It is difficult for the Cox model to predict absolute risk. It is impossible for the Cox model to project absolute risk beyond the range of observed failure times. This is because Cox models make use of an arbitrary baseline hazard function. According to the assumptions of the model, it is theoretically possible that immediately after you have observed patients, their hazard for CVD leaps 10,000 fold. We know that's not the case, but there's no way to reflect that in the model's assumptions.
The strength of the Cox model is quantifying the association of a risk factor with disease in terms of a hazard ratio. The hazard is the instantaneous risk of failure and thus is not interpretable on the raw scale, but a hazard ratio approximates a relative risk when the proportional hazards assumption is met.
Interpreting the results is easy: a hazard ratio for age of 15.2 means that participants differing by one unit in age have a 15 fold relative risk for CVD events. I say unit because 15 is a nonsensically large HR. This variable must be scaled by something. Not knowing the software you are using, getting a prediction of surviving proportion S0(10) = 0.95 when you haven't observed participants for 10 years suggests the survival outcome is coded incorrectly. When participants drop out of a study, they are left censored; you do not know if they died or not so you must code an event time for them when they leave and classify that event as a censoring event. This partly explains why the survival proportion is unbelievably high. However another possibility is that the survival prediction doesn't use prediction-at-the-means (predicted survival probability in a participant having all covariates equal to the average covariate value) but prediction-at-the-origin (survival probability in a participant with age 0, bmi 0, interarm systolic BP 0).
I'm afraid there would be very low credibility to a model which predicts 10 year absolute risk of disease that does not actually follow patients for 10 years. This in fact justifies the use of the Cox model for the time being.
The answer to the question in the title is yes. My suggestion would be to fit the adjusted Cox model controlling for as many confounders as possible: age, sex, atrial fibrillation, left ventricular hypertrophy (if available), diagnosed hypertension, treatment for hypertension, diabetes, weight, 1/height^2, BMI, sodium, etc. Then report the adjusted hazard ratio for intraarm SBP and its 95% CI. If this value does not include 1, then you can conclude there is evidence of an effect and that a larger study is warranted to predict risk (as one possibility).