Tag: R

Calculate the Charlson Score in R

It is unlikely that anyone else will have data formatted in the same way as me, but it shouldn’t be too hard to convert this.

You just need a data where the rows represent patients, and there are a series of columns containing indicator variables for the components of the Charlson score[1].

The following function looks for these 20 variables, and uses a quick bit of matrix multiplication to generate the total score.

For reference, here is the table from the original paper containing the weights for the score.

Charlson Score

[1]: Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. Journal of Chronic Diseases. 1987;40:373-383.

Syntax highlighting for R in the terminal

So R-studio seemed to be running really slowly today which prompted me to try using R in the terminal. This works nicely with R-Box. Otherwise said, type in Sublime Text, and execute in R (via iTerm.)

This all worked much more quickly, and the plots show up in a lovely quartz window. I lose a lot of the easy point-and-click functionality, but I never used the text editor so I don’t miss that.

What I did miss was syntax highlighting. The solution (via StackOverflow as usual) is a super cool little package called colorout.

Before (top half) and after (bottom half) of my screen. Which looks nicer?

141128 iTerm colorout screenshot

Don’t forget to load the package in your {{EJS1-14}} file.

Calculate the SOFA score in R

A follow on from the Charlson score function previously posted. Here are functions to calculate the SOFA score.

Please note

  • it’s almost inconceivable that your data will be similar to mine, and you will be able to just use these ‘as is’; however, they might provide a useful skeleton.
  • there are some add-ons included (e.g. if a blood gas is not available then you can still generate the SOFA respiratory score using oxygen saturations and the S:F ratio via this (slightly flawed) proposal)
  • there are some arbitrary decisions too (i.e. vasopressin use is considered to assign patients to 4 SOFA points for cardiovascular dysfunction)

Bootstrap at the cluster or the unit level

I have been using the bootstrap more often recently, but the data that I use is typically structured with patients nested in hospitals. The wonderful Cross Validated recommends that any sampling that is to be done should respect the structure of the data.

This means first sampling (with replacement) hospitals, and then sampling (with replacement again) within each hospital before re-assembling the data.

There is a better explanation along with a code snippet from the biostats department at Vanderbilt. However, with 48 hospitals and 15,000 patients, this ran very slowly.

I have re-written this using the data.table with a good (great?) improvement in speed (but some loss of flexibility).

# Generate sample data
# --------------------

tdt <- data.table(id=1:15000, site=1:50, age=round(rnorm(n=10000, mean=65, sd=18)))

# Non data.table version
# ----------------------

resample <- function(dat, cluster, replace) {
  # exit early for trivial data
  if(nrow(dat) == 1 || all(replace==FALSE))
  # sample the clustering factor
  cls <- sample(unique(dat[[cluster[1]]]), replace=replace[1])
  # subset on the sampled clustering factors
  sub <- lapply(cls, function(b) subset(dat, dat[[cluster[1]]]==b))
  # sample lower levels of hierarchy (if any)
  if(length(cluster) > 1)
    sub <- lapply(sub, resample, cluster=cluster[-1], replace=replace[-1])
  # join and return samples
  do.call(rbind, sub)

# Data.table version
# ------------------

rsample2 <- function(data=tdt, id.unit=id.u, id.cluster=id.c) {

    # Generate within cluster ID (needed for the sample command)
    tdt[, "id.within" := .SD[,.I], by=id.cluster, with=FALSE]

    # Random sample of sites
    bdt <- data.table(sample(unique(tdt[[id.cluster]]), replace=TRUE))

    # Use random sample of sites to select from original data
    # then
    # within each site sample with replacement using the within site ID
    bdt <- tdt[bdt, .SD[sample(.SD$id.within, replace=TRUE)],by=.EACHI]

    # return data sampled with replacement respecting clusters
    bdt[, id.within := NULL] # drop id.within

cluster <- c("site", "id")
system.time(d <- resample(tdt,cluster,c(T,T)))
> system.time(d <- resample(tdt,cluster,c(T,T)))
   user  system elapsed 
  8.597   0.092   8.786 
system.time(d <- rsample2(tdt, id.unit="pid", id.cluster="site"))
> system.time(d <- rsample2(tdt, id.unit="pid", id.cluster="site"))
   user  system elapsed 
  0.051   0.001   0.052