Developing Novel Prognostic Biomarkers for Multivariate Fracture Risk Prediction Algorithms
- First Online:
- Cite this article as:
- Poku, E.K., Towler, M.R., Cummins, N.M. et al. Calcif Tissue Int (2012) 91: 204. doi:10.1007/s00223-012-9627-0
- 190 Downloads
Multivariate prediction algorithms such as FRAX® and QFractureScores provide an opportunity for new prognostic biomarkers to be developed and incorporated, potentially leading to better fracture prediction. As more research is conducted into these novel biomarkers, a number of factors need to be considered for their successful development for inclusion in these algorithms. In this review, we describe two well-known multivariate prediction algorithms for osteoporosis fracture risk applicable to the UK population, FRAX and QFractureScores, and comment on the current prognostic tools available for fracture risk; dual X-ray assessment, quantitative ultrasonography, and genomic/biochemical markers. We also highlight the factors that need to be considered in the development of new biomarkers. These factors include the requirement for prospective data, collected in new cohort studies or using archived samples; the need for adequate stability data to be provided; and the need for appropriate storage methods to be used when retrospective data are required. Area under the receiver operating characteristic curve measures have been found to have limited utility in assessing the impact of the addition of new risk factors on the predictive performance of multivariate algorithms. New performance evaluation measures, such as net reclassification index and integrated discrimination improvement, are increasingly important in the evaluation of the impact of the addition of new markers to multivariate algorithms, and these are also discussed.