What is the validity of self-reported fractures?

Red flags to screen for vertebral fracture in people presenting with low back pain

BACKGROUND

Low back pain is a common presentation across different healthcare settings. Clinicians need to confidently be able to screen and identify people presenting with low back pain with a high suspicion of serious or specific pathology (e.g. vertebral fracture). Patients identified with an increased likelihood of having a serious pathology will likely require additional investigations and specific treatment. Guidelines recommend a thorough history and clinical assessment to screen for serious pathology as a cause of low back pain. However, the diagnostic accuracy of recommended red flags (e.g. older age, trauma, corticosteroid use) remains unclear, particularly those used to screen for vertebral fracture.

OBJECTIVES

To assess the diagnostic accuracy of red flags used to screen for vertebral fracture in people presenting with low back pain. Where possible, we reported results of red flags separately for different types of vertebral fracture (i.e. acute osteoporotic vertebral compression fracture, vertebral traumatic fracture, vertebral stress fracture, unspecified vertebral fracture).

SEARCH METHODS

We used standard, extensive Cochrane search methods. The latest search date was 26 July 2022.

SELECTION CRITERIA

We considered primary diagnostic studies if they compared results of history taking or physical examination (or both) findings (index test) with a reference standard test (e.g. X-ray, magnetic resonance imaging (MRI), computed tomography (CT), single-photon emission computerised tomography (SPECT)) for the identification of vertebral fracture in people presenting with low back pain. We included index tests that were presented individually or as part of a combination of tests.

DATA COLLECTION AND ANALYSIS

Two review authors independently extracted data for diagnostic two-by-two tables from the publications or reconstructed them using information from relevant parameters to calculate sensitivity, specificity, and positive (+LR) and negative (-LR) likelihood ratios with 95% confidence intervals (CIs). We extracted aspects of study design, characteristics of the population, index test, reference standard, and type of vertebral fracture. Meta-analysis was not possible due to heterogeneity of studies and index tests, therefore the analysis was descriptive. We calculated sensitivity, specificity, and LRs for each test and used these as an indication of clinical usefulness. Two review authors independently conducted risk of bias and applicability assessment using the QUADAS-2 tool.

MAIN RESULTS

This review is an update of a previous Cochrane Review of red flags to screen for vertebral fracture in people with low back pain. We included 14 studies in this review, six based in primary care, five in secondary care, and three in tertiary care. Four studies reported on 'osteoporotic vertebral fractures', two studies reported on 'vertebral compression fracture', one study reported on 'osteoporotic and traumatic vertebral fracture', two studies reported on 'vertebral stress fracture', and five studies reported on 'unspecified vertebral fracture'. Risk of bias was only rated as low in one study for the domains reference standard and flow and timing. The domain patient selection had three studies and the domain index test had six studies rated at low risk of bias. Meta-analysis was not possible due to heterogeneity of the data. Results from single studies suggest only a small number of the red flags investigated may be informative. In the primary healthcare setting, results from single studies suggest 'trauma' demonstrated informative +LRs (range: 1.93 to 12.85) for 'unspecified vertebral fracture' and 'osteoporotic vertebral fracture' (+LR: 6.42, 95% CI 2.94 to 14.02). Results from single studies suggest 'older age' demonstrated informative +LRs for studies in primary care for 'unspecified vertebral fracture' (older age greater than 70 years: 11.19, 95% CI 5.33 to 23.51). Results from single studies suggest 'corticosteroid use' may be an informative red flag in primary care for 'unspecified vertebral fracture' (+LR range: 3.97, 95% CI 0.20 to 79.15 to 48.50, 95% CI 11.48 to 204.98) and 'osteoporotic vertebral fracture' (+LR: 2.46, 95% CI 1.13 to 5.34); however, diagnostic values varied and CIs were imprecise. Results from a single study suggest red flags as part of a combination of index tests such as 'older age and female gender' in primary care demonstrated informative +LRs for 'unspecified vertebral fracture' (16.17, 95% CI 4.47 to 58.43). In the secondary healthcare setting, results from a single study suggest 'trauma' demonstrated informative +LRs for 'unspecified vertebral fracture' (+LR: 2.18, 95% CI 1.86 to 2.54) and 'older age' demonstrated informative +LRs for 'osteoporotic vertebral fracture' (older age greater than 75 years: 2.51, 95% CI 1.48 to 4.27). Results from a single study suggest red flags as part of a combination of index tests such as 'older age and trauma' in secondary care demonstrated informative +LRs for 'unspecified vertebral fracture' (+LR: 4.35, 95% CI 2.92 to 6.48). Results from a single study suggest when '4 of 5 tests' were positive in secondary care, they demonstrated informative +LRs for 'osteoporotic vertebral fracture' (+LR: 9.62, 95% CI 5.88 to 15.73). In the tertiary care setting, results from a single study suggest 'presence of contusion/abrasion' was informative for 'vertebral compression fracture' (+LR: 31.09, 95% CI 18.25 to 52.96).

AUTHORS' CONCLUSIONS

The available evidence suggests that only a few red flags are potentially useful in guiding clinical decisions to further investigate people suspected to have a vertebral fracture. Most red flags were not useful as screening tools to identify vertebral fracture in people with low back pain. In primary care, 'older age' was informative for 'unspecified vertebral fracture', and 'trauma' and 'corticosteroid use' were both informative for 'unspecified vertebral fracture' and 'osteoporotic vertebral fracture'. In secondary care, 'older age' was informative for 'osteoporotic vertebral fracture' and 'trauma' was informative for 'unspecified vertebral fracture'. In tertiary care, 'presence of contusion/abrasion' was informative for 'vertebral compression fracture'. Combinations of red flags were also informative and may be more useful than individual tests alone. Unfortunately, the challenge to provide clear guidance on which red flags should be used routinely in clinical practice remains. Further research with primary studies is needed to improve and consolidate our current recommendations for screening for vertebral fractures to guide clinical care.

Risk factors predicting the 'time to first fracture' and its association with imminent fractures: a substudy of the FRISBEE cohort

Only previous glucocorticoid use and rheumatoid arthritis were predictors of an early fracture (< 2 years after inclusion). A shorter 'time to first fracture' was not an independent clinical risk factor for imminent fractures.

PURPOSE

Risk factors for fragility fractures independent of BMD were assessed in several prediction models. However, predictors of a shorter 'time to first fracture' and its impact on imminent fractures are unknown.

METHODS

We studied the concept of 'time to first fracture' in the FRISBEE ("Fracture RIsk Brussels Epidemiological Enquiry") cohort (3560 postmenopausal women). Validated fractures were divided into 3 groups: first fracture < 2 years, 2-5 years, and > 5 years after inclusion. Factors associated with first fracture risk were evaluated with uni- and multivariate analyses using Cox modeling. We examined 'time to first fracture' as a risk factor for imminent fractures in untreated subjects and in those receiving pharmacological treatment.

RESULTS

Classical risk factors (age, prior fracture, fall history and low BMD) were associated with first fracture in all groups. Previous glucocorticoids and rheumatoid arthritis (RA) were predictors for fracture < 2 years. Imminent fractures were similar in subjects with or without osteoporosis treatment, despite a higher estimated 10-year risk of fragility fracture in those treated, suggesting that treatment is efficient. 'Time to first fracture' was not an independent risk factor for imminent fractures.

CONCLUSION

Among the risk factors considered, previous glucocorticoid use and RA were predictors for early fracture, consistent with the concept of very high risk. The 'time to first validated fracture' was not an independent risk factor for imminent fractures. Patients with a first osteoporotic fracture should thus be considered at very high risk for re-fracture, independent of the 'time to first fracture'.

Selection for treatment of patients at high risk of fracture by the short versus long term prediction models - data from the Belgian FRISBEE cohort

Our imminent model was less sensitive but more selective than FRAX® in the choice of treatment to prevent imminent fractures. This new model decreased NNT by 30%, which could reduce the treatment costs. In the Belgian FRISBEE cohort, the effect of recency further decreased the selectivity of FRAX®.

PURPOSE

We analyzed the selection for treatment of patients at high risk of fracture by the Belgian FRISBEE imminent model and the FRAX® tool.

METHODS

We identified in the FRISBEE cohort subjects who sustained an incident MOF (mean age 76.5 ± 6.8 years). We calculated their estimated 10-year risk of fracture using FRAX® before and after adjustment for recency and the 2-year probability of fracture using the FRISBEE model.

RESULTS

After 6.8 years of follow-up, we validated 480 incident and 54 imminent MOFs. Of the subjects who had an imminent fracture, 94.0% had a fracture risk estimated above 20% by the FRAX® before correction for recency and 98.1% after adjustment, with a specificity of 20.2% and 5.9%, respectively. The sensitivity and specificity of the FRISBEE model at 2 years were 72.2% and 55.4%, respectively, for a threshold of 10%. For these thresholds, 47.3% of the patients were identified at high risk in both models before the correction, and 17.2% of them had an imminent MOF. The adjustment for recency did not change this selection. Before the correction, 34.2% of patients were selected for treatment by FRAX® only, and 18.8% would have had an imminent MOF. This percentage increased to 47% after the adjustment for recency, but only 6% of those would suffer a MOF within 2 years.

CONCLUSION

In our Belgian FRISBEE cohort, the imminent model was less sensitive but more selective in the selection of subjects in whom an imminent fracture should be prevented, resulting in a lower NNT. The correction for recency in this elderly population further decreased the selectivity of FRAX®. These data should be validated in additional cohorts before using them in everyday practice.

Which parameters are more reliable in proximal humerus surgery in terms of the axillary nerve?

OBJECTIVES

This study aims to examine the reliability of the old and new parameters in determining the axillary nerve safe area for surgical interventions in the proximal humerus by measuring the distances between the top of the humeral head, the top of the greater tuberosity, the base of the greater tuberosity, and the acromion and axillary nerve.

PATIENTS AND METHODS

Between 2020 and 2022, a total of 52 shoulders of 26 fresh frozen male human cadavers (mean age: 46±25.5 years; range, 28 to 64 years), 26 right and 26 left were included. The deltopectoral approach was used. The intersection distances of the anterolateral end of the acromion, the top of the humeral head, the top of the tuberculum majus, and the base of the tuberculum majus with the N. axillaries were determined. All measurements were performed using the Microscribe® G2X.

RESULTS

The mean distance from the top of the tuberculum majus to the axillary nerve (shown as "A") was measured as 4.36±0.17 cm and 4±0.21 cm on the right and left, respectively. The mean distance from the center of the base of the tuberculum majus to the axillary nerve (shown as "B") was measured as 1.27±0.18 cm and 1.24±0.11 cm on the right and left, respectively. The mean distance from the apex of the humeral head to the axillary nerve (shown as "C") was measured as 6.15±0.39 cm and 5.89±0.34 cm on the right and left, respectively. The mean distance between the anterolateral end of the acromion (shown as "D") was measured as 6.15±0.39 cm and 5.89±0.34 cm on the right and left, respectively. There was a moderate positive correlation between distances A and B measured on the right and left side, respectively (r=0.484; p=0.012) (r=0.454; p=0.020).

CONCLUSION

A strong positive correlation was found between the distances A and B. The A, B, and C parameters had a weak correlation with parameter D. The anatomical parameters A and B was found to be less variable and more reliable than parameter D.

Fracture distribution in postmenopausal women: a FRISBEE sub-study

We registered 1336 incident-validated fractures in a prospective cohort of 3560 postmenopausal (60-85 years) Belgian women (mean follow-up of 9.1 years). The increase of fracture incidence with age varied widely depending on the fracture site and was significantly steeper for central than for peripheral fractures (e.g., not significant for the ankle).

INTRODUCTION

The epidemiology of fracture sites other than MOFs has been less studied. We examined the incidence of fractures according to their sites in a prospective cohort of postmenopausal Belgian women.

METHODS

Three thousand five hundred sixty postmenopausal women, aged 60-85 years old, were recruited from 2007 to 2013 and surveyed yearly (FRISBEE). The number of validated incident fractures was recorded and analyzed in relation to age and the fracture site.

RESULTS

One thousand three hundred thirty-six fractures were recorded after a mean follow-up of 9.1 years. Seven hundred fifty-six fractures (57%) were MOFs and 580 (43%) non-MOFs, while 813 (61%) were central and 523 (39%) peripheral. The increase of fracture incidence with age differed between fracture sites and was steeper for central than for peripheral fractures. The ratio of MOFs to non-MOFs increased significantly with age, from 1.10 (95% CI: 0.83-1.45) for the 60-69 to 1.69 [1.42-2.01] for the 80-89-year subgroup (P = 0.017). This was also true for central versus peripheral fracture. We differentiated three groups of fracture incidence evolution with age: fractures with a mean increase/decade (compared to the 60-69 age group) of less than 1.5, 1.5-2.0, and 2.0-3.0. The lowest increase was seen for most peripheral fractures, whereas the greatest increase included hip, scapula, pelvis, ribs, and spine fractures.

CONCLUSION

The increase of fracture incidence with age varied widely depending on the fracture site, and the ratio of MOFs to non-MOFs rose significantly with age. Some peripheral fractures, such as the ankle, did not increase significantly with age, suggesting that bone fragility does not play a major role in their occurrence.

Association Among Cognition, Frailty, and Falls and Self-Reported Incident Fractures: Results From the Canadian Longitudinal Study on Aging (CLSA)

Cognition, frailty, and falls have been examined independently as potential correlates of fracture risk, but not simultaneously. Our objective was to explore the association between cognition, frailty, and falls and self-reported incident fractures to determine if these factors show significant independent associations or interactions. We included participants who completed the Canadian Longitudinal Study on Aging (CLSA) 2012-2015 baseline comprehensive assessment, did not experience any self-reported fractures in the year prior to cohort recruitment, and completed the follow-up questionnaire at year 3 (n = 26,982). We compared all baseline cognitive measures available in the CLSA, the Rockwood Frailty Index (FI), and presence of self-reported falls in the past 12 months in those with versus without self-reported incident fractures in year 3 of follow-up. We used multivariable logistic regression adjusted for covariates and examined two-way interactions between cognition, frailty, and prior falls. CLSA specified analytic weights were applied. The mean ± standard error (SE) age of participants was 59.5 ± 0.1 years and 52.2% were female. A total of 715 participants (2.7%) self-reported incident fractures at 3-year follow-up. Participants who experienced incident fractures had similar baseline cognition scores (mean ± SE; Rey Auditory Verbal Learning Test [RAVLT]: Immediate recall 6.1 ± 0.1 versus 5.9 ± 0.0; standardized difference [d] 0.124); higher FI scores (mean ± SE; FI 0.134 ± 0.005 versus 0.116 ± 0.001; d 0.193), and a greater percentage had fallen in the past 12 months (weighted n [%] 518 [7.2] versus 919 [3.5]; d 0.165). FI (each increment of 0.08) was associated with a significantly increased risk of self-reported incident fractures in participants of all ages and those aged 65 years or older (adjusted odd ratio [OR] 1.24, 95% confidence limit [CL] 1.10-1.40; adjusted OR 1.44, 95% CL 1.11-1.52, respectively). The adjusted odds for self-reported incident fractures in participants of all ages was also significantly associated with falls in the past 12 months prior to baseline (adjusted OR 1.83; 95% CL 1.13-2.97), but not in those aged 65 years or older. No interactions between cognition, frailty, and prior falls were found. However, considering the relatively young age of our cohort, it may be appropriate to make strong inferences in individuals older than 65 years of age. © 2022 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.

Fragility Fractures in Postmenopausal Women: Development of 5-Year Prediction Models Using the FRISBEE Study

CONTEXT

Individualized fracture risk may help to select patients requiring a pharmacological treatment for osteoporosis. FRAX and the Garvan fracture risk calculators are the most used tools, although their external validation has shown significant differences in their risk prediction ability.

OBJECTIVE AND METHODS

Using data from the Fracture Risk Brussels Epidemiological Enquiry study, a cohort of 3560 postmenopausal women aged 60 to 85 years, we aimed to construct original 5-year fracture risk prediction models using validated clinical risk factors (CRFs). Three models of competing risk analysis were developed to predict major osteoporotic fractures (MOFs), all fractures, and central fractures (femoral neck, shoulder, clinical spine, pelvis, ribs, scapula, clavicle, sternum).

RESULTS

Age, a history of fracture, and hip or spine BMD were predictors common to the 3 models. Excessive alcohol intake and the presence of comorbidities were specific additional CRFs for MOFs, a history of fall for all fractures, and rheumatoid arthritis for central fractures. Our models predicted the fracture probability at 5 years with an acceptable accuracy (Brier scores ≤ 0.1) and had a good discrimination power (area under the receiver operating curve of 0.73 for MOFs and 0.72 for central fractures) when internally validated by bootstrap. Three simple nomograms, integrating significant CRFs and the mortality risk, were constructed for different fracture sites. In conclusion, we derived 3 models predicting fractures with an acceptable accuracy, particularly for MOFs and central fractures. The models are based on a limited number of CRFs, and we constructed nomograms for use in clinical practice.

MOF/Hip Fracture Ratio in a Belgian Cohort of Post-menopausal Women (FRISBEE): Potential Impact on the FRAX® Score

The ratio between major osteoporotic fractures (MOFs) and hip fractures in the Belgian FRAX® tool to predict fractures is currently based on Swedish data. We determined these ratios in a prospective cohort of Belgian postmenopausal women. 3560 women, aged 60-85 years (70.1 ± 6.4 years), were included in a prospective study from 2007 to 2013 and surveyed yearly (FRISBEE). We analyzed the number of validated incident fractures until October 2020 by age and sites and compared the MOFs/hip ratios in this cohort with those from the Swedish databases. We registered 1336 fractures (mean follow-up of 9.1 years). The MOFs/hip ratios extracted from the FRISBEE cohort were 10.7 [95% CI: (5.6-20.5)], 6.4 [4.7-8.7], and 5.0 [3.9-6.5] for women of 60-69, 70-79, and 80-89 years old, respectively. These ratios were 1.7-1.8 times higher for all age groups than those from the Swedish data, which decreased from 6.5 (60-64 years group) down to 1.8 (85-89 age group). The overall MOFs/hip ratio in Frisbee was 6.0 [5.9-6.1], which was higher than any Swedish ratio between 65 and 85 years. Nevertheless, the decrease of the ratios with age paralleled that observed in Sweden. In this Brussels prospective cohort, MOFs/hip ratios were 1.7-1.8 times those observed in Sweden currently used for MOFs prediction in the Belgian FRAX® version. This discrepancy can greatly modify the estimation of the risk of MOFs, which is among the main criteria used to recommend a pharmacological treatment for osteoporosis in several countries.

Independent External Validation of FRAX and Garvan Fracture Risk Calculators: A Sub-Study of the FRISBEE Cohort

Probabilistic models including clinical risk factors with or without bone mineral density (BMD) have been developed to estimate the 5‐ or 10‐year absolute fracture risk. We investigated the performance of the FRAX and Garvan tools in a well‐characterized population‐based cohort of 3560 postmenopausal, volunteer women, aged 60 to 85 years at baseline, included in the Fracture Risk Brussels Epidemiological Enquiry (FRISBEE) cohort, during 5 years of follow‐up. Baseline data were used to calculate the estimated 10‐year risk of hip and major osteoporotic fractures (MOFs) for each participant using FRAX (Belgium). We computed the 5‐year risk according to the Garvan model with BMD. For calibration, the predicted risk of fracture was compared with fracture incidence across a large range of estimated fracture risks. The accuracy of the calculators to predict fractures was assessed using the area under the receiver operating characteristic curves (AUC). The FRAX tool was well calibrated for hip fractures (slope 1.09, p < 0.001; intercept −0.001, p = 0.46), but it consistently underestimated the incidence of major osteoporotic fractures (MOFs) (slope 2.12, p < 0.001; intercept −0.02, p = 0.06). The Garvan tool was well calibrated for “any Garvan” fractures (slope 1.05, p < 0.001; intercept 0.01, p = 0.37) but largely overestimated the observed hip fracture rate (slope 0.32, p < 0.001; intercept 0.006, p = 0.05). The predictive value for hip fractures was better for FRAX (AUC: 0.841, 95% confidence interval [CI] 0.795–0.887) than for Garvan (AUC: 0.769, 95% CI 0.702–0.836, p = 0.01). The Garvan AUC for “any Garvan” fractures was 0.721 (95% CI 0.693–0.749) and FRAX AUC for MOFs was 0.708 (95% CI 0.675–0.741). In conclusion, in our Belgian cohort, FRAX estimated quite well hip fractures but underestimated MOFs, while Garvan overestimated hip fracture risk but showed a good estimation of “any Garvan” fractures. Both models had a good discriminatory value for hip fractures but only a moderate discriminatory ability for MOFs or “any Garvan” fractures. © 2021 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.

Fragility fractures and prescriptions of medications for osteoporosis in patients with polymyalgia rheumatica: results from the PMR Cohort Study

Objectives

PMR is a common indication for long-term glucocorticoid treatment, leading to an increased risk of osteoporosis and fragility fractures. Guidelines recommend calcium and vitamin D for all patients, in addition to anti-resorptive agents for high-risk patients. The aim of this study was to investigate falls and fragility fracture history and the use of medications for osteoporosis in a PMR cohort.

Methods

Six hundred and fifty-two people with incident PMR responded to a postal survey. Self-reported data on falls, fragility fracture history and medication were collected at baseline. Follow-up data on fragility fractures (hip, wrist and spine) and falls were collected at 12 and 24 months. Logistic regression was used to assess the association between baseline characteristics and fractures.

Results

Fewer than 50% of respondents received osteoporosis treatments, including supplements. One hundred and twelve (17.2%) participants reported a fragility fracture at baseline, 72 participants reported a fracture at 12 months, and 62 reported a fracture at 24 months. Baseline history of falls was most strongly associated with fracture at 12 (odds ratio 2.35; 95% CI: 1.35, 4.12) and 24 months (1.91; 1.05, 3.49) when unadjusted for previous fractures.

Conclusion

Fracture reporting is common in people with PMR. To improve fracture prevention, falls assessment and interventions need to be considered. A history of falls could help to inform prescribing decisions around medications for osteoporosis. Future research should consider both pharmacological and non-pharmacological approaches to reducing fracture risk.

Risk factors for imminent fractures: a substudy of the FRISBEE cohort

Multiple factors increase the risk of an imminent fracture, including a recent fracture, older age, osteoporosis, comorbidities, and the fracture site. These findings could be a first step in the development of a model to predict an imminent fracture and select patients most at need of immediate treatment.

INTRODUCTION

The risk of a recurrent fragility fracture is maximal during the first 2 years following an incident fracture. In this prospective cohort study, we looked at the incidence of recurrent fractures within 2 years after a first incident fracture and we assessed independent clinical risk factors (CRFs) increasing this imminent fracture risk.

METHODS

A total of 3560 postmenopausal women recruited from 2007 to 2013 were surveyed yearly for the occurrence of fragility fractures. We identified patients who sustained a fracture during the first 2 years following a first incident fragility fracture. We quantified the risk of a new fracture and assessed independent CRFs, associated with an imminent fracture at various sites.

RESULTS

A recent fracture was a significant CRF for an imminent fracture (OR (95% CI): 3.7 (2.4-5.7) [p < 0.0001]). The incidence of an imminent fracture was higher in subjects above 80 years (p < 0.001). Other CRFs highly predictive in a multivariate analysis were osteoporosis diagnosis (p < 0.01), a central fracture as the index fracture (p < 0.01), and the presence of comorbidities (p < 0.05), with likelihood ratios of 1.9, 1.9, and 2.2, respectively. An imminent fracture was better predicted by a central fracture (p < 0.01) than by a major osteoporotic fracture. The hazard ratio was the highest for a central fracture.

CONCLUSION

In patients with a recent fracture, older age, osteoporosis, comorbidities, and fracture site were associated with an imminent fracture risk. These findings could be a first step in the development of a model to predict an imminent fracture and select patients most at need of immediate and most appropriate treatment.

Sleep disturbance and bone mineral density, risk of falls and fracture: Results from a 10.7-year prospective cohort study

Sleep problems are common in the general population and have been linked to bone health, falls risk and fracture. However, longitudinal studies on sleep-bone health outcomes are lacking and no study has investigated whether an increased risk of fracture is attributable to sleep-related low bone mineral density (BMD) and an increased risk of falls. This study was designed to examine the associations of sleep disturbance with bone mineral density (BMD), risk of falls and fractures over 10.7 years. The analyses were performed in a population-based cohort study with 1099 participants (mean age 62.9 years) enrolled at baseline, and 875, 768 and 563 participants traced at a mean follow-up of 2.6, 5.1 and 10.7 years, respectively. At each visit, self-reported sleep disturbance was recorded. BMD (by dual-energy x-ray absorptiometry), falls risk score and fracture were measured at each visit. The short-form Physiological Profile Assessment was used to measure falls risk score expressed as Z-score. Fractures were self-reported. Mixed-effects model and generalized estimating equations were used for the analyses. In multivariable analysis, there was a dose-response relationship between the extent of sleep disturbance and falls risk score with the strongest association in those reporting the worst sleep disturbance (β = 0.15/unit; 95%CI 0.02-0.28). The worst sleep disturbance was associated with an increased risk of any (relative risk [RR] 1.30/unit; 95%CI 1.01-1.67) and vertebral fracture (RR 2.41/unit; 95%CI 1.00-5.80) compared with those reporting no interrupted sleep. Women but not men with sleep disturbance had a higher risk of vertebral fracture (RR: 2.07 to 6.02, P < 0.05). These were independent of covariates, hip BMD and falls risk. There was no statistically significant association between sleep disturbance and BMD at the hip, spine or total body. Sleep disturbance was independently associated with a greater falls risk score and an increased risk of fractures. Further research is needed to confirm and identify underlying mechanisms for these associations.

The association of objectively ascertained sibling fracture history with major osteoporotic fractures: a population-based cohort study

We investigated the association of objectively ascertained sibling fracture history with major osteoporotic fracture (hip, forearm, humerus, or clinical spine) risk in a population-based cohort using administrative databases. Sibling fracture history is associated with increased major osteoporotic fracture risk, which has implications for fracture risk prediction.

INTRODUCTION

We aimed to determine whether objectively ascertained sibling fracture history is associated with major osteoporotic fracture (MOF; hip, forearm, humerus, or clinical spine) risk.

METHODS

This retrospective cohort study used administrative databases from the province of Manitoba, Canada, which has a universal healthcare system. The cohort included men and women 40+ years between 1997 and 2015 with linkage to at least one sibling. The exposure was sibling MOF diagnosis occurring after age 40 years and prior to the outcome. The outcome was incident MOF identified in hospital and physician records using established case definitions. A multivariable Cox proportional hazards regression model was used to estimate the risk of MOF after adjustment for known fracture risk factors.

RESULTS

The cohort included 217,527 individuals; 91.9% were linked to full siblings (siblings having the same father and mother) and 49.0% were females. By the end of the study period, 6255 (2.9%) of the siblings had a MOF. During a median follow-up of 11 years (IQR 5-15), 5235 (2.4%) incident MOF were identified in the study cohort, including 234 hip fractures. Sibling MOF history was associated with an increased risk of MOF (hazard ratio [HR] 1.67, 95% confidence interval [CI] 1.44-1.92). The risk was elevated in both men (HR 1.57, 95% CI 1.24-1.98) and women (HR 1.74, 95% CI 1.45-2.08). The highest risk was associated with a sibling diagnosis of forearm fracture (HR 1.81, 95% CI 1.53-2.15).

CONCLUSION

Sibling fracture history is associated with increased MOF risk and should be considered as a candidate risk factor for improving fracture risk prediction.

Milk Consumption for the Prevention of Fragility Fractures

Results indicating that a high milk intake is associated with both higher and lower risks of fragility fractures, or that indicate no association, can all be presented in the same meta-analysis, depending on how it is performed. In this narrative review, we discuss the available studies examining milk intake in relation to fragility fractures, highlight potential problems with meta-analyses of such studies, and discuss potential mechanisms and biases underlying the different results. We conclude that studies examining milk and dairy intakes in relation to fragility fracture risk need to study the different milk products separately. Meta-analyses should consider the doses in the individual studies. Additional studies in populations with a large range of intake of fermented milk are warranted.

Underevaluation of fractures by self-report: an analysis from the FRISBEE cohort

We assessed the rate of non-reported fractures in the FRISBEE cohort. Over a median follow-up period of 9.2 years, we registered 992 fractures. The global percentage of non-reported fractures was 21.3%. Underreporting of fracture event might influence any model of fracture risk prediction.

INTRODUCTION

Most fracture cohort studies rely on participant self-report of fracture event. This approach may lead to fracture underreporting. The purpose of the study was to assess the rate of non-reported fractures in a well-characterized population-based cohort of 3560 postmenopausal women, aged 60-85 years, included in the Fracture Risk Brussels Epidemiological Enquiry (FRISBEE) study.

METHODS

Incident low-traumatic or non-traumatic fractures were registered annually during phone calls. In 2018, we reviewed the medical files of 67.9% of our study participants and identified non-reported fractures ("false negatives fractures (FN)"). We also evaluated whether the rate of FN was influenced by baseline patients' characteristics and fracture risk factors. Generalized estimating equation (GEE) was used to calculate odds ratio (OR) and 95% CI.

RESULTS

Over a median follow-up period of 9.2 years, we registered 992 fractures (781 by self-report, confirmed by a radiological report and 211 unreported). The global false negative rate for all fractures was 21.3%, including 22% for MOFs (major osteoporotic fractures), 13.1% for other major fractures, and 25.8% for minor fractures. The rate of non-reported fractures varied by fracture site: for MOFs, it was 2.7% (n = 2/73) at the hip, 5.3% at the proximal humerus (n = 5/94), 7.1% at the wrist (n = 11/154), and 46.5% at the spine (n = 100/215). For "other major" fractures, the highest rate of false negatives fractures was found at the pelvic bone (21%, n = 13/62), followed by the elbow (17.9%, n = 5/28), long bones (10.5%, n = 2/19), ankle (6.2%, n = 4/65), and knee (5.9%, n = 1/17). Older subjects (OR 1.7; 95% CI, 1.2-2.4; P = 0.003), subjects with early non-substituted menopause (OR 1.8; 95% CI, 1.0-3.3; P = 0.04), with a lower education level (OR 1.5; 95%CI, 1.1-2.2; P = 0.01), and those under drug therapy for osteoporosis (OR 1.5; 95% CI, 1.0-2.2; P = 0.05) were associated with a higher rate of FN.

CONCLUSIONS

In conclusion, underreporting of a substantial proportion of fracture events will influence any model of fracture risk prediction and induce bias when estimating the associations between candidate risk factors and incident fractures.