BMD screening in older women: initial measurement and testing interval

Parametric electrical impedance tomography for measuring bone mineral density in the pelvis using a computational model

Osteoporosis is defined as bone microstructure deterioration resulting a decrease of bone's strength. Measured bone mineral density (BMD) constitutes the main tool for Osteoporosis diagnosis, management, and defines patient's fracture risk. In the present study, parametric electrical impedance tomography (pEIT) method was examined for monitoring BMD, using a computerized simulation model and preliminary real measurements. A numerical solver was developed to simulate surface potentials measured over a 3D computerized pelvis model. Varying cortical and cancellous BMD were simulated by changing bone conductivity and permittivity. Up to 35% and 16% change was found in the real and imaginary modules of the calculated potential, respectively, while BMD changes from 100% (normal) to 60% (Osteoporosis). Negligible BMD relative error was obtained with SNR>60 [dB]. Position changes errors indicate that for long term monitoring, measurement should be taken at the same geometrical configuration with great accuracy. The numerical simulations were compared to actual measurements that were acquired from a healthy male subject using a five electrodes belt bioimpedance device. The results suggest that pEIT may provide an inexpensive easy to use tool for frequent monitoring BMD in small clinics during pharmacological treatment, as a complementary method to DEXA test.

Bone Density Screening and Re-screening in Postmenopausal Women and Older Men

Clinical practice guidelines universally recommend bone mineral density (BMD) screening to identify osteoporosis in women aged 65 years and older. Risk assessment is recommended to guide BMD screening in postmenopausal women under age 65. Insufficient data are available to inform standard ages to start and stop BMD screening in postmenopausal women. Based on longitudinal studies of incident osteoporosis and fracture in postmenopausal women, an initial BMD test should be ordered for all women aged 65, and the frequency of re-screening should be based on age and BMD T score (more frequent testing for older age and lower T score). Although clinical practice guidelines recommend BMD screening according to risk factors for fracture in postmenopausal women under age 65, no standard approach to risk assessment exists. Minimal evidence is available to guide osteoporosis screening in men, but some experts recommend initiation of BMD screening in men at age 70.

Screening for and treatment of osteoporosis: construction and validation of a state-transition microsimulation cost-effectiveness model

This study aimed to document and validate a new cost-effectiveness model of osteoporosis screening and treatment strategies. The state-transition microsimulation model demonstrates strong internal and external validity. It is an important tool for researchers and policy makers to test the cost-effectiveness of osteoporosis screening and treatment strategies.

INTRODUCTION

The objective of this study was to document and validate a new cost-effectiveness model of screening for and treatment of osteoporosis.

METHODS

A state-transition microsimulation model using a lifetime horizon was constructed with seven Markov states (no history of fractures, hip fracture, vertebral fracture, wrist fracture, other fracture, postfracture state, and death) describing the most important clinical outcomes of osteoporotic fractures. Tracker variables were used to record patients' history, such as fracture events, duration of treatment, and time since last screening. The model was validated for Chinese postmenopausal women receiving screening and treatment versus no screening. Goodness-of-fit analyses were performed for internal and external validation. External validity was tested by comparing life expectancy, osteoporosis prevalence rate, and lifetime and 10-year fracture risks with published data not used in the model.

RESULTS

The model represents major clinical facets of osteoporosis-related conditions. Age-specific hip, vertebral, and wrist fracture incidence rates were accurately reproduced (the regression line slope was 0.996, R(2) = 0.99). The changes in costs, effectiveness, and cost-effectiveness were consistent with changes in both one-way and probabilistic sensitivity analysis. The model predicted life expectancy and 10-year any major osteoporotic fracture risk at the age of 65 of 19.01 years and 13.7%, respectively. The lifetime hip, clinical vertebral, and wrist fracture risks at age 50 were 7.9, 29.8, and 18.7% respectively, all consistent with reported data.

CONCLUSIONS

Our model demonstrated good internal and external validity, ensuring it can be confidently applied in economic evaluations of osteoporosis screening and treatment strategies.

Development and validation of osteoporosis risk-assessment model for Korean postmenopausal women

Currently, dual-energy X-ray absorptiometry (DXA) is the gold standard for detecting osteoporosis, but is not recommended for general population screening. Therefore, this study aims to develop an osteoporosis risk-assessment model to identify high-risk individuals among Korean postmenopausal women. Data from 1,209 and 1,046 postmenopausal women who participated in the 2009 and 2010 Korean National Health and Nutrition Examination Survey, respectively, were used for development and validation of an osteoporosis risk-assessment model. Osteoporosis was defined as T score less than or equal to -2.5 at either the femoral neck or lumbar spine. Performance of the candidate models and the Osteoporosis Self assessment Tool for Asians (OSTA) were compared with respect to sensitivity, specificity, and area under the receiver operating characteristics curve (AUC). To compare the developed Korean Osteoporosis Risk-Assessment Model (KORAM) with OSTA, a net reclassification improvement was further calculated. In the development dataset, the prevalence of osteoporosis was 33.9 %. KORAM, consisting of age, weight, and hormone therapy, had a sensitivity of 91.2 %, a specificity of 50.6 %, and an AUC of 0.709 with a specific cut-off score of -9. Comparable results were shown in the validation dataset: sensitivity 84.8 %, specificity 51.6 %, and AUC 0.682. Additionally, risk categorization with KORAM showed improved reclassification over that of OSTA from 7.4 to 41.7 %. KORAM can be easily used as a pre-screening tool to identify candidates for DXA tests. Further studies investigating cost-effectiveness and replicability in other datasets are required to establish the clinical utility of KORAM.