Hello.
My name is Jennifer Barb, and I'm a research scientist
at the National Institutes of Health Clinical Center.
I'm going to talk to you today
about how I use JMP to manipulate research clinical medication data
and how I was able to create a publication quality figure
to show how the patient medications were used
through the course of a treatment protocol at the Clinical Center.
Clinical data,
especially in a research setting, can be extremely noisy.
There are a lot of staff and personnel who are involved in research protocols,
and the collection and storage of pertinent research data
is not always streamlined.
I will talk to you about how I use the JMP Graph Builder tool
to visualize patient medication prescriptions
through the course of a six- month treatment protocol
and how we were able to visualize what is called a Shannon Diversity Index
with relation to the antibiotic use
that were prescribed in the patient [inaudible 00:00:55]
in the clinical research setting.
I will go through how I created the illustration in JMP
using four patients with a very rare disease
that were enrolled in the treatment protocol.
As part of the treatment regimen of this protocol,
the four patients were prescribed a range of antibiotics,
totaling up to 21 different types of medications.
The data were provided to me in a long format,
including a start and stop date of medication administration.
As you can see here, I zoomed into the first figure of the poster.
What we're looking at here is a snapshot
of what some of the research data look like.
In the long format,
you see that there are repetitive rows of the patient ID
and there are repetitive rows of the different medications
that the patient received during the treatment protocol.
There's a lot of redundancy here.
In addition to that, we have a start of medication date
and a stop of medication date that each person received.
One of the first steps I had to take within the JMP data manipulation tools
was to edit the medication name so that it did not have so many words
in the medication name
and also did not include the dosage information
so that we could use this
as one of the axes of the graph that I'm going to make.
In addition to that, I had to check the date of patient consent
into the treatment program
and to see if the start and stop date of that person's medication administration
fell within the treatment protocol.
From that point then, I had to normalize each person's medication start and stop
so that everybody had a day one and it would all corresponded
to the certain point of the treatment protocol.
All of this information will be used to create the figure that I will show
at the end of this.
Once I was able to edit the medication name
and create the normalized medication start and stop,
I will then use the Graph Builder tool.
I also wanted to talk about one other aspect
of this particular research protocol,
and that is the fact that we wanted to look
at the oral microbiome of the patients in the treatment program.
What this means is that
we took samples of each patient's oral tongue brushings
and then converted those into specific counts of bacteria
that were found in their mouth.
What we ended up wanting to do was to look at how the antibiotic treatment
through the treatment protocol might have affected the oral microbiome.
As we know,
antibiotics can drastically change your gut microbiome
and can cause increases and decreases
of different microbial diversity in the gut.
But one question that has not been elucidated
is whether or not antibiotic use would also affect the oral microbiome.
What I'm showing here is that
we have built a set of scripts within the JMP
where we install that on the toolbar.
We have a specific set of scripts that would calculate the Shannon Diversity
of the bacterial counts in the table
associated with the medications of what I just showed on the previous slide.
Back to the medication table,
the first step that I took was to open up the JMP Graph Builder tool.
The first thing that I did was to drag and drop
the medication start and stop date into the X- axis as shown here.
Then I would go to the bar graph tool
and click that to make the data into a bar graph.
The third step was to drag and drop
the actual antibiotic shortened medicine name
into the Y- axis.
And then finally, in order to create the graph so that I could visualize
the longitudinal duration of medication administration,
I changed the bar type into stock.
Finally, as I talked to you earlier about the way
in which we were able to code the treatment time of the protocol
based on the medication start and stop,
we also were able to stratify the antibiotic use
into this different time point of the treatment protocol as here.
Now, all of these,
if you are familiar with the JMP Graph Builder tool,
is great ways that there's so many different possibilities
on how you can manipulate data to get a particular graph that you want.
And finally, one last thing we did was we took the patient ID
that was in the medication table
and colored each bar on the graph by patient.
The final figure looks like this.
So what you see here is all of the different antibiotics
that were prescribed in the treatment protocol.
You also see time point B,
which is the time point between baseline and the treatment of the protocol,
and time point C, which is the intervention point
starting at time point C, and then the end of the treatment protocol.
What you see here is a longitudinal bar
indicating the amount of time a person was on a given antibiotic.
And then you also see each of these different bars
stratified by patient color.
This particular figure did end up going into the publication,
and it was a nother way to look at a large table of medications
downloaded from our research database into a graphical form to visualize
all of the different medications
that the patient received during the treatment.
Now, finally, you might want to ask, why do we want to look at this?
One thing of importance for us
was to actually look at the oral microbial diversity.
As I mentioned,
we were able to take a separate table that corresponded to the patients
within the treatment protocol
and calculate what is called a Shannon Diversity metric.
A higher diversity indicates higher oral microbial diversity,
and a lower index indicates lower microbial diversity.
From within JMP, we were able to superimpose
the treatment leg between time point A and B
and the change of the diversity metric
from time point the start of the treatment to the end of the treatment.
Also, we're able to look at within one patient
how the different antibiotics correspondent to this.
Then the second leg of the protocol, we were able to see a slight rebound
of the diversity index
in correlation with the number of antibiotics
that were used in that treatment leg.
In conclusion,
we were able to visualize patient- prescribed antibiotics
through the course of a treatment protocol
using the JMP Graph Builder tool.
We took a table of 1,289 rows of medication employed in the protocol
and created a simplified graph of visualization.
We also were able to calculate a Shannon Diversity Index
on bacteria data associated with each person's oral samples.
We superimpose these two graphs, and it allowed us to draw conclusions
on how the antibiotics prescribed to each patient
might have affected the oral microbiome of individuals in the treatment protocol.
Finally, our group has used the graphical nature of JMP for many years
in a way to translate complex medical research data
into data- driven discovery and investigation.
The use of JMP has facilitated many publications
and highly cited research journals for our group.
Thank you for your time today.
