OBJECTIVES: Probabilistic sensitivity analysis (PSA) repeatedly runs a simulation model with randomly selected inputs to characterize the plausible distributions of projected outcomes. In cost-effectiveness analysis, PSA results are conventionally displayed as incremental cost-effectiveness scatter plots or cost-effectiveness acceptability curves. We explored patient-relevant health outcomes other than QALYs and investigated approaches for displaying probabilistically generated values, using the case of catheter-based renal denervation (RDN) for resistant hypertension. METHODS: A Markov model with 54 input distributions simulated the impact of RDN on stroke, myocardial infarction (MI), coronary heart disease (CHD), heart failure (HF), end-stage renal disease (ESRD), cardiovascular (CV) and all-cause death based on the Symplicity HTN-2 RCT and the literature. We report medians and 95% credible intervals (95% CI), defined as containing 95% of all simulation results, of 10-year relative risk reductions. One-sample t-tests and ANOVA were used to compare posterior distributions. Additional analyses evaluated alternative time horizons, with values ranging from one year to lifetime. RESULTS: Relative risk reductions over ten years (95% CI) were: stroke 31.4% (23.1; 36.0%), MI 30.0% (8.9; 38.1%), CHD 22.2% (9.0; 29.4%), HF 13.9% (11.0; 21.7%), ESRD 30.7% (28.8; 34.5%), CV death 30.3% (12.6; 35.7%), and all-cause death 14.4% (4.1; 20.1%). All clinical outcomes were significantly different from 0% and between each other (p<0.0001). Distribution dotplots, cumulative hazard curves, and survival curves with 95% credibility intervals were developed as graphic representations of probabilistic results. CONCLUSIONS: While model-based projections suggest that catheter-based renal denervation leads to clinically and statistically significant event reductions across clinical outcomes, considerable differences exist between the distributions of individual reductions. Graphic, probabilistic representation of patient-relevant endpoints may facilitate a deeper understanding of uncertainty associated with the long-term clinical effectiveness of new therapies. In addition to informing clinicians and payers, the proposed conceptual approach could also contribute to patient education and shared decision making.