• Medientyp: E-Artikel
  • Titel: Construction of a predictive model for osteoporosis risk in men: using the IOF 1-min osteoporosis test
  • Beteiligte: Zhang, Kun; Wang, Min; Han, Weidong; Yi, Weihong; Yang, Dazhi
  • Erschienen: Springer Science and Business Media LLC, 2023
  • Erschienen in: Journal of Orthopaedic Surgery and Research, 18 (2023) 1
  • Sprache: Englisch
  • DOI: 10.1186/s13018-023-04266-7
  • ISSN: 1749-799X
  • Schlagwörter: Orthopedics and Sports Medicine ; Surgery
  • Entstehung:
  • Anmerkungen:
  • Beschreibung: <jats:title>Abstract</jats:title><jats:sec> <jats:title>Objective</jats:title> <jats:p>To construct a clinical prediction nomogram model using the 1-min IOF osteoporosis risk test as an evaluation tool for male osteoporosis.</jats:p> </jats:sec><jats:sec> <jats:title>Methods</jats:title> <jats:p>The 1-min test results and the incidence of osteoporosis were collected from 354 patients in the osteoporotic clinic of our hospital. LASSO regression model and multi-factor logistic regression were used to analyze the risk factors of osteoporosis in patients, and the risk prediction model of osteoporosis was established. Verify with an additional 140 objects.</jats:p> </jats:sec><jats:sec> <jats:title>Results</jats:title> <jats:p>We used logistic regression to construct a nomogram model. According to the model, the AUC value of the training set was 0.760 (0.704–0.817). The validation set has an AUC value of 0.806 (0.733–0.879). The test set AUC value is 0.714 (0.609–0.818). The calibration curve shows that its advantage is that the deviation correction curve of the nomogram model can maintain a good consistency with the ideal curve. In terms of clinical applicability, compared with the "total intervention" and "no intervention" schemes, the clinical net return rate of the nomogram model showed certain advantages.</jats:p> </jats:sec><jats:sec> <jats:title>Conclusion</jats:title> <jats:p>Using the 1-min osteoporosis risk test provided by IOF, we built a male osteoporosis risk prediction model with good prediction effect, which can provide greater reference and help for clinicians.</jats:p> </jats:sec>
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