Fracture prediction in a Swiss cohort.

Fracture prediction is essential in managing patients with osteoporosis, and is an integral component of many fracture prevention guidelines. We aimed to identify the most relevant clinical fracture risk factors in contemporary populations by training and validating short- and long-term fracture risk prediction models in two cohorts. We used traditional and machine learning survival models to predict risks of vertebral, hip and any fractures on the basis of clinical risk factors, T-scores and treatment history among participants in a nationwide Swiss osteoporosis registry (N = 5944 postmenopausal women, median follow-up of 4.1 years between January 2015 and October 2022; a total of 1190 fractures during follow-up). The independent validation cohort comprised 5474 postmenopausal women from the UK Biobank with 290 incident fractures during follow-up. Uno's C-index and the time-dependent area under the receiver operating characteristics curve were calculated to evaluate the performance of different machine learning models (Random survival forests and eXtreme Gradient Boosting). In the independent validation set, the C-index was 0.74 [0.58, 0.86] for vertebral fractures, 0.83 [0.7, 0.94] for hip fractures and 0.63 [0.58, 0.69] for any fractures at year 2, and these values further increased for longer estimations of up to 7 years. In comparison, the 10-year fracture probability calculated with FRAX® Switzerland was 0.60 [0.55, 0.64] for major osteoporotic fractures and 0.62 [0.49, 0.74] for hip fractures. The most important variables identified with Shapley additive explanations (SHAP) values were age, T-scores and prior fractures, while number of falls was an important predictor of hip fractures. Performances of both traditional and machine learning models showed similar C-indices. We conclude that fracture risk can be improved by including the lumbar spine T-score, trabecular bone score, numbers of falls and recent fractures, and treatment information has a significant impact on fracture prediction.

Fracture prediction is essential in managing patients with osteoporosis. We developed and validated traditional and machine learning models to predict short- and long-term fracture risk and identify the most relevant clinical fracture risk factors for vertebral, hip, and any fractures in contemporary populations. We used data from 5944 postmenopausal women in a Swiss osteoporosis registry and validated our findings with 5474 women from the UK Biobank. Our machine learning models performed well, with C-index values of 0.74 [0.58, 0.86] for vertebral fractures, 0.83 [0.7, 0.94] for hip fractures and 0.63 [0.58, 0.69] for any fractures at year 2, and these values further increased for longer estimations of up to 7 years. In contrast, FRAX® Switzerland had lower C-index values (0.60 [0.55, 0.64] for major fractures and 0.62 [0.49, 0.74] for hip fracture probabilities over 10 years). Key predictors identified included age, T-scores, prior fractures, and number of falls. We conclude that incorporating a broader range of clinical factors, as well as lumbar spine T-scores, fall history, recent fractures, and treatment information, can improve fracture risk assessments in osteoporosis management. Both traditional and machine learning models showed similar effectiveness in predicting fractures.

Undergraduate Minor in Health Disparities in Society: a Magnet for Under-represented Pre-professional Students.

Increasing the diversity of tomorrow's healthcare work force remains a challenge despite many thoughtful published reports and recommendations. As part of an effort to grow a more diverse pre-professional health population, we created an undergraduate minor, Health Disparities in Society, at the University of Florida. Most courses for the minor were identified from existing offerings, and we created only two new courses, an introduction course and a capstone service-learning course. The new minor quickly became the most popular in the College of Liberal Arts and Sciences (which has approximately 12,000 total undergraduate students), and importantly, students selecting the minor were more likely to be under-represented minorities than would be expected given undergraduate demographics. Pre-professional students choosing this minor reflect the desired diversity of the healthcare workforce of tomorrow.

A genome-wide scan for juvenile rheumatoid arthritis in affected sibpair families provides evidence of linkage.

OBJECTIVE: Juvenile rheumatoid arthritis (JRA) represents a heterogeneous group of disorders with a complex genetic component. A genome scan was performed to detect linkage to JRA in 121 families containing 247 affected children in North America (the JRA Affected Sibpair [ASP] Registry). METHODS: Genotype data collected for HLA-DR and 386 microsatellite markers were subjected to multipoint nonparametric linkage analysis. Following analysis of the entire set of families, additional analyses were performed after a priori stratification by disease onset type, age at onset, disease course, and selected HLA-DRB1 alleles. RESULTS: Linkage of JRA to the HLA region was confirmed (logarithm of odds [LOD] score 2.26). Additional evidence supporting linkage of JRA was observed at 1p36 (D1S214; LOD 1.65), 19p13 (D19S216; LOD 1.72), and 20q13 (D20S100; LOD 1.75). For early-onset polyarticular disease, evidence of linkage was found at chromosome 7q11 (D7S502; LOD 3.47). For pauciarticular disease, evidence supporting linkage was observed on chromosome 19p13 (D19S216; LOD 2.98), the same marker that supported linkage to the "JRA" phenotype. Other regions supporting linkage with JRA disease subtype included 20q13, 4q24, 12q24, and Xp11. Stratification of families based on the presence of the HLA-DR8 allele in affected siblings resulted in significant linkage observed at 2p25 (D2S162/D2S305; LOD 6.0). CONCLUSION: These data support the hypothesis that multiple genes, including at least 1 in the HLA region, influence susceptibility to JRA. These findings for JRA are consistent with findings for other autoimmune diseases and support the notion that common genetic regions contribute to an autoimmune phenotype.