Development and Validation of a Risk Score to Predict the First Hip Fracture in the Oldest Old: A Retrospective Cohort Study

FRAX-derived intervention and assessment thresholds for osteoporosis in ten Middle Eastern countries

This study established FRAX-based age-specific assessment and intervention thresholds for ten Middle Eastern countries where FRAX is currently available, but the lack of specific thresholds has limited its usefulness. The intervention thresholds ranged from 0.6 (Saudi Arabia) to 36.0% (Syria) at the ages of 40 and 90 years, respectively.

INTRODUCTION

Developing fracture risk assessment tools allows physicians to select patients for therapy based on their absolute fracture risk instead of relying solely on bone mineral density (BMD). The most widely used tool is FRAX, currently available in ten Middle Eastern countries. This study aimed to set FRAX-derived assessment and intervention thresholds for individuals aged 40 or above in ten Middle Eastern countries.

METHODS

The age-specific 10-year probabilities of a major osteoporotic fracture (MOF) for a woman with a BMI of 25.0 kg/m2, without BMD and clinical risk factors except for prior fracture, were calculated as intervention Threshold (IT). The upper and lower assessment thresholds were set at 1.2 times the IT and an age-specific 10-year probability of a MOF in a woman with a BMI of 25.0 kg/m2, without BMD, prior fracture, and other clinical risk factors, respectively. IT is utilized to determine treatment or reassurance when BMD facilities are unavailable. However, with BMD facilities, assessment thresholds can offer treatment, reassurance, or bone densitometry based on MOF probability.

RESULTS

The age-specific IT varied from 0.9 to 11.0% in Abu Dhabi, 2.9 to 10% in Egypt, 2.7 to 14.0% in Iran, 1.0 to 28.0% in Jordan, 2.7 to 27.0% in Kuwait, 0.9 to 35.0% in Lebanon, 1.0 to 16.0% in Palestine, 4.1 to 14% in Qatar, 0.6 to 3.7% in Saudi Arabia, and 0.9 to 36.0% in Syria at the age of 40 and 90 years, respectively.

CONCLUSIONS

FRAX-based IT in Middle Eastern countries provides an opportunity to identify individuals with high fracture risk.

Development of a prediction model for postoperative complications and economic burden analysis in older patients with hip fractures

Purpose

The high rates of disability and mortality due to postoperative complications of hip fractures in the elderly, especially the oldest-old individuals, have become an increasingly serious global public health concern. This study aimed to establish a nomogram prediction model and analyze the economic burden to guide clinical decision-making and improve patient prognosis.

Methods

Data of 514 patients aged over 80 years with hip fractures who received surgical treatment were retrospectively collected, and the patients were divided into training and validation cohorts. Independent risk factors for postoperative complications were identified based on logistic regression analysis, and a nomogram was constructed. The model was evaluated for its discrimination and consistency using receiver operating characteristic (ROC) curves and calibration curves, and for its clinical benefit using decision curve analysis (DCA). The economic burden was analyzed using propensity score matching (PSM).

Results

The American Society of Anesthesiologists (ASA) classification ≥Ⅲ, anemia, male sex, diabetes mellitus, and the number of comorbidities were found to be independent risk factors for postoperative complications in oldest-old patients with hip fracture (all P < 0.05). The areas under the curve (AUC) of the nomogram prediction model for the training and validation cohorts were 0.743 and 0.767, respectively, indicating reliable discrimination. The calibration curves and DCA showed that the model has good consistency and high benefits. The direct economic burden of postoperative complications for the patients was US$1045.10.

Conclusions

The nomogram model can accurately quantify the risk of postoperative complications among oldest-old patients with hip fractures and guide clinical professionals to implement early and targeted preventive treatment for high-risk patients.

Validating the Fracture Risk Assessment Tool Score in a US Population-Based Study of Patients With Rheumatoid Arthritis

OBJECTIVE

The World Health Organization fracture risk assessment tool (FRAX) algorithm for risk prediction of major osteoporotic and hip fractures accounts for several risk factors, including rheumatoid arthritis (RA), since individuals with RA have an excess burden of fractures. FRAX has not been validated in population-based RA cohorts in the US. We aimed to determine the accuracy of FRAX predictions for individuals with RA in the US.

METHODS

This retrospective population-based cohort study included residents of Olmsted County, Minnesota, who were followed until death, migration, or last medical record review. Each patient with RA (1987 American College of Rheumatology criteria met in 1980-2007, age 40-89 years) was matched 1:1 on age and sex to an individual without RA from the same underlying population. Ten-year predictions for major osteoporotic and hip fractures were estimated using the FRAX tool. Fractures were ascertained through follow-up, truncated at 10 years. Standardized incidence ratios (SIRs) and 95% CI were calculated to compare observed and predicted fractures.

RESULTS

The study included 662 patients with RA and 658 non-RA comparators (66.8% vs 66.9% female and a mean age of 60.6 vs 60.5 years, respectively). Among patients with RA, 76 major osteoporotic fractures and 21 hip fractures were observed during follow-up (median follow-up: 9.0 years) compared to 67.0 predicted major osteoporotic fractures (SIR 1.13, 95% CI 0.91-1.42) and 23.3 predicted hip fractures (SIR 0.90, 95% CI 0.59-1.38). The observed and predicted major osteoporotic and hip fracture risks were similar for patients with RA and non-RA comparators.

CONCLUSION

The FRAX tool is an accurate method for estimating major osteoporotic and hip fracture risk in patients with RA.

BONEcheck: A digital tool for personalized bone health assessment

Objectives

Osteoporotic fracture is a significant public health burden associated with increased mortality risk and substantial healthcare costs. Accurate and early identification of high-risk individuals and mitigation of their risks is a core part of the treatment and prevention of fractures. Here we introduce a digital tool called 'BONEcheck' for personalized assessment of bone health.

Methods

The development of BONEcheck primarily utilized data from the prospective population-based Dubbo Osteoporosis Epidemiology Study and the Danish Nationwide Registry. BONEcheck has 3 modules: input data, risk estimates, and risk context. Input variables include age, gender, prior fracture, fall incidence, bone mineral density (BMD), comorbidities, and genetic variants associated with BMD.

Results

Based on the input variables, BONEcheck estimates the probability of any fragility fracture and hip fracture within 5 years, subsequent fracture risk, skeletal age, and time to reach osteoporosis. The probability of fracture is shown in both numeric and human icon array formats. The risk is also contextualized within the framework of treatment and management options on Australian guidelines, with consideration given to the potential fracture risk reduction and survival benefits. Skeletal age was estimated as the sum of chronological age and years of life lost due to a fracture or exposure to risk factors that elevate mortality risk.

Conclusions

BONEcheck is an innovative tool that empowers doctors and patients to engage in well-informed discussions and make decisions based on the patient's risk profile. Public access to BONEcheck is available via https://bonecheck.org and in Apple Store (iOS) and Google Play (Android).

Characteristics Associated With 5-Year Fracture Risk Versus 5-Year Mortality Risk Among Late-Life Men

BACKGROUND

Identifying late-life men who might benefit from treatment to prevent fracture is challenging given high mortality. Our objective was to evaluate risks of clinical fracture, hip fracture, and mortality prior to fracture among men aged at least 80 years.

METHODS

Study participants included 3 145 community-dwelling men (mean [standard deviation] age 83 [2.8] years) from the Osteoporotic Fractures in Men (MrOS) Study. We used separate multivariable Fine-Gray competing risk models with prespecified risk factors (age, hip bone mineral density [BMD], recent fracture [<5 years], fall history [previous year], and multimorbidity [# conditions]) to estimate subdistribution hazard ratios and absolute 5-year risks of any clinical fracture and mortality prior to clinical fracture. Secondary analysis considered a hip fracture.

RESULTS

There were 414 incident clinical fractures and 595 deaths without prior fracture within 5 years. BMD, fall history, and recent fracture were strong predictors of clinical fracture. Age and multimorbidity were strong predictors of mortality before fracture. After accounting for competing risks, age, BMD, and fall history were each associated with both risks of hip fracture and mortality before hip fracture. Model discrimination varied from 0.65 (mortality before fracture) to 0.79 (hip fracture). Estimated mortality differed substantially among men with similar clinical fracture risk due to a modest correlation between fracture risk and competing mortality risk = 0.37.

CONCLUSION

In late-life men, strong risk factors for clinical fracture and hip fracture include fall history, BMD, and recent fracture. Osteoporosis drug treatment decisions may be further enhanced by consideration of fracture risk versus overall life expectancy.

Development and validation of the Chinese osteoporosis screening algorithm (COSA) in identification of people with high risk of osteoporosis

Objectives

To enhance the public awareness and facilitate diagnosis of osteoporosis, we aim to develop a new Chinese Osteoporosis Screening Algorithm (COSA) to identify people at high risk of osteoporosis.

Methods

A total of 4747 postmenopausal women and men aged ≥ 50 from the Hong Kong Osteoporosis Study were randomly split into a development (N = 2373) and an internal validation cohort (N = 2374). An external validation cohort comprising 1876 community-dwelling subjects was used to evaluate the positive predictive value (PPV).

Results

Among 11 predictors included, age, sex, weight, and history of fracture were significantly associated with osteoporosis after correction for multiple testing. Age- and sex-stratified models were developed due to the presence of significant sex and age interactions. The area under the curve of the COSA in the internal validation cohort was 0.761 (95% CI, 0.711-0.811), 0.822 (95% CI, 0.792-0.851), and 0.946 (95% CI, 0.908-0.984) for women aged < 65, women aged ≥ 65, and men, respectively. The COSA demonstrated improved reclassification performance when compared to Osteoporosis Self-Assessment Tool for Asians. In the external validation cohort, the PPV of COSA was 40.6%, 59.4%, and 19.4% for women aged < 65, women aged ≥ 65, and men, respectively. In addition, COSA > 0 was associated with an increased 10-year risk of hip fracture in women ≥ 65 (OR, 4.65; 95% CI, 2.24-9.65) and men (OR, 11.51; 95% CI, 4.16-31.81).

Conclusions

We have developed and validated a new osteoporosis screening algorithm, COSA, specific for Hong Kong Chinese.

Effect of competing mortality risks on predictive performance of the QFracture risk prediction tool for major osteoporotic fracture and hip fracture: external validation cohort study in a UK primary care population

Objective

To externally evaluate the QFracture risk prediction tool for predicting the risk of major osteoporotic fracture and hip fracture.

Design

External validation cohort study.

Setting

UK primary care population. Linked general practice (Clinical Practice Research Datalink (CPRD) Gold), mortality registration (Office of National Statistics), and hospital inpatient (Hospital Episode Statistics) data, from 1 January 2004 to 31 March 2016.

Participants

2 747 409 women and 2 684 730 men, aged 30-99 years, with up-to-standard linked data that had passed CPRD checks for at least one year.

Main outcome measures

Two outcomes were modelled based on the QFracture: major osteoporotic fracture and hip fracture. Major osteoporotic fracture was defined as any hip, distal forearm, proximal humerus, or vertebral crush fracture, from general practice, hospital discharge, and mortality data. The QFracture 10 year predicted risk of major osteoporotic fracture and hip fracture was calculated, and performance evaluated versus observed 10 year risk of fracture in the whole population, and in subgroups based on age and comorbidity. QFracture calibration was examined accounting for, and not accounting for, competing risk of mortality from causes other than the major osteoporotic fracture.

Results

2 747 409 women with 95 598 major osteoporotic fractures and 36 400 hip fractures, and 2 684 730 men with 34 321 major osteoporotic fractures and 13 379 hip fractures were included in the analysis. The incidence of all fractures was higher than in the QFracture internal derivation. Competing risk of mortality was more common than fracture from middle age onwards. QFracture discrimination in the whole population was excellent or good for major osteoporotic fracture and hip fracture (Harrell's C statistic in women 0.813 and 0.918, and 0.738 and 0.888 in men, respectively), but was poor to moderate in age subgroups (eg, Harrell's C statistic in women and men aged 85-99 years was 0.576 and 0.624 for major osteoporotic fractures, and 0.601 and 0.637 for hip fractures, respectively). Without accounting for competing risks, QFracture systematically under-predicted the risk of fracture in all models, and more so for major osteoporotic fracture than for hip fracture, and more so in older people. Accounting for competing risks, QFracture still under-predicted the risk of fracture in the whole population, but over-prediction was considerable in older age groups and in people with high comorbidities at high risk of fracture.

Conclusions

The QFracture risk prediction tool systematically under-predicted the risk of fracture (because of incomplete determination of fracture rates) and over-predicted the risk in older people and in those with more comorbidities (because of competing mortality). The use of QFracture in its current form needs to be reviewed, particularly in people at high risk of death from other causes.

Comparison of fracture risk calculators in elderly fallers: a hospital-based cross-sectional study

OBJECTIVE

Elderly patients presenting with falls are known to carry an extremely high risk of future fragility fractures. Current osteoporosis guidelines recommend using fracture risk calculators such as FRAX, QFracture or Garvan to guide management. However, they differ considerably in their inputs and may therefore provide contrasting risk estimations in certain individuals. In this study, we compare these risk calculators in a high-risk cohort of elderly patients admitted to hospital with falls.

DESIGN

Hospital-based cross-sectional study.

SETTING

Secondary care, London, UK.

PARTICIPANTS

Data from 120 consecutive elderly patients who had falls presenting to a single hospital over 4 months were collected. 10-year major and hip fracture risks were calculated using FRAX, QFracture and Garvan. 1-year major and hip fracture risks from QFracture were assessed against prospective incidence of fracture.

RESULTS

Median 10-year major fracture risk was: FRAX 19.5%, QFracture 26.0%, Garvan 32.5%. Median 10-year hip fracture risk was: FRAX 9.6%, QFracture 21.1%, Garvan 6.5%. Correlation between FRAX and QFracture was r=0.672 for major, r=0.676 for hip fracture (both p<0.0001); FRAX and Garvan r=0.778 (p<0.0001) for major, r=0.128 (p=0.206) for hip fracture; QFracture and Garvan r=0.658 (p<0.0001) for major, r=0.318 (p<0.001) for hip fracture. QFracture 1-year predicted major and hip fracture rates were 1.8% and 1.2%, respectively, compared with actual rates of 2.1% and 0%, respectively.

CONCLUSIONS

Although strong correlations between calculators were observed in the study cohort, there were differences of up to 13% between estimated risks. QFracture captured several elderly-specific inputs not considered by other calculators and so projected higher fracture risk than the other calculators. QFracture provided 1-year fracture risks that were comparable with the prospective observed fracture incidence in the cohort. This study has important clinical implications for the use of fracture risk calculators to guide treatment decisions, particularly in the high-risk cohort of elderly patients admitted to hospital following falls.

Identification of risk factors for falls in postmenopausal women: a systematic review and meta-analysis

The purpose of this study was to identify risk factors for falls in postmenopausal women and provide evidence for the primary prevention of falls in postmenopausal women. The protocol for this meta-analysis is registered with PROSPERO (CRD42020170927). We searched PubMed, the Cochrane Library and EMBASE for observational studies on the risk factors for falls in postmenopausal women. Review Manager 5.3 was used to calculate the relative risk (RR) or weighted mean difference (WMD) of potential risk factors related to falls. STATA 14.0 was used for the quantitative evaluation of publication bias. Eleven studies with 42,429 patients from 7 countries were included. The main risk factors for falls in postmenopausal women were patient sociodemographic risk factors (age: WMD = 0.37, 95% CI 0.07 to 0.68; body weight: WMD = 0.88, 95% CI 0.56 to 1.12; BMI: WMD = 0.34, 95% CI 0.21 to 0.46; exercise: RR = 0.97, 95% CI 0.94 to 0.99; and FES-I: WMD = 6.60, 95% CI 0.72 to 12.47) and medical risk factors (dietary calcium intake: WMD = - 16.91, 95% CI - 25.80 to - 8.01; previous fracture history: RR = 1.21, 95% CI 1.13 to 1.29; previous falls: RR = 2.02, 95% CI 1.91 to 2.14; number of diseases, ˃ 2: RR = 1.17, 95% CI 1.11 to 1.23; and number of reported chronic health disorders: WMD = 0.30, 95% CI 0.10 to 0.49). Knowledge of the many risk factors associated with falls in postmenopausal women can aid in fall prevention. However, we cannot rule out some additional potential risk factors (age at the onset of menopause, years since last menstruation, hormone therapy and BMD) that need further clinical research.

The diversity of Fracture Risk Assessment Tool (FRAX)-based intervention thresholds in Asia

Fracture Risk Assessment Tool (FRAX), introduced in 2008, is the most frequently used fracture risk calculator. Many Asian countries have developed own FRAX models to suit their country needs. Only a few Asian countries, however, have developed country-specific intervention thresholds to demarcate high-risk patients. A wide variation is seen in these intervention thresholds partly due to the different approaches used in developing the cutoff values. This paper discusses the diversity of the intervention thresholds in Asian countries and possible reasons. It also discusses the future directions for the countries in the Asian region.