placeholderCardiac digital twins are powerful tools to improve treatment of complex cardiac arrhythmias. However, such computational models rely on many uncertain inputs, and the effects of this input uncertainty on the model-derived treatment strategies are unclear. We have developed a computational model-guided ablation planning tool to aid in the ablation of reentrant circuits found in atypical left atrial flutter (ALAF). We then applied parametric uncertainty quantification to assess the effect of errors and variability in the conductivity of fibrotic tissue on the model outputs and suggested ablation patterns. In a digital twin of a patient who presented with ALAF, we found that our model-guided ablation tool reduced the number of simulated ALAF circuits from 10 pre-ablation to 4 post. Uncertainty quantification revealed that fibrotic conductivity affected the suggested ablation sites substantially, however, the uncertainty quantification also provided a method to display a proposed ablation strategy in a manner that accounts for the input parameter uncertainty. The results of this study show the twofold insight of UQ. This method provides a robust means to explore the effects of input parameter variability on predictions of reentrant arrhythmia; we suggest it can also present modeling results that display the uncertainty associated with model predictions.