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An interesting resource that got some publicity today

I found this website today: http://ubble.co.uk/

It was highlighted on cnn.com under the provocative headline: ‘quiz claims
to predict your death’

I probably should have instead found it by reviewing the scholarly
literature from which it was published:
http://www.thelancet.com/journals/lancet/article/PIIS0140-6736(15)60175-1/abstract

Essentially, these reseachers have released a website of the ‘all-by-all’,
of all baseline characteristics relative to a large array of mortality
causes, providing age-adjusted predictions associated with each variable.
They’ve provided a fairly easy-to-use interface to explore the data and
build up a predictive model.

I’m impressed that this team has put all the pieces together and released
in an effective communications campaign. I think it serves as a powerful
example for what’s possible for making data useful both for academic
community and for the general public.

I’ll been keen to hear everyone’s thoughts on this development. Is this
the kind of direction you’d like to see the OHDSI community pursue? Are
there methodological issues with the approach they’ve taken that give you
pause? Wouldn’t it be amazing to have results from around the world,
across our 50+ databases and 600m+ patients all working together toward a
common goal of informing patient care?

Cheers,

Patrick

I think we should push towards research that allows us to suggests a
second line treatment for diabetes based on what has worked for others
(i.e. after our current profiling, we are able to say that X% of times, if
you use drug A you will end up switching to drug B).

Thanks for passing that along. It is definitely interesting.

I think OHDSI should choose some carefully crafted study designs that leverage the strengths of both the data and the community. We have such a broad range of expertise, from genomics to NLP to epidemiology to health services research to health economics. And we have global reach. Analyses that cut across 2 or 3 (or more, if possible) areas would be really innovative.

To follow onto Nigam’s comments, we could compare second-line anti-diabetic treatment across regions (and over time). We could also compare time to second-line treatment by region.

Friends:

The diabetes thing is intriguing, but I see a problem. If we recommend a treatment based on some data we collect, like these ubble folks do, we become a medical device, defined as diagnosing, treating or managing disease. The FDA will shut us down, if they find out. :frowning:

I was thinking of describing treatment patterns, not making recommendations.

We’d just say this is what has worked in the past (and past performance is
not a garuntee of future performance ;-)).

Nigam.

Maybe I"m missing a key thought here. We can describe patterns of care –
what providers do today. Are we trying to say anything about which of
those patterns are good? For anti-hyperlipidemic for example we know that
80% of the “observed” prescribing patterns are sub-optimal (start with a
drug that we are almost certain will never get the patient to goal for
example) so the risk of just describing what is done is that folks will
come back and show that the patterns conflict with clinical guidelines
etc. I"m not saying it isn’t useful to describe what is done at scale but
suggesting that we should be very clear bout what we think the information
is useful for.

This may well be a different barrier in industry than in the academic world, but I hope we could safely make a statement like “the average person who started X had a P chance moving on to Y within Z months; the average person who started A had Q chance of moving on to any other drug within Z months”. No disease management here, feel free do draw your own conclusion if P >> Q. If this meets the device threshold, the FDA may have to look at regulating PubMed. :smiley:

But I agree that we need to steer clear of framing anything as a suggestion.

Friends:

You are totally right. You can describe what’s going on. The only reason I brought up the device issue is the way ubble works: It asks you for a bunch of parameters, and then tells you something (in this case virtual age. I am 33!!! I feel good about myself. For all of you who think I am 33: I am older. :slight_smile: )

BTW: The FDA Warning Letter for 23andMe: http://www.fda.gov/iceci/enforcementactions/warningletters/2013/ucm376296.htm, who also claimed they just reiterated what’s already known about the risk of certain genetic markers. So, reporting on populations is kosher, telling individuals about their concrete risk based on the data is not: “… because of the potential health consequences that could result from false positive or false negative assessments for high-risk indications such as these”.

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