Display options
Cite
Dekker FW, Ramspek CL, van Diepen M. Con: Most clinical risk scores are useless. Nephrol Dial Transplant. 2017;32(5):752-755doi: 10.1093/ndt/gfx073.
Dekker, F. W., Ramspek, C. L., & van Diepen, M. (2017). Con: Most clinical risk scores are useless. Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association, 32(5), 752-755. https://doi.org/10.1093/ndt/gfx073
Dekker, Friedo W, et al. "Con: Most clinical risk scores are useless." Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association vol. 32,5 (2017): 752-755. doi: https://doi.org/10.1093/ndt/gfx073
Dekker FW, Ramspek CL, van Diepen M. Con: Most clinical risk scores are useless. Nephrol Dial Transplant. 2017 May 01;32(5):752-755. doi: 10.1093/ndt/gfx073. PMID: 28499028.
Copy Download .nbib Format: NLM
Share it on

Nephrol Dial Transplant. 2017 May 01;32(5):752-755. doi: 10.1093/ndt/gfx073.

Con: Most clinical risk scores are useless.

Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association

Friedo W Dekker, Chava L Ramspek, Merel van Diepen

Affiliations

  1. Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands.

PMID: 28499028 DOI: 10.1093/ndt/gfx073

Abstract

While developing prediction models has become quite popular both in nephrology and in medicine in general, most models have not been implemented in clinical practice on a larger scale. This should be no surprise, as the majority of published models has been shown to be poorly reported and often developed using inappropriate methods. The main problems identified relate to either using too few candidate predictors (based on univariable P < 0.05) or too many (for the number of events), resulting in poorly performing prediction models. Guidelines on how to develop and test a prediction model all stress the importance of external validation to test discrimination and calibration in other populations, as prediction models usually perform less well in new subjects. However, external validity has not often been tested for prediction models in renal patients. Moreover, impact studies showing improved clinical outcomes when using a prediction model in routine clinical practice have been reported rarely. By and large, notwithstanding a few notable exceptions like the kidney failure risk equation prediction model, most models have not been validated externally or are at best inadequately reported, preventing them from be used in clinical practice. Therefore, we recommend researchers to spend more energy on validation and assessing the impact of existing models, instead of merely developing more models that will most likely never be used in clinical practice as well.

© The Author 2017. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.

Keywords: clinical prediction models; nephrology; prediction research; prognosis; risk prediction

MeSH terms

Publication Types