How can you adjust a prediction based on features in the future being different than predicted?
Try these two approaches:

First:

Make a model, using any ML algorithm and divide your data into train and test. 
Now using the previous features, check the train and test accuracy.

Now add the new features to the previous ones, again divide data into train and test.  Check the train and test accuracy.

If the new features help improve the test accuracy then only use them otherwise just skip them.

Second:

Add the new features to the previous dataset and set their values as null/0. So while predicting the results before wont be affected by their values. And later when these features do get values, they will help improve the predictions.

I hope this helps!