Can ensemble methods improve predictive performance of existing models estimating chronic kidney disease among patients with diabetes?

Many clinical prediction models exist that predict the same outcome, but commonly suffer poor performance when applied in new settings. Ensemble methods provide a method of combining multiple models developed across diverse settings to potentially improve predictive performance. When applied in primary care electronic medical records, we found that ensemble models based on existing clinical prediction models could match, but did not surpass the performance of the best performing component model. Ensemble methods may not be necessary to combine existing models; rather, the best performing component model can be used.