Bayesian adaptive designs have gained popularity in all phases of clinical trials in the past few decades. Design of Bayesian adaptive trials, however, requires extensive simulation studies. The required computation may become infeasible in complex modelling frameworks or time sensitive settings. In this talk, I propose a set of methods for efficient estimation and uncertainty quantification for the design operating characteristics of Bayesian adaptive trials. The proposed approach employs spatial modelling techniques to provides estimates of the sampling distribution of the ``test statistic" -typically a posterior or posterior predictive probability statement in Bayesian adaptive designs- throughout the model parameter space. Various design operating characteristics can then be readily obtained as quantiles of this sampling distribution without additional simulations. I consider design of an adaptive clinical trial with the ordinal scale disease progression endpoint analyzed via the proportional odds model to showcase the implementation and performance of the proposed approach.