FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4383483417> ?p ?o ?g. }

• W4383483417 endingPage "1267" @default.
• W4383483417 startingPage "1258" @default.
• W4383483417 abstract "Bone strength is an important contributor to fracture risk. Areal bone mineral density (aBMD) derived from dual-energy X-ray absorptiometry (DXA) is used as a surrogate for bone strength in fracture risk prediction tools. 3D finite element (FE) models predict bone strength better than aBMD, but their clinical use is limited by the need for 3D computed tomography and lack of automation. We have earlier developed a method to reconstruct the 3D hip anatomy from a 2D DXA image, followed by subject-specific FE-based prediction of proximal femoral strength. In the current study, we aim to evaluate the method's ability to predict incident hip fractures in a population-based cohort (Osteoporotic Fractures in Men [MrOS] Sweden). We defined two subcohorts: (i) hip fracture cases and controls cohort: 120 men with a hip fracture (<10 years from baseline) and two controls to each hip fracture case, matched by age, height, and body mass index; and (ii) fallers cohort: 86 men who had fallen the year before their hip DXA scan was acquired, 15 of which sustained a hip fracture during the following 10 years. For each participant, we reconstructed the 3D hip anatomy and predicted proximal femoral strength in 10 sideways fall configurations using FE analysis. The FE-predicted proximal femoral strength was a better predictor of incident hip fractures than aBMD for both hip fracture cases and controls (difference in area under the receiver operating characteristics curve, ΔAUROC = 0.06) and fallers (ΔAUROC = 0.22) cohorts. This is the first time that FE models outperformed aBMD in predicting incident hip fractures in a population-based prospectively followed cohort based on 3D FE models obtained from a 2D DXA scan. Our approach has potential to notably improve the accuracy of fracture risk predictions in a clinically feasible manner (only one single DXA image is needed) and without additional costs compared to the current clinical approach. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR)." @default.
• W4383483417 created "2023-07-08" @default.
• W4383483417 creator A5062528819 @default.
• W4383483417 creator A5064365884 @default.
• W4383483417 creator A5070203472 @default.
• W4383483417 creator A5074735538 @default.
• W4383483417 creator A5075514882 @default.
• W4383483417 creator A5082508582 @default.
• W4383483417 creator A5086367634 @default.
• W4383483417 date "2023-08-07" @default.
• W4383483417 modified "2023-10-17" @default.
• W4383483417 title "<scp>3D</scp> Finite Element Models Reconstructed From <scp>2D</scp> Dual‐Energy X‐Ray Absorptiometry (<scp>DXA)</scp> Images Improve Hip Fracture Prediction Compared to Areal <scp>BMD</scp> in Osteoporotic Fractures in Men (MrOS) Sweden Cohort" @default.
• W4383483417 cites W1751168574 @default.
• W4383483417 cites W1904766911 @default.
• W4383483417 cites W1968092726 @default.
• W4383483417 cites W1976384386 @default.
• W4383483417 cites W1994486302 @default.
• W4383483417 cites W2001369830 @default.
• W4383483417 cites W2003170314 @default.
• W4383483417 cites W2005918389 @default.
• W4383483417 cites W2009508470 @default.
• W4383483417 cites W2012596468 @default.
• W4383483417 cites W2015052481 @default.
• W4383483417 cites W2015313580 @default.
• W4383483417 cites W2032189326 @default.
• W4383483417 cites W2032618685 @default.
• W4383483417 cites W2033171885 @default.
• W4383483417 cites W2044064269 @default.
• W4383483417 cites W2046167623 @default.
• W4383483417 cites W2047513757 @default.
• W4383483417 cites W2064314501 @default.
• W4383483417 cites W2068947159 @default.
• W4383483417 cites W2079459163 @default.
• W4383483417 cites W2106105679 @default.
• W4383483417 cites W2107177524 @default.
• W4383483417 cites W2110396808 @default.
• W4383483417 cites W2112081648 @default.
• W4383483417 cites W2121337876 @default.
• W4383483417 cites W2142307201 @default.
• W4383483417 cites W2145069783 @default.
• W4383483417 cites W2161555525 @default.
• W4383483417 cites W2161748213 @default.
• W4383483417 cites W2162419940 @default.
• W4383483417 cites W2164461503 @default.
• W4383483417 cites W2328176404 @default.
• W4383483417 cites W2416259076 @default.
• W4383483417 cites W2506683073 @default.
• W4383483417 cites W2565466202 @default.
• W4383483417 cites W2591678938 @default.
• W4383483417 cites W2594765539 @default.
• W4383483417 cites W2625355327 @default.
• W4383483417 cites W2762044025 @default.
• W4383483417 cites W2797796709 @default.
• W4383483417 cites W2805471295 @default.
• W4383483417 cites W2890865904 @default.
• W4383483417 cites W2896172166 @default.
• W4383483417 cites W2907875064 @default.
• W4383483417 cites W2912085974 @default.
• W4383483417 cites W2948129242 @default.
• W4383483417 cites W2954640276 @default.
• W4383483417 cites W2970515075 @default.
• W4383483417 cites W2980456048 @default.
• W4383483417 cites W3015380527 @default.
• W4383483417 cites W3019986263 @default.
• W4383483417 cites W3024322070 @default.
• W4383483417 cites W3091135993 @default.
• W4383483417 cites W3109228130 @default.
• W4383483417 cites W3134744038 @default.
• W4383483417 cites W3145508644 @default.
• W4383483417 cites W3153390140 @default.
• W4383483417 cites W3204241606 @default.
• W4383483417 cites W3214130138 @default.
• W4383483417 cites W4200083170 @default.
• W4383483417 cites W4239851848 @default.
• W4383483417 cites W4294119554 @default.
• W4383483417 doi "https://doi.org/10.1002/jbmr.4878" @default.
• W4383483417 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37417707" @default.
• W4383483417 hasPublicationYear "2023" @default.
• W4383483417 type Work @default.
• W4383483417 citedByCount "0" @default.
• W4383483417 crossrefType "journal-article" @default.
• W4383483417 hasAuthorship W4383483417A5062528819 @default.
• W4383483417 hasAuthorship W4383483417A5064365884 @default.
• W4383483417 hasAuthorship W4383483417A5070203472 @default.
• W4383483417 hasAuthorship W4383483417A5074735538 @default.
• W4383483417 hasAuthorship W4383483417A5075514882 @default.
• W4383483417 hasAuthorship W4383483417A5082508582 @default.
• W4383483417 hasAuthorship W4383483417A5086367634 @default.
• W4383483417 hasBestOaLocation W43834834171 @default.
• W4383483417 hasConcept C126322002 @default.
• W4383483417 hasConcept C2775854910 @default.
• W4383483417 hasConcept C2776541429 @default.
• W4383483417 hasConcept C2776886416 @default.
• W4383483417 hasConcept C2777425516 @default.
• W4383483417 hasConcept C2778885795 @default.
• W4383483417 hasConcept C2779329777 @default.
• W4383483417 hasConcept C2779447285 @default.
• W4383483417 hasConcept C2781440623 @default.

• next