pearson correlation in linear mixed models?

I'm trying to understand a research article that used linear mixed models, and I think they have done something wrong.

Basically, the experiment was that they got 22 subjects and:
-did baseline measures of 2 variables: the independent is called CCT and the dependent variable is IOP.
-they did the experimental intervention which caused CCT to increase, and IOP also increased due to the CCT increase.
-measurements were done over time on each subject to track the decrease of CCT and IOP back to baseline. (Therefore LMM was used, as there are multiple measures on each subject)

The article reports:
Linear mixed model analysis showed that the increase in measured IOP and CCT were related by the following equation: IOPG=0.10(CCT)-3.17 (r=0.84, p,0.001). The 95% confidence interval of the slope was 0.08 to 0.12.

However, my understanding is there is no R-sqaure or Pearson's 'r' in LMM, and the model is assessed using goodness of fit information criteria like the Akaike Information Criterion. So have the authors done something wrong or am I missing something? Also, does an LMM produce a regression equation and a confidence interval as the authors report, or is this wrong as well?