiometry (DXA) is the gold standard for diagnosing osteoporosis. However, due to limited access to DXA, there is a need for a screening tool to identify patients at a higher risk of osteoporosis. We analyzed the potential utility of the Osteoporosis Self-assessment Tool for Asians (OSTA) as a screening tool for osteoporosis.

Methods

A total of 1378 female patients who underwent hip surgery at 8 institutions were analyzed. For each patient, the BMD of the proximal femoral region was measured by DXA (DXA-BMD), and the correlation with OSTA score (as a continuous variable) was assessed. Receiver operating characteristic (ROC) curve analysis was performed to assess the ability of OSTA score to predict osteoporosis. Lastly, the OSTA score was truncated to yield an integer (OSTA index) to clarify the percentage of patients with osteoporosis for each index.

Results

DXA-BMD showed a strong correlation with OSTA (r = 0.683; P < 0.001). On ROC curve analysis, the optimal OSTA score cut-off value of -5.4 was associated with 73.8% sensitivity and 80.9% specificity for diagnosis of osteoporosis (area under the curve: 0.842). A decrease in the OSTA index by 1 unit was associated with a 7.3% increase in the probability of osteoporosis.

Conclusions

OSTA is a potentially useful tool for screening osteoporosis in patients undergoing hip surgery. Our findings may help identify high-risk patients who require further investigation using DXA.

Construction and Validation of a Nomogram Clinical Prediction Model for Predicting Osteoporosis in an Asymptomatic Elderly Population in Beijing

BACKGROUND

Based on the high prevalence and occult-onset of osteoporosis, the development of novel early screening tools was imminent. Therefore, this study attempted to construct a nomogram clinical prediction model for predicting osteoporosis.

METHODS

Asymptomatic elderly residents in the training (n = 438) and validation groups (n = 146) were recruited. BMD examinations were performed and clinical data were collected for the participants. Logistic regression analyses were performed. A logistic nomogram clinical prediction model and an online dynamic nomogram clinical prediction model were constructed. The nomogram model was validated by means of ROC curves, calibration curves, DCA curves, and clinical impact curves.

RESULTS

The nomogram clinical prediction model constructed based on gender, education level, and body weight was well generalized and had moderate predictive value (AUC > 0.7), better calibration, and better clinical benefit. An online dynamic nomogram was constructed.

CONCLUSIONS

The nomogram clinical prediction model was easy to generalize, and could help family physicians and primary community healthcare institutions to better screen for osteoporosis in the general elderly population and achieve early detection and diagnosis of the disease.

Osteoporosis Risk in Hemodialysis Patients: The Roles of Gender, Comorbidities, Biochemical Parameters, Health and Diet Literacy

Osteoporosis is a common bone health disorder in hemodialysis patients that is linked with a higher morbidity and mortality rate. While previous studies have explored the associated factors of osteoporosis, there is a lack of studies investigating the impacts of health literacy (HL) and digital healthy diet literacy (DDL) on osteoporosis. Therefore, we aimed to investigate the associations of HL, DDL, and other factors with osteoporosis among hemodialysis patients. From July 2020 to March 2021, a cross-sectional study was conducted on 675 hemodialysis patients in eight hospitals in Vietnam. The data were collected by using the osteoporosis self-assessment tool for Asians (OSTA) and the 12-item short form of the health literacy questionnaire (HLS-SF12) on digital healthy diet literacy (DDL) and hemodialysis dietary knowledge (HDK). In addition, we also collected information about the socio-demographics, the clinical parameters, the biochemical parameters, and physical activity. Unadjusted and adjusted multinomial logistic regression models were utilized in order to investigate the associations. The proportion of patients at low, medium, and high levels of osteoporosis risk was 39.6%, 40.6%, and 19.8%, respectively. In the adjusted models, women had a higher likelihood of osteoporosis risk than men (odds ratio, OR, 3.46; 95% confidence interval, 95% CI, 1.86, 6.44; p < 0.001; and OR, 6.86; 95% CI, 2.96, 15.88; p < 0.001). The patients with rheumatoid arthritis (OR, 4.37; 95% CI, 1.67, 11.52; p = 0.003) and stomach ulcers (OR, 1.95; 95% CI, 1.01, 3.77; p = 0.048) were more likely to have a higher likelihood of osteoporosis risk than those without. The patients who had a higher waist circumference (WC), HL, and DDL were less likely to have a medium level of osteoporosis risk (OR, 0.95; 95% CI, 0.92, 0.98; p = 0.004; OR, 0.92; 95% CI, 0.88, 0.96; p < 0.001; OR, 0.96; 95% CI, 0.93, 0.99; p = 0.017, respectively) and a high level of osteoporosis risk (OR, 0.93; 95% CI, 0.89, 0.97; p = 0.001; OR, 0.89; 95% CI, 0.84, 0.94; p < 0.001; OR, 0.95; 95% CI, 0.91, 0.99; p = 0.008, respectively) compared with a low level of osteoporosis risk and to those with a lower WC, HL, and DDL. In addition, higher levels of hemoglobin (Hb) (OR, 0.79; 95% CI, 0.66, 0.95; p = 0.014), hematocrit (Hct) (OR, 0.95; 95% CI, 0.92, 0.99; p = 0.041), albumin (OR, 0.91; 95% CI, 0.83, 0.99; p = 0.030), and education (OR, 0.37; 95% CI, 0.16, 0.88; p = 0.025) were associated with a lower likelihood of a high level of osteoporosis risk. In conclusion, osteoporosis risk is highly prevalent in hemodialysis patients. Improved HL, DDL, education, WC, albumin, Hb, and Hct levels should be considered in preventing hemodialysis patients from developing osteoporosis.

Predicting the risk of osteoporosis in older Vietnamese women using machine learning approaches

Osteoporosis contributes significantly to health and economic burdens worldwide. However, the development of osteoporosis-related prediction tools has been limited for lower-middle-income countries, especially Vietnam. This study aims to develop prediction models for the Vietnamese population as well as evaluate the existing tools to forecast the risk of osteoporosis and evaluate the contribution of covariates that previous studies have determined to be risk factors for osteoporosis. The prediction models were developed to predict the risk of osteoporosis using machine learning algorithms. The performance of the included prediction models was evaluated based on two scenarios; in the first one, the original test parameters were directly modeled, and in the second the original test parameters were transformed into binary covariates. The area under the receiver operating characteristic curve, the Brier score, precision, recall and F1-score were calculated to evaluate the models' performance in both scenarios. The contribution of the covariates was estimated using the Permutation Feature Importance estimation. Four models, namely, Logistic Regression, Support Vector Machine, Random Forest and Neural Network, were developed through two scenarios. During the validation phase, these four models performed competitively against the reference models, with the areas under the curve above 0.81. Age, height and weight contributed the most to the risk of osteoporosis, while the correlation of the other covariates with the outcome was minor. Machine learning algorithms have a proven advantage in predicting the risk of osteoporosis among Vietnamese women over 50 years old. Additional research is required to more deeply evaluate the performance of the models on other high-risk populations.

Decision Tree Modeling for Osteoporosis Screening in Postmenopausal Thai Women

Osteoporosis is still a serious public health issue in Thailand, particularly in postmenopausal women; meanwhile, new effective screening tools are required for rapid diagnosis. This study constructs and confirms an osteoporosis screening tool-based decision tree (DT) model. Four DT algorithms, namely, classification and regression tree; chi-squared automatic interaction detection (CHAID); quick, unbiased, efficient statistical tree; and C4.5, were implemented on 356 patients, of whom 266 were abnormal and 90 normal. The investigation revealed that the DT algorithms have insignificantly different performances regarding the accuracy, sensitivity, specificity, and area under the curve. Each algorithm possesses its characteristic performance. The optimal model is selected according to the performance of blind data testing and compared with traditional screening tools: Osteoporosis Self-Assessment for Asians and the Khon Kaen Osteoporosis Study. The Decision Tree for Postmenopausal Osteoporosis Screening (DTPOS) tool was developed from the best performance of CHAID’s algorithms. The age of 58 years and weight at a cutoff of 57.8 kg were the essential predictors of our tool. DTPOS provides a sensitivity of 92.3% and a positive predictive value of 82.8%, which might be used to rule in subjects at risk of osteopenia and osteoporosis in a community-based screening as it is simple to conduct.