Effect of Clothing on Measurement of Bone Mineral Density

McNamara, Elizabeth; Feldman, Andrew B.; Malabanan, Alan O.; Abate, Ejigayehu G.; Whittaker, LaTarsha G.; Yano-Litwin, Amanda; Dorazio, Jolene; Rosen, Harold N.

doi:10.1016/j.jocd.2015.05.071

Cited by 6 publications

(3 citation statements)

References 6 publications

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“…The T value was calculated according to the BMD reference value of healthy adults of the same sex and nationality. 16 T value ≥ −1.0 indicates normal bone mass; −2.5<t value <-1.0 indicates bone loss; T value ≤ −2.5 indicates osteoporosis.…”

Section: Methodsmentioning

confidence: 99%

Development and Validation of Machine Learning Models for Prediction of Fracture Risk in Patients with Elderly-Onset Rheumatoid Arthritis

Chen¹,

Huang²,

Chen³

2022

IJGM

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Objective Fracture is a critical unfavorable prognostic factor in patients with rheumatoid arthritis(RA) and osteoporosis. At present, models involving clinical indices that accurately predict fracture are still uncommon. We addressed this gap by developing machine learning (ML)-based predictive models to individualize the risk of fracture in elderly patients with RA and osteoporosis and to identify a high-risk group for fracture. Methods 487 patients diagnosed with RA and osteoporosis at the Central Hospital of Enshi Tujia and Miao Autonomous Prefecture were randomly divided into a training cohort (used for building the model) and a validation cohort (used for validating the model). Five ML-assisted models were developed from candidate clinical features using two-step estimation methods. The receiver operating characteristic curve (ROC), decision curve analysis (DCA), and clinical impact curve (CIC) were performed to evaluate the robustness and clinical practicability of each model. Results A total of twenty-two candidate variables were included, and the prediction model was established by an ML-based algorithm. The areas under the ROC curve (AUCs) of the random forest classifier (RFC) model, artificial neural network (ANN), support vector machine (SVM), eXtreme gradient boosting (XGBoost), decision tree (DT), probability of major osteoporotic fractures (PMOF), and probability of hip fracture (PHF) ranged from 0.695 to 0.878. Among them, RFC obtained the optimal prediction efficiency via adding serum selenium and clinical indices, that is, glucocorticoid, and erythrocyte sedimentation rate (ESR). Conclusion Based on the classic clinical parameters, the fracture risk of RA patients with osteoporosis can be accurately predicted. In particular, RFC prediction model shows good discrimination ability in identifying high-risk patients with fracture.

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Section: Methodsmentioning

confidence: 99%

Development and Validation of Machine Learning Models for Prediction of Fracture Risk in Patients with Elderly-Onset Rheumatoid Arthritis

Chen¹,

Huang²,

Chen³

2022

IJGM

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“…Patient clothing can also have an impact on BMD 15,35,36 (Figure 10). Phantom studies haves shown that thick or reflective clothing can negatively impact DXA precision.…”

Section: Interpretation Pitfallsmentioning

confidence: 99%

“…Phantom studies haves shown that thick or reflective clothing can negatively impact DXA precision. 35,36 This can be mitigated by having the patient avoid wearing clothing made of dense materials (such as wool or denim), reflective material, or that which uses metallic thread.…”

Section: Interpretation Pitfallsmentioning

confidence: 99%

Bone Mineral Densitometry Reporting: Pearls and Pitfalls

2020

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Dual-energy X-ray absorptiometry (DXA) is the method of choice for assessing bone mineral density (BMD). Unfortunately, the performance and interpretation of DXA can be challenging and errors are common. In fact, it has been reported that up to 90% of BMD reports contain at least 1 error. Errors can be the result of technique or interpretative in nature or both and can result in inappropriate diagnosis and management. In this article, we review the various types of pitfalls frequently encountered by physicians interpreting DXA studies. Being aware of these pitfalls will help readers recognize and avoid them when encountered in clinical practice.

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Quality in dual-energy X-ray absorptiometry scans

Morgan

Prater

2017

Bone

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Effect of Clothing on Measurement of Bone Mineral Density

Cited by 6 publications

References 6 publications

Development and Validation of Machine Learning Models for Prediction of Fracture Risk in Patients with Elderly-Onset Rheumatoid Arthritis

Development and Validation of Machine Learning Models for Prediction of Fracture Risk in Patients with Elderly-Onset Rheumatoid Arthritis

Bone Mineral Densitometry Reporting: Pearls and Pitfalls

Quality in dual-energy X-ray absorptiometry scans

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