Document Type
Article
Publication Date
9-24-2018
Abstract
Osteoporosis is the most common metabolic bone disease and goes largely undiagnosed throughout the world, due to the inaccessibility of DXA machines. Multivariate analyses of serum bone turnover markers were evaluated in 226 Orange County, California, residents with the intent to determine if serum osteocalcin and serum pyridinoline cross-links could be used to detect the onset of osteoporosis as effectively as a DXA scan. Descriptive analyses of the demographic and lab characteristics of the participants were performed through frequency, means and standard deviation estimations. We implemented logistic regression modeling to find the best classification algorithm for osteoporosis. All calculations and model building steps were carried out using R statistical language. Through these analyses, a mathematical algorithm with diagnostic potential was created. This algorithm showed a sensitivity of 1.0 and a specificity of 0.83, with an area under the Receiver Operating Characteristic curve of 0.93, thus demonstrating a high predictability for osteoporosis. Our intention is for this algorithm to be used to evaluate osteoporosis in locations where access to DXA scanning is scarce.
Recommended Citation
Levesque E, Ketterer A, Memon W, et al. The Chapman bone algorithm: A diagnostic alternative for the evaluation of osteoporosis. Bone Muscle. 2018;1:1-6.
Peer Reviewed
1
Copyright
The authors
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Included in
Diagnosis Commons, Health Information Technology Commons, Investigative Techniques Commons, Musculoskeletal Diseases Commons, Musculoskeletal System Commons, Other Analytical, Diagnostic and Therapeutic Techniques and Equipment Commons, Other Computer Sciences Commons, Other Mathematics Commons, Other Medicine and Health Sciences Commons, Other Physical Sciences and Mathematics Commons, Theory and Algorithms Commons
Comments
This article was originally published in Bone and Muscle, volume 1, in 2018.