The Irish dual-energy X-ray absorptiometry (DXA) Health Informatics Prediction (HIP) for Osteoporosis Project. Issue 12 (18th December 2020)

• Record Type: Journal Article
• Authors: E, Erjiang; Wang, Tingyan; Yang, Lan; Dempsey, Mary; Brennan, Attracta; Yu, Ming; Chan, Wing P.; Whelan, Bryan; Silke, Carmel; O'Sullivan, Miriam; Rooney, Bridie; McPartland, Aoife; O'Malley, Gráinne; Carey, John J.
• Is Part Of: BMJ open. Volume 10:Issue 12(2020)
• Publication Date: 2020-12-18
• Subjects: rheumatology -- health informatics -- risk management -- bone diseases; Medicine -- Research -- Periodicals; 610.72
• DOI: 10.1136/bmjopen-2020-040488
• Languages: English
• ISSNs: 2044-6055

Abstract

Abstract : Purpose: The purpose of the Irish dual-energy X-ray absorptiometry (DXA) Health Informatics Prediction (HIP) for Osteoporosis Project is to create a large retrospective cohort of adults in Ireland to examine the validity of DXA diagnostic classification, risk assessment tools and management strategies for osteoporosis and osteoporotic fractures for our population. Participants: The cohort includes 36 590 men and women aged 4–104 years who had a DXA scan between January 2000 and November 2018 at one of 3 centres in the West of Ireland. Findings to date: 36 590 patients had at least 1 DXA scan, 6868 (18.77%) had 2 scans and 3823 (10.45%) had 3 or more scans. There are 364 unique medical disorders, 186 unique medications and 46 DXA variables identified and available for analysis. The cohort includes 10 349 (28.3%) individuals who underwent a screening DXA scan without a clear fracture risk factor (other than age), and 9947 (27.2%) with prevalent fractures at 1 of 44 skeletal sites. Future plans: The Irish DXA HIP Project plans to assess current diagnostic classification and risk prediction algorithms for osteoporosis and fractures, identify the risk predictors for osteoporosis and develop novel, accurate and personalised risk prediction tools, by using the large multicentre longitudinal follow-up cohort. Furthermore, the dataset may be used to assess, and possibly support, multimorbidity management due to the large number of variables collected in this project.