Virtual rehabilitation for patients with osteoporosis or other musculoskeletal disorders: a systematic review.

This study aims to identify effective ways to design virtual rehabilitation to obtain physical improvement (e.g. balance and gait) and support engagement (i.e. motivation) for people with osteoporosis or other musculoskeletal disorders. Osteoporosis is a systemic skeletal disorder and is among the most prevalent diseases globally, affecting 0.5 billion adults. Despite the fact that the number of people with osteoporosis is similar to, or greater than those diagnosed with cardiovascular disease and dementia, osteoporosis does not receive the same recognition. Worldwide, osteoporosis causes 8.9 million fractures annually; it is associated with substantial pain, suffering, disability and increased mortality. The importance of physical therapy as a rehabilitation strategy to avoid osteoporosis fracture cannot be over-emphasised. However, the main rehabilitation challenges relate to engagement and participation. The use of virtual rehabilitation to address such challenges in the delivery of physical improvement is gaining in popularity. As there currently is a paucity of literature applying virtual rehabilitation to patients with osteoporosis, the authors broadened the search parameters to include articles relating to the virtual rehabilitation of other skeletal disorders (e.g. Ankylosing spondylitis, spinal cord injury, motor rehabilitation, etc.). This systematic review initially identified 130 titles, from which 23 articles (involving 539 participants) met all eligibility and selection criteria. Four groups of devices supporting virtual rehabilitation were identified: a head-mounted display, a balance board, a camera and more specific devices. Each device supported physical improvement (i.e. balance, muscle strength and gait) post-training. This review has shown that: (a) each device allowed improvement with different degrees of immersion, (b) the technology choice is dependent on the care need and (c) virtual rehabilitation can be equivalent to and enhance conventional therapy and potentially increase the patient's engagement with physical therapy.

Machine Learning Can Improve Clinical Detection of Low BMD: The DXA-HIP Study.

BACKGROUND: Identification of those at high risk before a fracture occurs is an essential part of osteoporosis management. This topic remains a significant challenge for researchers in the field, and clinicians worldwide. Although many algorithms have been developed to either identify those with a diagnosis of osteoporosis or predict their risk of fracture, concern remains regarding their accuracy and application. Scientific advances including machine learning methods are rapidly gaining appreciation as alternative techniques to develop or enhance risk assessment and current practice. Recent evidence suggests that these methods could play an important role in the assessment of osteoporosis and fracture risk. METHODS: Data used for this study included Dual-energy X-ray Absorptiometry (DXA) bone mineral density and T-scores, and multiple clinical variables drawn from a convenience cohort of adult patients scanned on one of 4 DXA machines across three hospitals in the West of Ireland between January 2000 and November 2018 (the DXA-Heath Informatics Prediction Cohort). The dataset was cleaned, validated and anonymized, and then split into an exploratory group (80%) and a development group (20%) using the stratified sampling method. We first established the validity of a simple tool, the Osteoporosis Self-assessment Tool Index (OSTi) to identify those classified as osteoporotic by the modified International Society for Clinical Densitometry DXA criteria. We then compared these results to seven machine learning techniques (MLTs): CatBoost, eXtreme Gradient Boosting, Neural network, Bagged flexible discriminant analysis, Random forest, Logistic regression and Support vector machine to enhance the discrimination of those classified as osteoporotic or not. The performance of each prediction model was measured by calculating the area under the curve (AUC) with 95% confidence interval (CI), and was compared against the OSTi. RESULTS: A cohort of 13,577 adults aged ≥40 yr at the age of their first scan was identified including 11,594 women and 1983 men. 2102 (18.13%) females and 356 (17.95%) males were identified with osteoporosis based on their lowest T-score. The OSTi performed well in our cohort in both men (AUC 0.723, 95% CI 0.659-0.788) and women (AUC 0.810, 95% CI 0.787-0.833). Four MLTs improved discrimination in both men and women, though the incremental benefit was small. eXtreme Gradient Boosting showed the most promising results: +4.5% (AUC 0.768, 95% CI 0.706-0.829) for men and +2.3% (AUC 0.833, 95% CI 0.812-0.853) for women. Similarly MLTs outperformed OSTi in sensitivity analyses-which excluded those subjects taking osteoporosis medications-though the absolute improvements differed. CONCLUSION: The OSTi retains an important role in identifying older men and women most likely to have osteoporosis by bone mineral density classification. MLTs could improve DXA detection of osteoporosis classification in older men and women. Further exploration of MLTs is warranted in other populations, and with additional data.

Utility of Osteoporosis Self-Assessment Tool as a Screening Tool for Osteoporosis in Irish Men and Women: Results of the DXA-HIP Project.

Many algorithms have been developed and publicised over the past 2 decades for identifying those most likely to have osteoporosis or low BMD, or at increased risk of fragility fracture. The Osteoporosis Self-assessment Tool index (OSTi) is one of the oldest, simplest, and widely used for identifying men and women with low BMD or osteoporosis. OSTi has been validated in many cohorts worldwide but large studies with robust analyses evaluating this or other algorithms in adult populations residing in the Republic of Ireland are lacking, where waiting times for public DXA facilities are long. In this study we evaluated the validity of OSTi in men and women drawn from a sampling frame of more than 36,000 patients scanned at one of 3 centres in the West of Ireland. 18,670 men and women aged 40 years and older had a baseline scan of the lumbar spine femoral neck and total hip available for analysis. 15,964 (86%) were female, 5,343 (29%) had no major clinical risk factors other than age, while 5,093 (27%) had a prior fracture. Approximately 2/3 had a T-score ≤-1.0 at one or more skeletal sites and 1/3 had a T-score ≤-1.0 at all 3 skeletal sites, while 1 in 5 had a DXA T-score ≤-2.5 at one or more skeletal sites and 5% had a T-score ≤-2.5 at all 3 sites. OSTi generally performed well in our population with area under the curve (AUC) values ranging from 0.581 to 0.881 in men and 0.701 to 0.911 in women. The performance of OSTi appeared robust across multiple sub-group analyses. AUC values were greater for women, proximal femur sites, those without prior fractures and those not taking osteoporosis medication. Optimal OSTi cut-points were '2' for men and '0' for women in our study population. OSTi is a simple and effective tool to aid identification of Irish men and women with low BMD or osteoporosis. Use of OSTi could improve the effectiveness of DXA screening programmes for older adults in Ireland.

Patients' preferences for anti-osteoporosis drug treatment: a cross-European discrete choice experiment.

Objectives: To estimate the preferences of osteoporotic patients for medication attributes, and analyse data from seven European countries. Methods: A discrete choice experiment was conducted in Belgium, France, Ireland, the Netherlands, Spain, Switzerland and the UK. Patients were asked to choose repeatedly between two hypothetical unlabelled drug treatments (and an opt-out option) that varied with respect to four attributes: efficacy in reducing the risk of fracture, type of potential common side effects, and mode and frequency of administration. In those countries in which patients contribute to the cost of their treatment directly, a fifth attribute was added: out-of-pocket cost. A mixed logit panel model was used to estimate patients' preferences. Results: In total, 1124 patients completed the experiment, with a sample of between 98 and 257 patients per country. In all countries, patients preferred treatment with higher effectiveness, and 6-monthly subcutaneous injection was always preferred over weekly oral tablets. In five countries, patients also preferred a monthly oral tablet and yearly i.v. injections over weekly oral tablets. In the three countries where the out-of-pocket cost was included as an attribute, lower costs significantly contributed to the treatment preference. Between countries, there were statistically significant differences for 13 out of 42 attribute/level interactions. Conclusion: We found statistically significant differences in patients' preferences for anti-osteoporosis medications between countries, especially for the mode of administration. Our findings emphasized that international treatment recommendations should allow for local adaptation, and that understanding individual preferences is important if we want to improve the quality of clinical care for patients with osteoporosis.

Modelling future bone mineral density: Simplicity or complexity?

BACKGROUND: Osteoporotic fractures are a major global public health issue, leading to patient suffering and death, and considerable healthcare costs. Bone mineral density (BMD) measurement is important to identify those with osteoporosis and assess their risk of fracture. Both the absolute BMD and the change in BMD over time contribute to fracture risk. Predicting future fracture in individual patients is challenging and impacts clinical decisions such as when to intervene or repeat BMD measurement. Although the importance of BMD change is recognised, an effective way to incorporate this marginal effect into clinical algorithms is lacking. METHODS: We compared two methods using longitudinal DXA data generated from subjects with two or more hip DXA scans on the same machine between 2000 and 2018. A simpler statistical method (ZBM) was used to predict an individual's future BMD based on the mean BMD and the standard deviation of the reference group and their BMD measured in the latest scan. A more complex deep learning (DL)-based method was developed to cope with multidimensional longitudinal data, variables extracted from patients' historical DXA scan(s), as well as features drawn from the ZBM method. Sensitivity analyses of several subgroups was conducted to evaluate the performance of the derived models. RESULTS: 2948 white adults aged 40-90 years met our study inclusion: 2652 (90 %) females and 296 (10 %) males. Our DL-based models performed significantly better than the ZBM models in women, particularly our Hybrid-DL model. In contrast, the ZBM-based models performed as well or better than DL-based models in men. CONCLUSIONS: Deep learning-based and statistical models have potential to forecast future BMD using longitudinal clinical data. These methods have the potential to augment clinical decisions regarding when to repeat BMD testing in the assessment of osteoporosis.

Ireland DXA-FRAX may differ significantly and substantially to Web-FRAX.

Appropriate use of FRAX reduces the number of people requiring DXA scans, while contemporaneously determining those most at risk. We compared the results of FRAX with and without inclusion of BMD. It suggests clinicians to carefully consider the importance of BMD inclusion in fracture risk estimation or interpretation in individual patients. PURPOSE: FRAX is a widely accepted tool to estimate the 10-year risk of hip and major osteoporotic fracture in adults. Prior calibration studies suggest this works similarly with or without the inclusion of bone mineral density (BMD). The purpose of the study is to compare within-subject differences between FRAX estimations derived using DXA and Web software with and without the inclusion of BMD. METHOD: A convenience cohort was used for this cross-sectional study, consisting of 1254 men and women aged between 40 and 90 years who had a DXA scan and complete validated data available for analysis. FRAX 10-year estimations for hip and major osteoporotic fracture were calculated using DXA software (DXA-FRAX) and the Web tool (Web-FRAX), with and without BMD. Agreements between estimates within each individual subject were examined using Bland-Altman plots. We performed exploratory analyses of the characteristics of those with very discordant results. RESULTS: Overall median DXA-FRAX and Web-FRAX 10-year hip and major osteoporotic fracture risk estimations which include BMD are very similar: 2.9% vs. 2.8% and 11.0% vs. 11% respectively. However, both are significantly lower than those obtained without BMD: 4.9% and 14% respectively, P < 0.001. Within-subject differences between hip fracture estimates with and without BMD were < 3% in 57% of cases, between 3 and 6% in 19% of cases, and > 6% in 24% of cases, while for major osteoporotic fractures such differences are < 10% in 82% of cases, between 10 and 20% in 15% of cases, and > 20% in 3% of cases. CONCLUSIONS: Although there is excellent agreement between the Web-FRAX and DXA-FRAX tools when BMD is incorporated, sometimes there are very large differences for individuals between results obtained with and without BMD. Clinicians should carefully consider the importance of BMD inclusion in FRAX estimations when assessing individual patients.

Prevalence of Low Bone Mass and Osteoporosis in Ireland: the Dual-Energy X-Ray Absorptiometry (DXA) Health Informatics Prediction (HIP) Project.

Osteoporosis is a common disease that has a significant impact on patients, healthcare systems, and society. World Health Organization (WHO) diagnostic criteria for postmenopausal women were established in 1994 to diagnose low bone mass (osteopenia) and osteoporosis using dual-energy X-ray absorptiometry (DXA)-measured bone mineral density (BMD) to help understand the epidemiology of osteoporosis, and identify those at risk for fracture. These criteria may also apply to men ≥50 years, perimenopausal women, and people of different ethnicity. The DXA Health Informatics Prediction (HIP) project is an established convenience cohort of more than 36,000 patients who had a DXA scan to explore the epidemiology of osteoporosis and its management in the Republic of Ireland where the prevalence of osteoporosis remains unknown. In this article we compare the prevalence of a DXA classification low bone mass (T-score < -1.0) and of osteoporosis (T-score ≤ -2.5) among adults aged ≥40 years without major risk factors or fractures, with one or more major risk factors, and with one or more major osteoporotic fractures. A total of 33,344 subjects met our study inclusion criteria, including 28,933 (86.8%) women; 9362 had no fractures or major risk factors, 14,932 had one or more major clinical risk factors, and 9050 had one or more major osteoporotic fractures. The prevalence of low bone mass and osteoporosis increased significantly with age overall. The prevalence of low bone mass and osteoporosis was significantly greater among men and women with major osteoporotic fractures than healthy controls or those with clinical risk factors. Applying our results to the national population census figure of 5,123,536 in 2022 we estimate between 1,039,348 and 1,240,807 men and women aged ≥50 years have low bone mass, whereas between 308,474 and 498,104 have osteoporosis. These data are important for the diagnosis of osteoporosis in clinical practice, and national policy to reduce the illness burden of osteoporosis. © 2023 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.

Bone mineral density and fractures in patients with rheumatoid arthritis: the DXA-HIP project.

Objectives: RA is a chronic disabling disease affecting 0.5-1% of adults worldwide. People with RA have a greater prevalence of multimorbidity, particularly osteoporosis and associated fractures. Recent studies suggest that fracture risk is related to both non-RA and RA factors, whose importance is heterogeneous across studies. This study seeks to compare baseline demographic and DXA data across three cohorts: healthy controls, RA patients and a non-RA cohort with major risk factors and/or prior major osteoporotic fracture (MOF). Methods: This is a cross-sectional study using data collected from three DXA centres in the west of Ireland from January 2000 to November 2018. Results: Data were available for 30 503 subjects who met our inclusion criteria: 9539 (31.3%) healthy controls, 1797 (5.9%) with RA and 19 167 (62.8%) others. Although age, BMI and BMD were similar between healthy controls, the RA cohort and the other cohort, 289 (16.1%) RA patients and 5419 (28.3%) of the non-RA cohort had prior MOF. In the RA and non-RA cohorts, patients with previous MOF were significantly older and had significantly lower BMD at the femoral neck, total hip and spine. Conclusion: Although age, BMI and BMD were similar between a healthy control cohort and RA patients and others with major fracture risk factors, those with a previous MOF were older and had significantly lower BMD at all three measured skeletal sites. Further studies are needed to address the importance of these and other factors for identifying those RA patients most likely to experience fractures.

Conceptual design of the dual X-ray absorptiometry health informatics prediction system for osteoporosis care.

Osteoporotic fractures are a major and growing public health problem, which is strongly associated with other illnesses and multi-morbidity. Big data analytics has the potential to improve care for osteoporotic fractures and other non-communicable diseases (NCDs), reduces healthcare costs and improves healthcare decision-making for patients with multi-disorders. However, robust and comprehensive utilization of healthcare big data in osteoporosis care practice remains unsatisfactory. In this paper, we present a conceptual design of an intelligent analytics system, namely, the dual X-ray absorptiometry (DXA) health informatics prediction (HIP) system, for healthcare big data research and development. Comprising data source, extraction, transformation, loading, modelling and application, the DXA HIP system was applied in an osteoporosis healthcare context for fracture risk prediction and the investigation of multi-morbidity risk. Data was sourced from four DXA machines located in three healthcare centres in Ireland. The DXA HIP system is novel within the Irish context as it enables the study of fracture-related issues in a larger and more representative Irish population than previous studies. We propose this system is applicable to investigate other NCDs which have the potential to improve the overall quality of patient care and substantially reduce the burden and cost of all NCDs.

How does proximal femur BMD of healthy Irish adults compare to NHANES III? Results of the DXA-HIP Project.

This study examines the distribution of proximal femur bone mineral density in a cohort of healthy Irish adults. These values are similar to those of the NHANES III Caucasian cohorts, supporting international recommendations to use this reference group for calculating DXA T-scores and Z-scores in Irish adults. INTRODUCTION: Bone mineral density (BMD) is widely used in the assessment and monitoring of osteoporosis. International guidelines recommend referencing proximal femur BMD measurements to NHANES III values to calculate T-scores and Z-scores, but their validity for the Irish population has not been established. In this study, we compare BMD values of healthy Irish Caucasian adults to those of Caucasian men and women in the NHANES III cohort study. METHODS: Men and women without bone disease and/or major risk factors for fracture, and/or not taking osteoporosis medication who had a screening DXA scan (GE Lunar, Madison, USA) at one of 3 centres in the West of Ireland were selected for this study. We calculated the mean and standard deviation (SD) used by GE for calculating white female NHANES III T-scores at the femoral neck and total hip sites, and used these values to calculate white female T-scores for men and women across each decade in our study sample. We calculated mean white female T-scores for each decade for both Caucasian men and women in the NHANES III cohort using the published data. Finally, we plotted these results against those of our study population. RESULTS: In total, 6729 (18.5%) of 36,321 adults were included in our analyses, including 5923 (88%) women. The majority of the study population were aged between 40 and 89 years. Our results show that the proximal femur BMD of healthy Irish men and women is broadly similar to that of the NHANES III reference population, especially middle-aged adults. Results differ for very young and very old adults, likely reflecting the small sample size and a referral bias. Further studies of these populations and other manufacturers could help clarify these uncertainties. CONCLUSIONS: Our results support using the NHANES III reference population to calculate proximal femur adult T-scores and Z-scores to establish the presence or prevalence of osteoporosis in Ireland.

mAbs in nonlupus autoimmune rheumatic disease.

PURPOSE OF REVIEW: mAb therapies are being used more frequently in all medical conditions, including the autoimmune rheumatic diseases. Autoimmune myositis, vasculitis and primary Sjogren's syndrome are rare diseases in which these therapies are being used without the support of large-scale randomized controlled trials. We have summarized the evidence that does exist to support the use of mAb therapies in these diseases. RECENT FINDINGS: There have been a number of open-label trials and case reports using mainly rituximab and antitumour necrosis factor-alpha agents to treat these diseases. The results have in general shown efficacy, the main exception being the use of antitumour necrosis factor-alpha agents with cyclophosphamide for the treatment of vasculitis. SUMMARY: mAb therapies have shown efficacy in the treatment of patients with autoimmune rheumatic diseases who have failed conventional therapy. Their use ahead of conventional immunosuppressive strategies cannot be supported on the basis of current evidence.

The Irish dual-energy X-ray absorptiometry (DXA) Health Informatics Prediction (HIP) for Osteoporosis Project.

PURPOSE: The purpose of the Irish dual-energy X-ray absorptiometry (DXA) Health Informatics Prediction (HIP) for Osteoporosis Project is to create a large retrospective cohort of adults in Ireland to examine the validity of DXA diagnostic classification, risk assessment tools and management strategies for osteoporosis and osteoporotic fractures for our population. PARTICIPANTS: The cohort includes 36 590 men and women aged 4-104 years who had a DXA scan between January 2000 and November 2018 at one of 3 centres in the West of Ireland. FINDINGS TO DATE: 36 590 patients had at least 1 DXA scan, 6868 (18.77%) had 2 scans and 3823 (10.45%) had 3 or more scans. There are 364 unique medical disorders, 186 unique medications and 46 DXA variables identified and available for analysis. The cohort includes 10 349 (28.3%) individuals who underwent a screening DXA scan without a clear fracture risk factor (other than age), and 9947 (27.2%) with prevalent fractures at 1 of 44 skeletal sites. FUTURE PLANS: The Irish DXA HIP Project plans to assess current diagnostic classification and risk prediction algorithms for osteoporosis and fractures, identify the risk predictors for osteoporosis and develop novel, accurate and personalised risk prediction tools, by using the large multicentre longitudinal follow-up cohort. Furthermore, the dataset may be used to assess, and possibly support, multimorbidity management due to the large number of variables collected in this project.

Adherence and persistence to urate-lowering therapies in the Irish setting.

To identify adherence and persistence levels with urate-lowering therapies using the national administrative pharmacy claim database. This was a retrospective, pharmacy claims-based analysis of dispensed anti-gout medications on the Irish national HSE-PCRS scheme database between January 2008 and December 2012. Adherence is defined by the medication possession ratio (MPR), and patients were considered to be adherent if the MPR ≥80 % (good adherers) in any given time period. Persistence was defined as continued use of therapy with no periods exceeding a refill gap of >63 days (9 weeks). Logistic regression analysis was used to predict odd ratios (OR) and 95 % confidence interval (CI) for persistence and adherence in relation to age, gender and level of comorbidity. There was a 53 % increase in the number of patients prescribed anti-gout medications between 2008 and 2012 with an increase of 27 % in the associated ingredient cost of these medications. Allopurinol accounted for 87 % of the prescribing and febuxostat accounted for a further 9 %. In patients who started on 100 mg allopurinol, only 14.6 % were titrated to the 300 mg dose. For all those initiating urate-lowering therapies, 45.8 % of patients were persistent with treatment at 6 months decreasing to 22.6 % at 12 months. In multivariate analysis, females had poorer adherence (OR = 0.83 (0.77-0.90)), and increasing age was associated with increased adherence (OR = 4.19 (2.53-6.15)) Increasing comorbidity score was associated with increased adherence and persistence at 6 months (OR = 0.68 (0.59-0.79)). Adherence with anti-gout medications in this study cohort was relatively low. Sustained treatment for gouty arthritis is essential in the prevention of serious adverse outcomes.Significance and Innovations-Poor adherence to medications prescribed to patients for the management of chronic diseases such as gout is an ongoing problem which urgently needs to be addressed.-Some of the reasons identified for poor adherence to anti-gout medications include the risk of flare of acute gout with the initiation of urate-lowering therapy (ULT), poor response to ULT and persistence of attacks of acute gout, suboptimal dosing of allopurinol therapy and intolerance of allopurinol.-The results of this study identified adherence and persistence rates of approximately 50 % at 6 months which is in line if not lower than many of the other published studies to date which have measured adherence and persistence using pharmacy claims databases.-The results of poor adherence and persistence affect both the health of the patients with financial implications for the healthcare service.

Serum albumin as an outcome predictor in hospital emergency medical admissions.

BACKGROUND: To examine the relationship between admission serum albumin and 30-day mortality during an emergency medical admission. METHODS: An analysis was performed of all emergency medical patients admitted to St. James's Hospital (SJH), Dublin between 1st January 2002 and 31st December 2008, using the hospital in-patient enquiry (HIPE) system, linked to the patient administration system, and laboratory datasets. Mortality was defined as an in-hospital death within 30 days. Logistic regression was used to calculate unadjusted and adjusted odds ratios (ORs) and 95% confidence intervals for defined albumin subsets. FINDINGS: Univariate analysis using predefined criteria based on distribution, identified the groups of <10% and between 10 and 25% of the serum albumin frequency distribution as at increased mortality risk. Their mortality rates were 31.7% and 15.4% respectively; their unadjusted odds rates were 6.35 (5.68, 7.09) and 2.11 (1.90, 2.34). Patients in the lowest 25% of the distribution had a 30-day mortality of 19.9% and this significantly increased risk persisted, after adjustment for other outcome predictors including co-morbidity and illness severity (OR 2.95 (2.49, 3.48): p<0.0001). INTERPRETATION: Serum albumin is predictive of 30-day mortality in emergency medical patients; mortality is non-linearly related to baseline albumin. The disproportionate increased death risk for patients in the lowest 25% of the frequency distribution (<36 g/L) is not due to co-morbidity factors or acute illness severity.