Con: Most clinical risk scores are useless.

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.