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Visualising the ATC

intro_plot_ATC.Rmd

visATC

The goal of visATC is to extract subtrees and plot (as networks) from the World Health Organisation’s (WHO) Anatomical Therapeutic Chemical (ATC) system. The ATC arranges medicines in a 5-level hierarchical system with body-system, functional and pharmacological levels. See https://www.who.int/tools/atc-ddd-toolkit/atc-classification for more information.

In this package, tree objects are created to represent this hierarchy, with subsets of the hierarchy made in different ways, including pruning or taking cuttings. Plots of the trees can be made in a layered or circular fashion. To reduce clutter, descriptive text about the nodes is made available by hovering over the nodes using the package plotly. Because the ATC system is so large, using hovering within plots (via plotly) is the main advantage of visATC.

The functionality here (code wholly rewritten since) was used as part of an approach to the detection of rare side-effects to drugs. This included Bayesian ‘partial pooling’ of event rates in families of drugs, with the family structure supplied by ATC. That analysis also supplied data to each node pooled from European pharmacological databases and included further modelling.

Results were reported in:

Schuemie, M. et al. (2012) Using electronic health care records for drug safety signal detection: a comparative evaluation of statistical methods. Medical Care 50(10):890-897

Installation

You can install the development version of visATC from GitHub with:

# install.packages("pak")
pak::pak("jnm212/visATC")

The ‘full’ tree, containing all of levels 1 to 5, is created and summarised here:

h <- atctree(schema="full")
h
#> 
#>  schema:  full
#>  total number of nodes:  6996
#>  total number of levels:  5
#>  number of nodes by level :
#>    1    2    3    4    5 
#>   14   94  271  939 5678

The lowest (most specific) level contains individual drugs of which there are 5678 included in the ATC release used here. The full tree is large and could be plotted with plot(h) but takes some 10s of seconds.

The ATC combines three concepts (anatomical, functional, pharmacological). When utilising the ATC tree it may be desirable (but not necessary) to select one of these. There are three prespecified ‘schemas’ which are tree representations of the hierarchical information and can be specified in the atctree constructor, along with the full tree which combines all of them. The specified schemas contain the following levels:

  • full: 1,2,3,4,5
  • anatomical : 1, 5
  • therapeutic : 2, 3, 5
  • chemical : 4, 5

Other combinations of the levels may be specified but are bespoke.

When using one of the four prespecified schema, default plotting attributes are chosen with a little intelligence: for example, the default behaviour when plotting the full tree is not to annotate and not to plot on a circle (note that text by hovering is always available).

The highest level of the ATC is anatomical and can be plotted compactly as a tree. In this case there is space to directly print the associated text on the stems, rather than only rely only on the hover mechanism.

# just the top level of the tree
h <- atctree(schema="anatomical")
plot(h, width=1000)
#> Remember you can hover over nodes to see text

We can take a subset of the tree, using the ‘cutting’ function to examine the drugs within a single anatomical group:

h <- atctree(schema="anatomical")
plot(cutting(h,"D"), circle=T)
#> Remember you can hover over nodes to see text

The second and third levels are functional or therapeutic. First plot the nodes within the functional group P01 (antiprotozoals):

h <- atctree(schema="therapeutic")
# annotations on this tree are too cluttered, so take a cutting
plot(cutting(h, "P01"), width=1000)
#> Remember you can hover over nodes to see text

and now a chemical group:

h <- atctree(schema="chemical")
# annotations on this tree are too cluttered, so take a cutting.
# here there's enough space not to surpress the leaf labelling:
plot(cutting(h, "J01DD"), leaf_label=T, text_size=5)
#> Remember you can hover over nodes to see text
# try plotting on a circle:
plot(cutting(h, "J01DD"), leaf_label=T, circle=T, text_angle=0, text_size=5)
#> Remember you can hover over nodes to see text

You might want to produce a bespoke tree with a subset of levels. These will have their schema set to ‘none’, and you will need to make decisions about the plotting arguments. For example:


h <- atctree(whichlevs=c(1,2,4))
plot(h, leaf_label=F)
#> Remember you can hover over nodes to see text

To produce a less cluttered display, parts of the tree can be eliminated by pruning. See pruning.

Another way to go about producing plots, less intuitive but with more control, is using subsetting with the [ operator - see subset. This is most likely to be useful with a full ATC tree, since other schema (anatomical, therapeutic, chemical) have few generations to choose from.