Active Shape Modeling of the Hip in the Prediction of Incident Hip Fracture

Overview

Journal J Bone Miner Res

Publisher Oxford University Press

Date 2010 Sep 30

PMID 20878772

Citations 22

Authors

Julie C Baker-Lepain

Kali R Luker

John A Lynch

Neeta Parimi

Michael C Nevitt

Nancy E Lane

Affiliations

Soon will be listed here.

Abstract

The objective of this study was to evaluate right proximal femur shape as a risk factor for incident hip fracture using active shape modeling (ASM). A nested case-control study of white women 65 years of age and older enrolled in the Study of Osteoporotic Fractures (SOF) was performed. Subjects (n = 168) were randomly selected from study participants who experienced hip fracture during the follow-up period (mean 8.3 years). Controls (n = 231) had no fracture during follow-up. Subjects with baseline radiographic hip osteoarthritis were excluded. ASM of digitized right hip radiographs generated 10 independent modes of variation in proximal femur shape that together accounted for 95% of the variance in proximal femur shape. The association of ASM modes with incident hip fracture was analyzed by logistic regression. Together, the 10 ASM modes demonstrated good discrimination of incident hip fracture. In models controlling for age and body mass index (BMI), the area under receiver operating characteristic (AUROC) curve for hip shape was 0.813, 95% confidence interval (CI) 0.771-0.854 compared with models containing femoral neck bone mineral density (AUROC = 0.675, 95% CI 0.620-0.730), intertrochanteric bone mineral density (AUROC = 0.645, 95% CI 0.589-0.701), femoral neck length (AUROC = 0.631, 95% CI 0.573-0.690), or femoral neck width (AUROC = 0.633, 95% CI 0.574-0.691). The accuracy of fracture discrimination was improved by combining ASM modes with femoral neck bone mineral density (AUROC = 0.835, 95% CI 0.795-0.875) or with intertrochanteric bone mineral density (AUROC = 0.834, 95% CI 0.794-0.875). Hips with positive standard deviations of ASM mode 4 had the highest risk of incident hip fracture (odds ratio = 2.48, 95% CI 1.68-3.31, p < .001). We conclude that variations in the relative size of the femoral head and neck are important determinants of incident hip fracture. The addition of hip shape to fracture-prediction tools may improve the risk assessment for osteoporotic hip fractures.

Citing Articles

Effect of Artificial Intelligence or Machine Learning on Prediction of Hip Fracture Risk: Systematic Review.

Cha Y, Kim J, Kim J, Seo S, Lee S, Yoo J J Bone Metab. 2023; 30(3):245-252.

PMID: 37718902 PMC: 10509025. DOI: 10.11005/jbm.2023.30.3.245.

Application of statistical shape modeling to the human hip joint: a scoping review.

Johnson L, Bortolussi-Courval S, Chehil A, Schaeffer E, Pawliuk C, Wilson D JBI Evid Synth. 2023; 21(3):533-583.

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Morphological risk factors for scaphoid fracture: a case-control study.

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Improving the Hip Fracture Risk Prediction with a Statistical Shape-and-Intensity Model of the Proximal Femur.

Aldieri A, Bhattacharya P, Paggiosi M, Eastell R, Audenino A, Bignardi C Ann Biomed Eng. 2022; 50(2):211-221.

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The influence of adult hip shape genetic variants on adolescent hip shape: Findings from a population-based DXA study.

Frysz M, Baird D, Gregory J, Aspden R, Lane N, Ohlsson C Bone. 2020; 143:115792.

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