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Osteoporos Int. 2023;34(8):1437-1451.

Development and validation of common data model-based fracture prediction model using machine learning algorithm.

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제1저자 공성혜, 김시현 · 교신저자 김정희, 김광수

PMID 37195320 DOI 10.1007/s00198-023-06787-7 IF 5.4 내분비대사내과
소속 (8)
  1. Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea.
  2. Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea.
  3. Department of Integrative Medicine, Seoul National University Hospital, Seoul, 03080, Korea.
  4. Interdisciplinary Program in Bioengineering, Seoul National University Graduate School, Seoul, Korea.
  5. Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea. jhee1@snu.ac.kr.
  6. Department of Internal Medicine, Seoul National University Hospital, Seoul, 03080, Korea. jhee1@snu.ac.kr.
  7. Department of Integrative Medicine, Seoul National University Hospital, Seoul, 03080, Korea. kksoo716@gmail.com.
  8. Department of Internal Medicine, Seoul National University Hospital, Seoul, 03080, Korea.

Abstract

UNLABELLED 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.

METHODS The 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.

RESULTS The 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.

CONCLUSION We 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.

© 2023. International Osteoporosis Foundation and Bone Health and Osteoporosis Foundation.

Keywords

Common data modelFractureOsteoporosisPrediction model

그 밖의 정보
출판 형태
Journal Article
저널
Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · eISSN 1433-2965
전자 출판
2023-05-17
MeSH
Humans · Female · Middle Aged · Male · Osteoporotic Fractures* / epidemiology / etiology · Cardiovascular Diseases* · Bone Density · Hip Fractures* / epidemiology / etiology · Absorptiometry, Photon · Algorithms · Risk Factors · Risk Assessment
참고문헌
48편