Understanding the value of a class variable in the "randomForest" package R

I am having trouble understanding the class columns in the importance function inside randomForest.

My dataset has two classes: "Current" and "Sent". To predict these classes,

First, I create a random forest model:

fit <- randomForest(IsDeparted ~ ..., df_train),

      

        
        
        
      

    

Then I run the importance function:

importance(fit)   

      

        
        
        
      

    

Now I am getting a snippet of results like this:

Can someone explain how to interpret the first two columns of the class? Is this the average decrease in prediction accuracy for one particular class after permuting the values ​​of that particular variable? And if so, does that mean I should focus on those columns rather than the MDA column when selecting a feature if I'm more interested in model performance for one particular class?