Bootstrapping was used to correct for possible overoptimistic results of the final model. Bootstrapping is an internal validation technique, where many repeated samples are drawn with replacement from the data set at hand. Bootstrapping generates an estimate of how well the model might fit in a new study population. In other words, bootstrapping estimates the expected optimism in model performance or shrinkage of the model [17].