A machine learning tool for predicting newly diagnosed osteoporosis in primary healthcare in the Stockholm Region
Singer, A. J., Sharma, A., Deignan, C. & Borgermans, L. Closing the gap in osteoporosis management: the critical role of primary care in bone health. Curr. Med. Res. Opin. 39, 387–398 (2023).
Google Scholar
Recker, R. R. & Deng, H. W. Role of genetics in osteoporosis. Endocrine 17, 55–66 (2002).
Google Scholar
Morris, J. A. et al. An atlas of genetic influences on osteoporosis in humans and mice. Nat. Genet. 51, 258–266 (2019).
Google Scholar
Muraki, S. et al. Diet and lifestyle associated with increased bone mineral density: Cross-sectional study of Japanese elderly women at an osteoporosis outpatient clinic. J. Orthop. Sci. 12, 317–320 (2007).
Google Scholar
Kanis, J,A., Cooper, C., Rizzoli, R., Reginster, J.Y., Scientific Advisory Board of the European Society for C, Economic Aspects of O, et al. European guidance for the diagnosis and management of osteoporosis in postmenopausal women. Osteoporos. Int. 30, 3–44 (2019).
Abtahi, S. et al. Secular trends in major osteoporotic fractures among 50+ adults in Denmark between 1995 and 2010. Osteoporos. Int. 30, 2217–2223 (2019).
Google Scholar
Warriner, A. H. et al. Which fractures are most attributable to osteoporosis?. J. Clin. Epidemiol. 64, 46–53 (2011).
Google Scholar
Mitchell, P. J., Chan, D. D., Lee, J. K., Tabu, I. & Alpuerto, B. B. The global burden of fragility fractures – what are the differences, and where are the gaps. Best Pract Res Clin Rheumatol. 36, 101777 (2022).
Google Scholar
Johnell, O. & Kanis, J. A. An estimate of the worldwide prevalence and disability associated with osteoporotic fractures. Osteoporos. Int. 17, 1726–1733 (2006).
Google Scholar
Hernlund, E. et al. Osteoporosis in the European union: medical management, epidemiology and economic burden: A report prepared in collaboration with the international osteoporosis foundation (IOF) and the European federation of pharmaceutical industry associations (EFPIA). Arch. Osteoporos. 8, 136 (2013).
Google Scholar
Cauley, J. A., Chalhoub, D., Kassem, A. M. & Fuleihan, G. H. Geographic and ethnic disparities in osteoporotic fractures. Nat. Rev. Endocrinol. 10, 338–351 (2014).
Google Scholar
Wang, X. F. & Seeman, E. Epidemiology and structural basis of racial differences in fragility fractures in Chinese and Caucasians. Osteoporos. Int. 23, 411–422 (2012).
Google Scholar
Lofthus, C. M. et al. Epidemiology of distal forearm fractures in Oslo, Norway. Osteoporos. Int. 19, 781–786 (2008).
Google Scholar
Shim, J. G. et al. Application of machine learning approaches for osteoporosis risk prediction in postmenopausal women. Arch. Osteoporos. 15, 169 (2020).
Google Scholar
Jakab, M., Farrington, J., Borgermans, L. & Mantingh, F. Health systems respond to noncommunicable diseases: time for ambition. Copenhagen, Denmark: WHO regional office for Europe. Public Health Panor. 4, 507–514 (2018).
Fuggle, N. R. et al. The treatment gap: The missed opportunities for osteoporosis therapy. Bone 144, 115833 (2021).
Google Scholar
Malaise, O. et al. High detection rate of osteoporosis with screening of a general hospitalized population: A 6-year study in 6406 patients in a university hospital setting. BMC Musculoskelet. Disord. 21, 90 (2020).
Google Scholar
Carey, J. J., Chih-Hsing, Wu. P. & Bergin, D. Risk assessment tools for osteoporosis and fractures in 2022. Best Pract. Res. Clin. Rheumatol. 36, 101775 (2022).
Google Scholar
Ballane, G., Cauley, J. A., Luckey, M. M. & El-Hajj, F. G. Worldwide prevalence and incidence of osteoporotic vertebral fractures. Osteoporos. Int. 28, 1531–1542 (2017).
Google Scholar
Bow, C. H. et al. Ethnic difference of clinical vertebral fracture risk. Osteoporos. Int. 23, 879–885 (2012).
Google Scholar
Johnell, O. & Kanis, J. A. An estimate of the worldwide prevalence, mortality and disability associated with hip fracture. Osteoporos. Int. 15, 897–902 (2004).
Google Scholar
Ghannam, S., Blaney, H., Gelfond, J. & Bruder, J. M. The use of FRAX in identifying women less than 65 years needing bone mineral density testing. J. Clin. Densitom. 24, 36–43 (2021).
Google Scholar
Tomasiuk, J. M., Nowakowska-Plaza, A., Wislowska, M. & Gluszko, P. Osteoporosis and diabetes—Possible links and diagnostic difficulties. Reumatologia 61, 294–304 (2023).
Google Scholar
Petersen, T. G. et al. Ten-year follow-up of fracture risk in a systematic population-based screening program: The risk-stratified osteoporosis strategy evaluation (ROSE) randomised trial. EClinicalMedicine 71, 102584 (2024).
Google Scholar
Shevroja, E. et al. Update on the clinical use of trabecular bone score (TBS) in the management of osteoporosis: Results of an expert group meeting organized by the European society for clinical and economic aspects of osteoporosis, osteoarthritis and musculoskeletal diseases (ESCEO), and the international osteoporosis foundation (IOF) under the auspices of WHO collaborating center for epidemiology of musculoskeletal health and aging. Osteoporos. Int. 34, 1501–1529 (2023).
Google Scholar
Park, S. H. Tools for assessing fall risk in the elderly: a systematic review and meta-analysis. Aging Clin. Exp. Res. 30, 1–16 (2018).
Google Scholar
Nilsson, M. et al. Fall risk assessment predicts fall-related injury, hip fracture, and head injury in older adults. J. Am. Geriatr. Soc. 64, 2242–2250 (2016).
Google Scholar
Kulbay, A., Vest, D., Thorngren, K.G., Hommel, A. & Hedstrom, M. Osteoporotic fractures—Still a challenge, the Swedish National Hip Fracture Registry shows undertreatment of osteoporosis after hip fractures. Lakartidningen.118 (2021).
Smets, J., Shevroja, E., Hugle, T., Leslie, W. D. & Hans, D. Machine learning solutions for osteoporosis—A Review. J. Bone Miner. Res. 36, 833–851 (2021).
Google Scholar
Yoo, T. K. et al. Osteoporosis risk prediction for bone mineral density assessment of postmenopausal women using machine learning. Yonsei Med. J. 54, 1321–1330 (2013).
Google Scholar
Suh, B. et al. Interpretable deep-learning approaches for osteoporosis risk screening and individualized feature analysis using large population-based data: Model development and performance evaluation. J. Med. Internet Res. 25, e40179 (2023).
Google Scholar
Lee, C., Joo, G., Shin, S., Im, H. & Moon, K. W. Prediction of osteoporosis in patients with rheumatoid arthritis using machine learning. Sci. Rep. 13, 21800 (2023).
Google Scholar
Wu, X. et al. Development of machine learning models for predicting osteoporosis in patients with type 2 diabetes mellitus—A preliminary study. Diabetes Metab. Syndr. Obes. 16, 1987–2003 (2023).
Google Scholar
Wu, X. et al. Application of machine learning algorithms to predict osteoporosis in postmenopausal women with type 2 diabetes mellitus. J. Endocrinol. Invest. 46, 2535–2546 (2023).
Google Scholar
Friedman, J. H. Stochastic gradient boosting. Comput. Stat. Data Anal. 38, 367–378 (2002).
Google Scholar
Carlsson, A. C. et al. High prevalence of diagnosis of diabetes, depression, anxiety, hypertension, asthma and COPD in the total population of Stockholm, Sweden—A challenge for public health. BMC Public Health 13, 670 (2013).
Google Scholar
Friedman, J. H. Greedy function approximation: A gradient boosting machine. Ann. Stat. 29(5), 1189–1232 (2001).
Google Scholar
Fregoso-Aparicio, L., Noguez, J., Montesinos, L. & Garcia-Garcia, J. A. Machine learning and deep learning predictive models for type 2 diabetes: A systematic review. Diabetol. Metab. Syndr. 13, 148 (2021).
Google Scholar
Wandell, P. et al. A machine learning tool for identifying patients with newly diagnosed diabetes in primary care. Prim. Care Diabetes 18 (5), 501–505 (2024).
Google Scholar
Wandell, P. et al. Most common diseases diagnosed in primary care in Stockholm, Sweden, in 2011. Fam. Pract. 30, 506–513 (2013).
Google Scholar
Team RC. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. (2016).
Gupta, A. et al. Digital health interventions for osteoporosis and post-fragility fracture care. Ther. Adv. Musculoskelet. Dis. 14, 1759720X221083523 (2022).
Google Scholar
Cruz, A. S., Lins, H. C., Medeiros, R. V. A., Filho, J. M. F. & da Silva, S. G. Artificial intelligence on the identification of risk groups for osteoporosis, a general review. Biomed. Eng. Online 17, 12 (2018).
Google Scholar
Ferizi, U., Honig, S. & Chang, G. Artificial intelligence, osteoporosis and fragility fractures. Curr. Opin. Rheumatol. 31, 368–375 (2019).
Google Scholar
Rahim, F. et al. Machine learning algorithms for diagnosis of hip bone osteoporosis: A systematic review and meta-analysis study. Biomed. Eng. Online. 22, 68 (2023).
Google Scholar
McCarthy, J. & Davis, A. Diagnosis and management of vertebral compression fractures. Am. Fam. Physician 94, 44–50 (2016).
Google Scholar
Kanis, J. A. et al. Long-term risk of osteoporotic fracture in Malmo. Osteoporos. Int. 11, 669–674 (2000).
Google Scholar
Johansson, L. et al. The prevalence of vertebral fractures is associated with reduced hip bone density and inferior peripheral appendicular volumetric bone density and structure in older women. J. Bone Miner. Res. 33, 250–260 (2018).
Google Scholar
Rubino, F. J. et al. Active identification of vertebral fracture in the FLS model of care. Arch. Osteoporos. 18, 89 (2023).
Google Scholar
Zeytinoglu, M., Jain, R. K. & Vokes, T. J. Vertebral fracture assessment: Enhancing the diagnosis, prevention, and treatment of osteoporosis. Bone 104, 54–65 (2017).
Google Scholar
Glaser, D. L. & Kaplan, F. S. Osteoporosis. Definition and clinical presentation. Spine Phila Pa 1976 22, 12S-S16 (1997).
Google Scholar
Alswat, K. A. Gender disparities in osteoporosis. J. Clin. Med. Res. 9, 382–387 (2017).
Google Scholar
Terakado, A. et al. A clinical prospective observational cohort study on the prevalence and primary diagnostic accuracy of occult vertebral fractures in aged women with acute lower back pain using magnetic resonance imaging. Pain Res. Manag. 2017, 9265259 (2017).
Google Scholar
Johansson, L., Sundh, D., Nilsson, M., Mellstrom, D. & Lorentzon, M. Vertebral fractures and their association with health-related quality of life, back pain and physical function in older women. Osteoporos. Int. 29, 89–99 (2018).
Google Scholar
Johansson, L. et al. Improved fracture risk prediction by adding VFA-identified vertebral fracture data to BMD by DXA and clinical risk factors used in FRAX. Osteoporos. Int. 33, 1725–1738 (2022).
Google Scholar
Salari, N. et al. The global prevalence of osteoporosis in the world: A comprehensive systematic review and meta-analysis. J. Orthop. Surg. Res. 16, 609 (2021).
Google Scholar
Delmas, P. D. et al. Underdiagnosis of vertebral fractures is a worldwide problem: The IMPACT study. J. Bone Miner. Res. 20, 557–563 (2005).
Google Scholar
link
