A physician says, well, I'm starting the patient on one drug.
Do they take any other drugs whose dose I should adjust?
The question of drug interactions, everybody's familiar with that idea, but
the number of drug interactions is huge and some of them are very, very important.
Some of them are less important and so the question is every time you add a drug to
a complicated regiment, are you running the risk of a drug interaction?
Do you need to know about that?
Do you need to be alerted?
On the other hand, I will say now that there are certain things electronic
medical records don't do well.
And if you alert physicians 20 times a day every time they change medications for
drug interactions that are probably trivial, they start to ignore everything.
And they start to ignore everything, they'll ignore the important stuff.
Of course, electronic records have the problem that somebody has to do the input.
There's a lot of push back in the healthcare community around
the need to develop those records.
But I think once the growing pains are over, the healthcare system
can be efficient enough that you can not slow down to actually get
the information into the record and actually improve care.
So a researcher will come to me and will say,
I'm interested in studying variability in vitamin D levels.
How many of our patients have vitamin D levels across this entire system?
That's a question that you could never even ask in a paper environment.
Now you can ask and you can also ask as I'll show in a minute,
do any of them have DNA samples that are stored somewhere?
That would be an important question for a researcher, and then a physician might
say, well, a patient tells me he gets high every time he gets codeine for
dental pain, then a physician might say, I wonder why that is?
I hope many of you will have guessed that that person is probably
an ultra rapid metabolizer for CYP2D6 and
they biotransform codeine into morphine very, very rapidly.
Now it would be nice to know that information in a patient before they get
codeine, you can do it by history or
you can do it by having the genetic information embedded in their electronic
medical record against the day that they might receive a CYP2D6 substrate.
And in the next module, I'll talk about initial efforts at our center and
other places to actually execute that kind of vision.
So at Vanderbilt, we have created large resources for
using electronic medical records as a tool for research for discovery and
that's what I want to talk about here.
And in the next module, I'll talk about how we use that to also
delivery information that we hope will improve healthcare.
So we have created a de-identified image of our entire electronic
medical record and that is over 2.3 million subjects.
Because it's de-identified, it's relatively easy to use for accruing large
sets of patients and asking interesting questions about what it is their like?
What it is that they have?
How their diseases, or their conditions, progressed?
And notice I haven't used the word genetic yet, because the 2.3 million
people don't have DNA samples, but you can still use that information to
predict who after admission to the hospital is likely to get a bedsore.
After admission to hospital, which patient is likely to be discharged and
readmitted within 30 days?
And if you could use information from examining large numbers of subjects
to develop predictive tools like that, you can start to personalize care
on the level of preventing complications in the hospital.
On the level of preventing remissions on the level of individualizing or
personalizing care, which has nothing to do with genetics.
At Vanderbilt, we've created a large data bank that we call BioVU that
includes around 185,000 subjects that have DNA samples coupled to
these identified electronic medical records.
And we're in the process of doing a lot of genetic work on those
at the GWAS level and other kinds of levels to use that information.
To do discovery.
Discovery of variants that drive variability in drug response,
variants that drive variability and susceptibility to disease.
So I alluded to this idea before,
that an investigator might have this question around vitamin D.
And in fact one of our investigators had exactly that question.
So we've created an interface at Vanderbilt that allows an investigator to
ask that question and get an answer within 60 seconds.
So they go to a web site, the web site looks like this, and
it's a lot of drag and dropping.
But they basically drag and drop the vitamin D levels and
ask, how many patients do we have with vitamin D levels that
is something greater than 17,000 in our electronic medical record.
And then they'll ask how many of those patients have genetic information, and
it's something around 10,000.
And then the intersection of those sets, right now it's around 1,795 a subject.
So that's something that happens on a high speed search engine, and
it happens instantaneously.
So an investigator can find out how many samples there are, and
is there viability in doing a study.
One of the other things that we have done at Vanderbilt is we've developed a new
kind of technology to interrogate the relationship between human diseases and
genetic variation.
The top of this slide shows the typical GWAS approach,
or a typical approach across genetics, identify a phenotype.
The phenotype could be high or low cholesterol.
The phenotype could be breast cancer in your family.
The phenotype could be macular degeneration.
The phenotype could be red hair.
And then you do a genome wide scan across 500,000 or
a million common SNPs in the genome and you come up with signals
using the Manhattan plot, that's familiar to many of you.
That's the GWAS, the genome-wide association study.
We've created the phenome-wide association study which we call PheWAS.
And the PheWAS turns that question on its head.
It says here is a gene or
here is a genetic variant that seems to be of biological interest.
But I don't know exactly what human phenotype it associates with.
So I take the particular genetic variant and
I do exactly the same search strategy as with the GWAS.
I take the genetic variant and I say you either have the reference allele or
you have a variant allele.
And then for every diagnosis in the electronic medical record I say
you have the diagnosis or you don't have the diagnosis.
So you create a two-by-two table, you attach a P value to that two-by-two table,
and you display the data just like a Manhattan plot.
And you can see on that particular PheWAS that I'm showing you here,
there are some dots that seem to rise up, and
I'll show you those on a subsequent slide in a moment.
The idea is, to do this experiment you have to have many,
many patients with many, many diagnoses.
And the electronic medical record lends itself to that very, very nicely.
