ArticleOsteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA2023
Development and validation of common data model-based fracture prediction model using machine learning algorithm.
Article in Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 5 citations in OpenAlex.
- Risk prediction models for adverse drug reactions in pediatrics: a scoping review.International journal of clinical pharmacy · 2026Article
- A Simple Osteoporosis Clinical Risk Model Based on the National Health and Nutrition Examination Survey.Indian journal of orthopaedics · 2025Article
- Developing and validating a nomogram prediction model for osteoporosis risk in the UK biobank: a national prospective cohort.BMC public health · 2025Article
- Application of artificial intelligence in osteoporosis: a review.Frontiers in medicine · 2025Review
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6 authors at 2 institutions in 2 countries.
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Abstract
The need for an accurate country-specific real-world-based fracture prediction model is increasing. Thus, we developed scoring systems for osteoporotic fractures from hospital-based cohorts and validated them in an independent cohort in Korea. The model includes history of fracture, age, lumbar spine and total hip T-score, and cardiovascular disease. PURPOSE: Osteoporotic fractures are substantial health and economic burden. Therefore, the need for an accurate real-world-based fracture prediction model is increasing. We aimed to develop and validate an accurate and user-friendly model to predict major osteoporotic and hip fractures using a common data model database.
methodsThe study included 20,107 and 13,353 participants aged ≥ 50 years with data on bone mineral density using dual-energy X-ray absorptiometry from the CDM database between 2008 and 2011 from the discovery and validation cohort, respectively. The main outcomes were major osteoporotic and hip fracture events. DeepHit and Cox proportional hazard models were used to identify predictors of fractures and to build scoring systems, respectively.
resultsThe mean age was 64.5 years, and 84.3% were women. During a mean of 7.6 years of follow-up, 1990 major osteoporotic and 309 hip fracture events were observed. In the final scoring model, history of fracture, age, lumbar spine T-score, total hip T-score, and cardiovascular disease were selected as predictors for major osteoporotic fractures. For hip fractures, history of fracture, age, total hip T-score, cerebrovascular disease, and diabetes mellitus were selected. Harrell's C-index for osteoporotic and hip fractures were 0.789 and 0.860 in the discovery cohort and 0.762 and 0.773 in the validation cohort, respectively. The estimated 10-year risks of major osteoporotic and hip fractures were 2.0%, 0.2% at score 0 and 68.8%, 18.8% at their maximum scores, respectively.
conclusionWe developed scoring systems for osteoporotic fractures from hospital-based cohorts and validated them in an independent cohort. These simple scoring models may help predict fracture risks in real-world practice.
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