Prediction of an Imminent Fracture After an Index Fracture - Models Derived From the Frisbee Cohort.

Patients who sustain a fracture are at greatest risk of recurrent fracture during the next 2 years. We propose three models to identify subjects most at risk of an imminent fracture, according to fracture site (any fracture, major osteoporotic fracture [MOF] or central). They were constructed using data of the prospective Frisbee cohort, which includes 3560 postmenopausal women aged 60 to 85 years who were followed for at least 5 years. A total of 881 subjects had a first incident validated fragility fracture before December 2018. Among these, we validated 130 imminent fractures occurring within the next 2 years; 79 were MOFs, and 88 were central fractures. Clinical risk factors were re-evaluated at the time of the index fracture. Fine and Gray proportional hazard models were derived separately for each group of fractures. The following risk factors were significantly associated with the risk of any imminent fracture: total hip bone mineral density (BMD) (p < 0.001), a fall history (p < 0.001), and comorbidities (p = 0.03). Age (p = 0.05 and p = 0.03, respectively) and a central fracture as the index fracture (p = 0.04 and p = 0.005, respectively) were additional predictors of MOFs and central fractures. The three prediction models are presented as nomograms. The calibration curves and the Brier scores based on bootstrap resampling showed calibration scores of 0.089 for MOF, 0.094 for central fractures, and 0.132 for any fractures. The predictive accuracy of the models expressed as area under the receiver operating characteristic (AUROC) curve (AUC) were 0.74 for central fractures, 0.72 for MOFs, and 0.66 for all fractures, respectively. These AUCs compare well with those of FRAX and Garvan to predict the 5- or 10-year fracture probability. In summary, five predictors (BMD, age, comorbidities, falls, and central fracture as the incident fracture) allow the calculation with a reasonable accuracy of the imminent risk of fracture at different sites (MOF, central fracture, and any fracture) after a recent sentinel fracture. © 2021 American Society for Bone and Mineral Research (ASBMR).

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.

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.