Hazard ratio plot with confidence "waist" in ggplot2 - r

Question

When fitting a cox model that includes spline terms for a continuous covariate, I would like to be able to produce a plot of the hazard ratio across range of that covariate (relative to a fixed reference value) using ggplot2.
I have adapted an example from Terry Therneau's splines vignette here (see page 3). The only issue with this approach is the lack of a "waist" in the confidence interval at the reference value, as in this plot:
The example below produces the following plot, without the narrowing of the CI at the reference value.
library(survival)
library(splines)
library(ggplot2)
# colon cancer death dataset
ccd <- na.omit(subset(colon, etype == 2))
# fit model with ns() term for age
cox <- coxph(Surv(time, status) ~ rx + sex + ns(age, knots = c(20, 50, 70)), data = ccd)
# get data for plot
tp <- termplot(cox, se = TRUE, plot = FALSE)
# hazard ratio plot for natural spline of age, with reference # 50 yrs
ref <- tp$age$y[tp$age$x == 50]
ggplot() +
geom_line(data = tp$age, aes(x = x, y = exp(y - ref))) +
geom_line(data = tp$age, aes(x = x, y = exp(y - 1.96 * se - ref)), linetype = 2) +
geom_line(data = tp$age, aes(x = x, y = exp(y + 1.96 * se - ref)), linetype = 2) +
geom_hline(aes(yintercept = 1), linetype = 3) +
geom_rug(data = ccd, aes(x = age), sides = "b") +
labs(x = "Age at baseline, years",
y = "Hazard Ratio (95% CI) vs. 50 years",
title = "Mortality hazard ratio as a function of age",
subtitle = "Natural spline: knots at 20, 50, and 70 years")
I am aware that there are features in the rms package and the smoothHRpackage that produce these types of plots, but I am looking for a solution that is amenable to ggplot2 graphics and the coxph() function in the survival package. My question therefore boils down to:
Is there a way to adapt the output of termplot() to produce a plot with a "waist" at the reference value?
If termplot() cannot be used, how can I obtain the relevant plotting data by other means?
Edit 1: As the first comment suggested, this can be accomplished using rms and ggplot2 together. For example:
library(rms)
dd <- datadist(ccd)
dd$limits$age[2] <- 50
options(datadist = "dd")
cph <- cph(Surv(time, status) ~ rx + sex + rcs(age, c(20, 50, 70)), data = ccd, x = TRUE, y = TRUE)
pdata <- Predict(cph, age, ref.zero = TRUE, fun = exp)
ggplot(data = pdata) +
geom_hline(aes(yintercept = 1), linetype = 3) +
labs(x = "Age at baseline, years",
y = "Hazard Ratio (95% CI) vs. 50 years",
title = "Mortality hazard ratio as a function of age",
subtitle = "Natural spline: knots at 20, 50, and 70 years")
Which produces a plot very close to what I am after:
However, I would still like to know if there is a way to do this using coxph() and ns(). Not that I have anything against the rms package, I just have a bunch of old code based on survivalfunctionality.