CII - Harlan Krumholz, MD, SM

Transcript

• 00:00So our next speaker is
• 00:01Doctor Harlan Kremoltz.
• 00:03He's a cardiologist here and Harold
• 00:06H Hines junior professor of Medicine.
• 00:09Doctor Kremoltz went to IS from Ohio.
• 00:12He went to Yale for undergrad
• 00:15HMS or Harvard Med School,
• 00:18and then went on to the School of
• 00:20Public Health at Harvard as well.
• 00:22He's a distinguished scientist of
• 00:24the American Heart Association and
• 00:26a member of the National Academy of
• 00:28Medicine and has served as a member of
• 00:30the advisory committee for for the NIH.
• 00:34So I'll give you the floor. Yeah.
• 00:45Thank you. And Nicole just told
• 00:46me I don't have to end it too.
• 00:48So I appreciate that.
• 00:50But maybe that's not merciful
• 00:52for the audience. I'm not sure.
• 00:55So I'm so happy to be here today.
• 00:56I'm so happy to be here at the inaugural
• 00:58of the Center for Infection and Immunity.
• 01:00I'm so privileged to work with Akiko Iwasaki,
• 01:03who is just an extraordinary individual and
• 01:06both as a scientist and as a collaborator.
• 01:09And I also want to call
• 01:11out Vernali Bhattachary,
• 01:12who has been just a key individual in
• 01:15anything that I've been able to do with
• 01:18the Iwasaki lab and and for whom I'm.
• 01:20I'm really grateful.
• 01:21And she embodies really everything
• 01:22that's good about science and
• 01:24collaboration and generosity.
• 01:26And it's really my privilege and
• 01:27honor to be able to work with her.
• 01:29And thank you,
• 01:30Bernali for for being in that position.
• 01:33I want to thank you for the swag.
• 01:34I think that's so cool.
• 01:35I have.
• 01:36This thing isn't like the Taylor
• 01:37Swift concert where you get to
• 01:39wear the the bracelets.
• 01:40So thank you.
• 01:40I'm going to wear that the rest of
• 01:42the day and to be very proud of that.
• 01:44The.
• 01:45So Bernali and I are kind of doing
• 01:47a little dance around the talk.
• 01:49So she's going to talk a little
• 01:50bit about some of the studies
• 01:52in the mechanics of the studies
• 01:53that we're doing together.
• 01:54And I changed my title because to
• 01:56yield to a little bit of what she's
• 01:58going to talk about it and I'm just
• 02:00going to talk about a collaboration on
• 02:02the way to impact and to share some
• 02:04thoughts with you a little bit about.
• 02:06So I also Lisa said this,
• 02:08I feel a little bit like a
• 02:10stranger in a strange land.
• 02:11This isn't a natural thing for me.
• 02:13I'm a cardiologist and also
• 02:15I do very applied research,
• 02:16very different than the typical things
• 02:18that are going on in the tack building.
• 02:20I have a a group where Center for
• 02:23Outcomes research and evaluation are.
• 02:26You may not be as familiar
• 02:27with what outcomes research is.
• 02:29So I just want to frame for a
• 02:31little bit what what this is.
• 02:32So outcomes research evaluates and
• 02:36optimize health outcomes for individual
• 02:38patients and for populations.
• 02:39It's not so much about a particular
• 02:42technique as it is a driven approach
• 02:43to say that we want our work to
• 02:45be consequential and to have
• 02:47tangible impact on people's lives.
• 02:49It emphasizes what's important to
• 02:51patients and people, the end result.
• 02:54So we're interested in surrogates
• 02:56and markers on the way to actually,
• 02:58what does it matter for each individual?
• 03:00We don't want to say we've
• 03:02improved your lab test.
• 03:02We don't want to say we've
• 03:04improved your profile.
• 03:04We want to know that you feel better.
• 03:06We want to know that your life is better.
• 03:07We don't want to know whether
• 03:09people are living longer and better.
• 03:10It's a it's a orientation we
• 03:12seek to understand mechanisms,
• 03:14identify targets, test strategies.
• 03:16That may sound familiar to you,
• 03:18but but it's on the way to actually
• 03:21knowing that people are better.
• 03:23It involves discovery,
• 03:24accountability and improvement,
• 03:25discovery of new approaches,
• 03:27accountability for what we're actually doing.
• 03:31You know, what is it that we can say
• 03:32the current level of performances,
• 03:33What is it that's actually happening?
• 03:36An improvement in an
• 03:37aspiration to do ever better.
• 03:39And we're very results oriented.
• 03:41I mean, in the end of the day,
• 03:43I want to know like what actually happened.
• 03:45And if I'm going to try to help Lisa,
• 03:47I don't want to tell her about
• 03:49papers published or grants obtained.
• 03:50I want to talk to her about we've
• 03:53actually in enhanced her ability to
• 03:55help people and not just her ability,
• 03:58but that people actually are better,
• 04:00people are better as a result.
• 04:03So some of the key questions that we address,
• 04:05we may ask how do we improve
• 04:07healthcare performance?
• 04:07You know,
• 04:08there's this large gap between what we
• 04:09actually know today and what's actually
• 04:11being delivered on the front lines and
• 04:13what's being achieved for patients.
• 04:14While Covid's a different thing
• 04:15because actually we know nothing
• 04:17about how to make people better.
• 04:18But there are lots of areas of
• 04:20medicine where we know things but
• 04:22but it's not being translated.
• 04:23We ask how can we identify target
• 04:25and address factors that can be
• 04:27transformative for health and
• 04:29and we're very much interested
• 04:30in multimodal data coming in.
• 04:32So biological data has great utility
• 04:34but but social context can actually
• 04:36have more powerful influences.
• 04:38You may understand them as
• 04:39epigenetic influences,
• 04:40but we understand them as,
• 04:42you know,
• 04:43where people live and the exposures
• 04:44that they have and the lives and
• 04:47the stressors that they experience
• 04:48can have profound impacts on on
• 04:51modifying the disease process that
• 04:52they're that they're encountering
• 04:54or facing or recovering from.
• 04:56How do we give more voice to patients?
• 04:58How do we put them in a more
• 04:59powerful position?
• 05:00How do we beat the information
• 05:01asymmetry that has been historic and
• 05:03hierarchical paternalistic profession
• 05:05that where people have tended to
• 05:07walk in the room and just tell
• 05:09people what to do in a modern era,
• 05:12we're going to break down those barriers
• 05:14and make sure that even people with
• 05:15various levels of health literacy
• 05:16can have a basic understanding of
• 05:18what the tradeoffs are between the
• 05:19options that are available to them.
• 05:21Based on how we stream forward.
• 05:22How do we best promote HealthEquity?
• 05:24We're doing a horrific job
• 05:25of this in this country.
• 05:26We have an unjust system.
• 05:28We have people who are disadvantaged
• 05:29merely by the color of their skin or
• 05:31the circumstances of their birth.
• 05:33And they're they're in a category
• 05:35where they are are are largely
• 05:37going to live shorter lives and
• 05:39and at with higher comorbidity
• 05:42and worse function as they age
• 05:43because of the station that they
• 05:45are in their lives having nothing
• 05:46to do with intrinsic biology and
• 05:48everything to do with the social
• 05:50context of their lives.
• 05:52And it's something that demands our
• 05:54attention and how can we improve
• 05:56the knowledge generation pipelines.
• 05:57Our research is slow and sluggish.
• 05:59It's expensive and it's often not
• 06:01responsive to the needs of what
• 06:03people are are asking us for.
• 06:05And so I'm all for the very basic science
• 06:07of unlocking secrets of the universe,
• 06:09helping us understand some of the basic
• 06:12beautiful ways in which biology unfolds.
• 06:14But actually I'm more focused on how
• 06:16do we make sure that that's in the
• 06:18service of actually promoting better,
• 06:20better life, better better humanity.
• 06:23You know what are we doing
• 06:25to actually improve things.
• 06:26So and I want to be able to do
• 06:28this quickly test things faster.
• 06:30You look at long COVID,
• 06:31the the knowledge gaps are so profound,
• 06:33part of what Bernalli will mention to us,
• 06:35we're trying to build platforms
• 06:36so that we can reload.
• 06:37Not we're not building bespoke
• 06:39research projects, not huntergatherer.
• 06:40How do we go out, have an idea,
• 06:43build something up only to break
• 06:44it down when we're done?
• 06:46How can we do industrial farming
• 06:47where we build it,
• 06:48we've got the platforms and we just
• 06:50can continue to ask new questions
• 06:52rapidly and efficiently and be
• 06:54able to keep cycling through.
• 06:56And people who are working with
• 06:57us know that they're going to
• 06:59be honored and respected.
• 07:00We're going to listen.
• 07:01We're going to try to make the
• 07:02studies in a way that delights them,
• 07:04that they feel that they would do
• 07:06it again because of the way that
• 07:08we interact with them and be able
• 07:10to create an entirely new approach
• 07:12from the very hierarchical where
• 07:13people are subjects.
• 07:14We don't use the word subjects anymore.
• 07:16We we think that that's entirely
• 07:17different construct king and a
• 07:19queen and a subjects and they just
• 07:20follow directions and what they
• 07:21don't do what you tell them they're
• 07:23lost the following I got and chase
• 07:24them and bring them back.
• 07:25They're in studies are supposed to
• 07:27help them and they they leave in
• 07:29droves because they're so alienated
• 07:30by the way in which we do research
• 07:33we we do it in ways and makes it
• 07:35difficult for them to to participate
• 07:36and and doesn't ennoble them in any
• 07:39way and and I participate in research
• 07:41that I always say I'm ashamed to say
• 07:43where I finished the studies and I
• 07:45didn't tell people what we found.
• 07:47I mean that's the ultimate disrespect.
• 07:49People are in these studies and they
• 07:51didn't we just told them it's over.
• 07:53But we we didn't even do them.
• 07:54The honor of saying by the way here's
• 07:56what your efforts helped us to learn.
• 07:59They they we were exploiting them.
• 08:01They were just working for us.
• 08:02They we were they we weren't bringing
• 08:05them in as partners and refused to
• 08:07participate in that kind of research anymore.
• 08:09And I was socialized about that.
• 08:12I mean that's how we were taught.
• 08:14So
• 08:16and I'm just saying ultimately you know
• 08:18it's about the people and and we have
• 08:20to approach our research with humility.
• 08:22You know, it's not like we're the
• 08:23smartest people in the room and
• 08:25and everyone just should listen to
• 08:26us until we say it's a matter of
• 08:28us having that humility about our
• 08:29ideas and wanting to test them,
• 08:31figuring out what we can do to help
• 08:32and knowing if we're successful.
• 08:34It's because we've worked synergistically
• 08:35and in a complementary way with those
• 08:38people who are experts in their own
• 08:40disease because they live it every day.
• 08:42And so we've got to be able to
• 08:45understand how we create that synergism.
• 08:47So you know it.
• 08:50I think a very fortuitous thing occurred
• 08:53when I met Akiko and it occurred to
• 08:55me that that maybe there was some
• 08:56opportunities for us to come together.
• 08:58And she's been such a gracious and
• 09:00generous collaborator who immediately
• 09:02embraced all of these ideas,
• 09:03moving from mouse models now to
• 09:05working with people and immediately
• 09:07wanting to to work in the way with
• 09:10people that would honor and respect.
• 09:11Of course that's part of her,
• 09:13the way she operates in every different
• 09:15direction of of her life and in her science.
• 09:17And and so we thought you know that
• 09:19this would be good to bring the
• 09:20groups together and work together.
• 09:22And in our approaches that were one team,
• 09:25even though we have different
• 09:26areas of expertise.
• 09:27Our goal is to make a difference
• 09:29the how matters the how means it.
• 09:32It's not like just get the results
• 09:34with and doesn't matter how people
• 09:37feel when they're participating
• 09:38or or or you know how we,
• 09:40you know what happens as a result
• 09:42collateral damage because you
• 09:44know we're just trying to pound on
• 09:45people to get get all the work done.
• 09:47It has to be in a way that we
• 09:49are even among the researchers,
• 09:51recognizing that everyone deserves
• 09:54to be respected,
• 09:55that people are working hard and how
• 09:57do we create the conditions where
• 09:59people can excel to the greatest
• 10:01extent while at the same time,
• 10:02you know,
• 10:03we need to make progress together so
• 10:05that the how we approach this is important.
• 10:08It's a sensibility within Akiko's lab.
• 10:10I've seen that from the very beginning
• 10:12bring together the best lab and applied
• 10:14science and and see through its see
• 10:16it through the application benefit.
• 10:18Often times the very strongest
• 10:20scientific groups aren't necessarily
• 10:21working with strongest clinical groups
• 10:23and the clinical groups are just
• 10:25like trying to get tests in the in
• 10:26the basic science groups you're just
• 10:28trying to get subjects participants.
• 10:30But you know when we're trying to do
• 10:32this in a way that's a true partnership.
• 10:35We're also trying to partner
• 10:37this this aspect of it.
• 10:39If I can figure out how to go
• 10:41forward that's a metaphor problem.
• 10:45So the, so we've launched 2 studies.
• 10:48Again, Bernali will go into more detail.
• 10:51I'm just going to mention one is in
• 10:52a digital observational study.
• 10:54So these are digital and decentralized
• 10:56and we call them democratized.
• 10:57Democratized in this context
• 10:59really means full access.
• 11:01We're we're trying to let people select
• 11:02themselves to be part of the studies.
• 11:04You may say,
• 11:05well doesn't bring very high selection.
• 11:06You know what happens normally
• 11:07in clinical practice?
• 11:08The doctor walks in and decides,
• 11:11Gee, are you.
• 11:11I wonder if you'd be a good person
• 11:12for statement and all the people
• 11:13who might be eligible for study it.
• 11:15They're in a busy day.
• 11:16You know,
• 11:17they may look at the person and say,
• 11:19you know, I don't know, do we have time?
• 11:20Is it going to be hard to
• 11:22explain to this person?
• 11:23I mean, that's why we get this selection.
• 11:24And who gets into studies.
• 11:26Maybe they look at people with
• 11:27lower health literacy and think
• 11:28this is going to be take too long.
• 11:30You know that they're it's
• 11:31not equipped for this.
• 11:32We're trying to figure out can
• 11:33we create the means by which
• 11:35we can reach out to people,
• 11:36make them aware of these kind of studies,
• 11:38make it easy for them to join,
• 11:39make it so they don't have
• 11:40to take time off work.
• 11:41If you're an hourly worker,
• 11:42taking any time off work to participate
• 11:44in study can be a great burden.
• 11:45Can we make it so that the people
• 11:47can join us and can we make it so we
• 11:49can ship drugs to people's houses?
• 11:50Can we make it so we can collect
• 11:52bloods at their homes?
• 11:53Can we make it so that people can
• 11:54do this so that we minimize the
• 11:57burden and enhance the experience?
• 11:59So we have the listen studies
• 12:01and observational study,
• 12:01the Paxil C trial as a as a phase
• 12:06two investigation on new drug
• 12:08randomized trial Paxil for 15 days.
• 12:10But we're also in the course of this
• 12:12trying to build and test new ways of
• 12:14doing this knowledge generation pipelines.
• 12:16If we've got large numbers of people
• 12:18with these conditions now we can
• 12:19quickly enroll them in trials that we
• 12:21can quickly get them into studies.
• 12:22They're they're eager and and it's
• 12:25a readiness cohort that's in.
• 12:28But it's important for us again
• 12:29not to be exploitive,
• 12:31but to be participatory in
• 12:32partnering in ways that they feel
• 12:34that they want to stay with us,
• 12:36they can leave at any time.
• 12:37And then how do we use all the digital
• 12:39strategies to move the data and collect it?
• 12:41Another thing that we've done together,
• 12:44which has been a remarkable
• 12:46experience for me,
• 12:48is actually give people in our studies
• 12:51direct access to the investigators.
• 12:53So this is like something
• 12:55people generally thought,
• 12:56well,
• 12:56that seems like a bad idea.
• 12:57Aren't you going to contaminate
• 12:58the study or what does this do?
• 12:59Well it turns out if you have a town
• 13:01hall where you invite people who
• 13:03are in your studies to come and and
• 13:05you just pick a time lots of people
• 13:07show up. We've had you know at
• 13:09a random time we pick because we
• 13:10we can't schedule with everyone.
• 13:12We have 2000 people in the listen study
• 13:15now and you know 10% of people show
• 13:18up and they we we're still working
• 13:22on this how to optimize this they
• 13:24love Akiko that there's no surprise
• 13:26it's like and it's it's thrilling
• 13:28for them to have an opportunity.
• 13:30We we're we're careful about things
• 13:32we can and can't say what we can and
• 13:35can't disclose about what's going on.
• 13:37But we're we're telling them as
• 13:39much as we can and and we're trying
• 13:41to listen to their suggestions and
• 13:42what are their concerns and and
• 13:44what are the questions and how can
• 13:45we be a resource to them.
• 13:46But it it ties us to them in ways
• 13:48that has never been possible before.
• 13:50And I've found one of the best
• 13:53experiences I've had my entire academic
• 13:55career is to attend these these.
• 13:58And you know there was one where Akiko
• 14:01really presented the entire time and the
• 14:03number of hearts and claps at the end.
• 14:05You know,
• 14:06I found it so touching really
• 14:08honestly that we were in the same
• 14:10virtual room with people who are in
• 14:12our studies and we were also able
• 14:14to express directly our appreciation
• 14:16for their involvement in the studies.
• 14:18It it was,
• 14:19I think it's an innovation that way.
• 14:21So some findings,
• 14:23this isn't meant to be anything
• 14:25more than just giving you a sense.
• 14:27So one thing is we use validated assays, so.
• 14:29So we're trying to triangulate different
• 14:31information, clinical information,
• 14:32testing information.
• 14:33Ultimately we're we're going to link
• 14:36to wearables so we can get information
• 14:38coming from sensors that people are wearing.
• 14:40So a lot of real world information but
• 14:42also patient reported outcome measures.
• 14:44So for people in the lab,
• 14:45you know you're thinking about
• 14:46assays all the time.
• 14:47They've got different
• 14:49characteristics and properties.
• 14:50You want them to be reproducible,
• 14:51you want them to be reflective of the
• 14:53whatever it is you think you're measuring.
• 14:55The analytic validity is important
• 14:57and you know in the in clinical
• 14:59research there are tools that are
• 15:01about patients reporting their
• 15:02experience that have undergone quite
• 15:04a lot of testing and validation.
• 15:06Their psychometric properties are
• 15:07quite strong that we think we can
• 15:10rely on them and we have a lot of
• 15:11reference populations to compare them to.
• 15:13This is just an example and you
• 15:15look at these and go like what?
• 15:17What's so special about this?
• 15:18In the past seven days I felt worthless
• 15:20about helpless, felt depressed.
• 15:21And you can put never, rarely,
• 15:22sometimes, often, always.
• 15:23But but these have been through so many
• 15:27rounds of testing for understandability,
• 15:29context, validity,
• 15:30a whole range of criteria to
• 15:34be able to determine that,
• 15:35yeah, we can use them.
• 15:35They produce results that that
• 15:38can compare across populations
• 15:39and have some meaning.
• 15:41And so you know,
• 15:42we that for example the Promise
• 15:4529 which we're using in
• 15:47the Paxil C trial can be put translated
• 15:49into a scale from zero to 100.
• 15:52And for example, the promise cut
• 15:54points of that can correlate,
• 15:56have some interpretability with regard
• 15:57to what the person's overall health is.
• 16:00But because there are a lot of
• 16:02specific questions you can dig
• 16:03into what's driving their results.
• 16:05What is it that led to the findings
• 16:07that that we have And it gives a,
• 16:09we can, we can do computational
• 16:12phenotyping on their clinical data.
• 16:14But we can also when we have a lot
• 16:15of data about their experience,
• 16:17their symptoms their their
• 16:21how their lives are led,
• 16:22then it also gives us a chance
• 16:24to to to phenotype based on that.
• 16:26And it can be as simple as this.
• 16:28I mean this is the EQ5D visual analog
• 16:31scale where again you think this is gosh,
• 16:34this is so simple why you know some
• 16:35of you must have done this overnight.
• 16:37But but you know this is a tool that
• 16:39has been tested in in millions of
• 16:42individuals in different kinds of
• 16:44populations for interpretability,
• 16:46meaning and so forth about just saying,
• 16:48you know we would like you to indicate
• 16:50on this scale how good or bad is
• 16:52your health today in your opinion.
• 16:54And you you say like the thing about
• 16:56it is when people report how they feel,
• 16:59they're intrinsically correct because
• 17:01that's that's their impression
• 17:03of how they feel that day.
• 17:05And of course lots of things can affect
• 17:07it but we're trying to get some sense
• 17:10of a draw a line you know across this
• 17:12scale that represents where you are.
• 17:14The zero is the worst imaginable
• 17:16health state and 100 is the
• 17:18best imaginable health state.
• 17:20And if you look like in surveys
• 17:22of the United States adults,
• 17:24of course it varies a little
• 17:26bit by age you may have this is
• 17:29showing you different scales,
• 17:30but the the the black line is the
• 17:35EQ online vast that would be like
• 17:37equivalent to what we're doing.
• 17:39And you can see that you know in
• 17:41the younger group it's about 80,
• 17:42it can dip down that there are
• 17:43different ones.
• 17:44Sometimes it suggests for the United
• 17:46States population maybe around 80
• 17:48overall and it can correlate to how
• 17:50people say excellent, very good,
• 17:52good, fair or poor.
• 17:53But you can see you're like 70 to 80
• 17:56EQ vast for for the general population,
• 17:58which by the way that's not
• 18:00the healthy population.
• 18:01That's just the general population all
• 18:03things considered including people
• 18:05who who have health issues going on.
• 18:08And then if you look at us,
• 18:11the people who are in the listen
• 18:13study who are reporting long COVID,
• 18:15these are the distributions.
• 18:16So you can see in the far left this
• 18:18is just the overall distribution with
• 18:20a line sort of going down at 50 and
• 18:22and and maybe if we had more people
• 18:24that would be fully a normal distribution.
• 18:26It's got a little bit of this notch.
• 18:27I think it you know maybe
• 18:29more people fills it in.
• 18:30I think it's probably normally distributed.
• 18:32But you know, we're we're down 50
• 18:34or less and a lot of people much
• 18:37lower that that's very poor health.
• 18:40It's it's fair or poor health.
• 18:42And we by the way,
• 18:43just compared men and women,
• 18:44young and old.
• 18:45We looked at different,
• 18:47different waves of the of the virus.
• 18:51We were able to look from people's reports
• 18:54of 25 most prevalent symptoms in our group.
• 18:56Others have reported this.
• 18:58This isn't necessarily a breakthrough stuff,
• 19:00but I'm just giving you an idea
• 19:02of the kind of data we have 99
• 19:04symptoms that are collected.
• 19:06These are the most common ones,
• 19:07but we're able to in pretty clear
• 19:10detail characterized these people's
• 19:11experience and begin to look at
• 19:13not just calling it long COVID
• 19:15and everybody's got everything,
• 19:17but that actually there are some
• 19:19specific clusters within this where
• 19:20there are not only do we think
• 19:22they're underlying mechanisms,
• 19:24long COVID may be different,
• 19:25but it's it's being reflected in
• 19:27different ways that that people
• 19:29are manifesting it.
• 19:30They're not all the same,
• 19:31but again,
• 19:32if you're practitioner right now
• 19:33you you you're just keep seeing
• 19:35people with lots of symptoms.
• 19:36We need tools that are helping to
• 19:38take an inventory of symptoms and then
• 19:40to help in multidimensional space,
• 19:42sort of locate you who's who are your
• 19:44neighbors versus some other neighbors
• 19:45who are you like We got to start
• 19:47building a taxonomy that helps us
• 19:49understand this with greater nuance
• 19:51than just calling everyone long COVID.
• 19:55This is the frequency of treatments
• 19:56tried among long COVID participants.
• 19:58And by the way it goes down to,
• 20:00I mean these are categories.
• 20:01But when you actually within each
• 20:03of these categories and these
• 20:04people are trying like I think
• 20:05the average number of people have
• 20:07tried like 88 different things.
• 20:08You know,
• 20:09it's like,
• 20:10so it's just a remarkable amount
• 20:12of and of 1 trying things without
• 20:15really any systematic collection of
• 20:17information about what it brings.
• 20:19But it shows you the level of desperation
• 20:22that exists within this this group,
• 20:25that that they're trying everything.
• 20:27Because they're the,
• 20:27you know,
• 20:28they they they feel that their
• 20:30current life is untenable and are so
• 20:33desperate to find relief that that
• 20:35they're willing to go after everything
• 20:36and anything comes up on Facebook.
• 20:38And and I've seen this stuff
• 20:40like gambling block stuff.
• 20:41I haven't don't know what
• 20:42to make of it either.
• 20:43They had on the national news,
• 20:44one person went to Cleveland Clinic,
• 20:45had it done and then the
• 20:46patient goes like it,
• 20:47you know,
• 20:47it's like the people had water thrown
• 20:49on them and then they would stand
• 20:50up from their wheelchair in the in,
• 20:51you know, and they can walk again.
• 20:53You know it's like I don't know
• 20:56like maybe the water is magic.
• 20:58I don't know.
• 20:59But you know it's like I I I think we
• 21:03at in the Academy have an obligation
• 21:06to be able to help people
• 21:08understand what what happens and
• 21:09and what can make people better.
• 21:11I'm I'm happy if that person smelled
• 21:13the coffee but I do before I begin
• 21:15to start doing that on hundreds of
• 21:17thousands of people would like to
• 21:19have some basis to to to believe
• 21:21that it's it's actually working.
• 21:23So we you know we we we're looking at
• 21:26these in different ways this is symptom
• 21:28severity it's a different thing which
• 21:30is on in this case 100 unlike the vast
• 21:32score is like my symptoms are unbearable.
• 21:35So we're asking people on the worst days
• 21:37how bad are your symptoms and we're
• 21:40up around 80 or even higher for some people.
• 21:43I mean, people are saying not only,
• 21:45I mean these symptoms are coming and going,
• 21:47but so that's the other thing When
• 21:49you captured in any given moment,
• 21:50they could be feeling better in that moment.
• 21:52But if you ask them overall in the last
• 21:54two weeks how how bad is the worst day?
• 21:57They're saying it's horrific, right?
• 21:59So we have to be thinking,
• 22:00how do we capture this because it's
• 22:02about capturing periods of time.
• 22:03It's also about understanding
• 22:05how things track over time.
• 22:07We also ask a whole bunch
• 22:09of psychosocial questions,
• 22:10but this is just some for example,
• 22:12how many felt fearful and a lot
• 22:16of people feel that way and 16%
• 22:20that feel often feel anxious.
• 22:23A lot of people feel anxious, feel worried.
• 22:24I mean these people are are in
• 22:26terrible shape with regard to that.
• 22:27We asked about transportation challenges,
• 22:29we asked about insecurity about food,
• 22:31we asked about insecurity about housing.
• 22:33I mean these people in a very tenuous
• 22:35position with regard to their lives.
• 22:37Their incomes have often been been
• 22:39cut off because they're unable to
• 22:41work and they're they don't have
• 22:42a a big safety net behind them.
• 22:44And then a lot of them are socially
• 22:46isolated and depressed and these
• 22:48weren't preexisting conditions but
• 22:50these have come up as a result
• 22:52of what they're experiencing.
• 22:53We also are looking at people we're calling,
• 22:55I'm calling so far,
• 22:56I don't know if I'm still keep going
• 22:58on this yet post vaccination syndrome.
• 23:00But you know,
• 23:01the idea that there are some people
• 23:03who started developing a bunch of
• 23:06symptoms in in a period that was very
• 23:09short after they got their vaccination,
• 23:11maybe within six days after they got
• 23:13their vaccination that's had a long tail.
• 23:15Now people have said they've got a lot,
• 23:16they sounds like long COVID.
• 23:17It's true.
• 23:18They have a large number of
• 23:20different symptoms.
• 23:20But in some of the research we're doing,
• 23:23we can actually differentiate the
• 23:24pattern of the symptoms and people
• 23:26if if I just give you a bunch of
• 23:28people with a bunch of symptoms,
• 23:29then I say predict which ones have
• 23:31long COVID and which ones have
• 23:33this post vaccination syndrome.
• 23:34When you look at it, you might say,
• 23:36well,
• 23:36let's just look like a bunch
• 23:37of people got a bunch of symptoms.
• 23:38But if you actually do,
• 23:40you know, if you look at it
• 23:42mathematically and prediction wise,
• 23:43you actually can predict which ones of
• 23:45them have post vaccination syndrome,
• 23:46which ones have long COVID,
• 23:48which I think gives some credence
• 23:49to the fact that these may
• 23:51have some overlapping features,
• 23:52but they're actually distinctive.
• 23:53And I think we're going to be able
• 23:55to show for the first time this,
• 23:57this distinctive nature of it.
• 24:00And Andrew Wangston doing some
• 24:02I think very great work kind of
• 24:04working with these individuals
• 24:05that again just like if if people
• 24:07with long COVID get dismissed,
• 24:09these people get dismissed doubly because
• 24:11they fall into the political maelstrom.
• 24:13And and nobody wants to talk about
• 24:14it and I think it can it can be true
• 24:16that the vaccines were a miracle
• 24:18and saved millions of lives and
• 24:19that there were a number of people
• 24:21who were adversely affected.
• 24:22Both things can be true and if we're
• 24:24truly scientists we're not going to
• 24:26to shy away from investigation of
• 24:28this even though we know that we may
• 24:30put ourselves in a position where
• 24:31what we talk about maybe weaponized
• 24:33by others who have agendas that are
• 24:35different than ours but we we have
• 24:37to keep pushing forward with but
• 24:39what we think is the right thing
• 24:40to do and and and try to do this
• 24:42and again Kiko at every step has
• 24:44been I think so strong about this
• 24:46too and I'm so appreciate that.
• 24:48So the top lines of what we've
• 24:49done so far and you look at these
• 24:51groups highly symptomatic group
• 24:52that we've been able to assemble.
• 24:53I do think it's a subset of the
• 24:55people with long COVID but but this
• 24:56is the where we're going to find
• 24:58clues where we're going to find
• 24:59clues is where there's the most
• 25:02manifestation of what people have.
• 25:04Right.
• 25:04Let's start there.
• 25:05And so we're able to assemble I think
• 25:08large numbers of people who are highly
• 25:10symptomatic A diversity of symptom
• 25:11profiles that were beginning to be
• 25:13able to differentiate and characterize
• 25:15so that that that they're different.
• 25:18Tianna Joe,
• 25:19medical student has been doing some
• 25:20great work looking at people who are
• 25:22complaining of internal vibrations
• 25:24and and tremors and how they're
• 25:25different from people who don't
• 25:27have that as a prominent symptom.
• 25:28And and actually again you can
• 25:30based on the pattern of symptoms
• 25:32outside of that symptom you can
• 25:34differentiate them and and so we can,
• 25:36we can begin to start to understand
• 25:38these are clues we're on a search now.
• 25:40We're looking for any clues that
• 25:41help us begin to understand and
• 25:43more that we can look at this
• 25:44better off we are people have tried
• 25:46many treatments without relief.
• 25:48They have substantial psychosocial
• 25:50burden and extensive opportunities
• 25:51for their extensive opportunities
• 25:53for impact for all of us.
• 25:54And and I'll say one more thing that
• 25:56there are a lot of people in this group
• 25:57who were completely healthy before.
• 26:00So that's not just a group who was
• 26:03struggling with their health before.
• 26:05And that's again what makes me
• 26:06think there were marathon runners,
• 26:07there were people who are highly active.
• 26:09Now. It's not that we're just
• 26:11interested in those people,
• 26:12but again that those people may give us
• 26:14the opportunity to really look at clues
• 26:16because the where we find contrast,
• 26:18where we find things that that puzzle us,
• 26:21why did that happen then?
• 26:22That's where I think we can
• 26:24find rich opportunities.
• 26:25And our interest is looking at
• 26:26contrast by age, sex, race,
• 26:28ethnicity differences, timing differences.
• 26:30You know, when did when did it occur?
• 26:31How long did it last?
• 26:33What what, what did it change by waves,
• 26:35trajectory differences?
• 26:36Can we start to plot these out
• 26:38with latent class analyses and see,
• 26:40you know,
• 26:40this person is going up and down like this,
• 26:42this person's starting to get better.
• 26:44You know, what are the different?
• 26:45How do we begin to differentiate people
• 26:47based on trajectories, syndrome,
• 26:49contrast like what Tianna's doing with
• 26:51with the vibrations or we look pots Yes no,
• 26:53we look tinnitus.
• 26:54Yes no, Like.
• 26:55Are there clues here about people who
• 26:58primarily have one driving symptom?
• 27:00I think the the problem with the
• 27:01taxonomy that came up in JAMA that
• 27:03came out of the project recover
• 27:04was that they were wanted us to
• 27:06count symptoms and when you got
• 27:07to a certain number count,
• 27:07they were some of them were weighted,
• 27:09you got a score and they said bang,
• 27:10that's that's social.
• 27:11With long COVID,
• 27:12you know there's some people just
• 27:14have one symptom but it's intense
• 27:16and and I think it still can be post
• 27:18infectious in nature or post vaccination,
• 27:20computational,
• 27:20clinical and lab phenotype correlation.
• 27:22Of course, this is the Holy Grail
• 27:23of what we want to be able to do.
• 27:25Take all the information coming
• 27:26in clinically,
• 27:27take the information coming in from the
• 27:28lab and see where do we see the overlaps.
• 27:31I mean, where are the correlations,
• 27:32what can we learn together from this?
• 27:34I think ultimately we want to
• 27:36be doing taxonomy development,
• 27:38strategy testing and much more.
• 27:40I think the goals are,
• 27:41I think we understand it is what
• 27:43we want to do.
• 27:44We understand it,
• 27:45we can treat and mitigate or cure it
• 27:48and ultimately we can prevent it,
• 27:50that this is what our marching orders are.
• 27:52This is where we want to be in 10 years,
• 27:54five years, 6-6 months.
• 27:57If somebody in here is really smart,
• 28:00I'm looking for that person.
• 28:01Now, who's that going to be?
• 28:04Progress requires teamwork,
• 28:06trust and tenacity and the courage
• 28:08to believe that anything is possible
• 28:09If we work together,
• 28:10that that's the spirit that we're
• 28:12trying to bring to this effort.
• 28:14The goal is better outcomes.
• 28:16Let's work together to make it so.
• 28:17This is a poem.
• 28:19I want to say just that one member
• 28:21of Listen gave us which,
• 28:22which I'm not saying everyone's
• 28:24had this experience but
• 28:26but I'll let you read it. But
• 28:51I thought she was far too kind to us.
• 28:54But it did show me that maybe we're
• 28:56making some success in trying to build
• 28:58a study that people feel truly part of.
• 29:01And as Lisa was saying,
• 29:02what she's trying to clinic, listening,
• 29:04acknowledging and engaging and earnestly
• 29:07is the same thing that we're trying to
• 29:10do within the the research side as well.
• 29:13And and we have work to do
• 29:16this this isn't an end.
• 29:18This is just encouraging us
• 29:19to continue along with that.
• 29:21There's so many people to thank who've
• 29:23been part of this and I'm sure left it
• 29:25out because I did this really quickly.
• 29:27But it's been just a remarkable
• 29:28team effort and anyone who wants
• 29:30to join us is very welcome.
• 29:32Thank you.
• 29:54Okay, right. Thanks. Great talk.
• 29:58So I just have a question about the
• 30:01tax Levid long COVID, any potential,
• 30:06you know, predictions on when
• 30:07you might have some readouts.
• 30:08I think you might have said that
• 30:10it's a randomized phase two study.
• 30:12So I'm just curious about, you know,
• 30:14when you might anticipate some
• 30:16readouts for that for that study.
• 30:17So I'm hoping after the first of the year,
• 30:21you know it's actually actively
• 30:22enrolling anyone who's knows people
• 30:24who have long COVID who would
• 30:25like to participate in the trial.
• 30:26We'd love to include them.
• 30:28It it, it includes people who were
• 30:30had good or excellent health before
• 30:32and now have fair or poor health
• 30:35attributed to long COVID now.
• 30:37And I think one of the most
• 30:39interesting parts, it's decentralized.
• 30:40I like the the platform,
• 30:42but also that the fact that Akiko's
• 30:45lab is doing deep immunophenotyping
• 30:49before treatment and and then after
• 30:52treatment And because it may give us
• 30:55clues about who are responders and who fits,
• 30:57because if we do think that
• 30:58there are multiple mechanisms,
• 30:59we don't want to just say what
• 31:00was the average result.
• 31:01I was just pointing average result.
• 31:02But we don't want to actually
• 31:04dig in deeper to say, you know,
• 31:06even if the average result isn't there,
• 31:09maybe there were some people who
• 31:11did have remarkable improvement.
• 31:12And is there any clues to that
• 31:14within the the immuno phenotyping?
• 31:16So,
• 31:16but I'm hoping after the first of the year.
• 31:23Yeah, so I'm wondering about this question.
• 31:26So if you consider a plot with on Y axis
• 31:32health state and then there's
• 31:34somewhere threshold where we
• 31:36anyone below threshold is healthy,
• 31:38above threshold is not healthy and
• 31:40has some symptoms with some names.
• 31:42But below threshold the healthy range is
• 31:45not all the same though it varies a lot,
• 31:49some much closer to the
• 31:51threshold than others.
• 31:52And the question is and and for
• 31:55different reasons some somebody's
• 31:57kidneys at not that 100% but at
• 32:0019 somebody's liver and so forth.
• 32:03And then the question is when
• 32:06something like this infection or
• 32:09vaccine or or whatever challenge
• 32:12hits people who close up to
• 32:14thresholds as the ones that maybe
• 32:17will develop a particular illness.
• 32:19And in the case of long COVID,
• 32:24I wonder whether these the common symptoms
• 32:28that people develop have some relation
• 32:31to what where these people are on on
• 32:34their health status below threshold.
• 32:36They all were healthy before and
• 32:38then something happened and then
• 32:40this strange relations with very
• 32:42young athletic women suddenly
• 32:44developing something that way off.
• 32:47And the reason I'm thinking that
• 32:48is I got like terrible tinnitus
• 32:51I think after my booster shot,
• 32:54but I had very mild tonight as before.
• 32:56And that's what made me think that
• 32:58if you have some predisposition to
• 33:01something you just slightly off,
• 33:03then something hits some information
• 33:05or whatever and that's what you're
• 33:07going to develop.
• 33:08Will you be able to capture these
• 33:10types of relations in your,
• 33:12I mean do you you probably will
• 33:13have all the data to to find that
• 33:15if there is such a relation?
• 33:17I think it's a really good question
• 33:19and you know we have the chance
• 33:21to collect more data and be more
• 33:23specific about what we want.
• 33:24Obviously there's some recall
• 33:25bias that some people can have,
• 33:26but but to report whether you had
• 33:28prior tinnitus, most people should be
• 33:30able to tell us if it in that case.
• 33:34Yeah, I think I mean the beauty
• 33:37of this is it's an active live
• 33:39community that we're interacting with.
• 33:41So as we come up,
• 33:42it may be that we haven't collected
• 33:44enough information to be able to do that.
• 33:45But because you have that idea,
• 33:46we should say we we should go back
• 33:49and and collect that information.
• 33:51There's another feature here though,
• 33:52I think that's important that you just said,
• 33:54like I really don't like dichotomous
• 33:57variables.
• 33:57I I I really think that they are
• 33:59reductionists in nature and they
• 34:01obscure important relationships.
• 34:03So the degree to which we can
• 34:04collect spectrum, you know,
• 34:05not just saying did you have it
• 34:07before and someone says no because
• 34:08it was really just a little bit and
• 34:10it didn't didn't really bother them.
• 34:12You know, we want to say no,
• 34:13no from zero to 100.
• 34:140 means you absolutely had none.
• 34:17And then you know how how much
• 34:18did you have before?
• 34:19We need to start moving towards,
• 34:21I think, higher dimensional information.
• 34:23So that just like you said,
• 34:24it's not just saying, oh,
• 34:25you were healthy and not healthy,
• 34:27but like,
• 34:27how healthy were you and what
• 34:28and what did that mean before?
• 34:30And I totally agree with you.
• 34:31I do know that there's some people
• 34:33in here who had low levels,
• 34:34something that was totally
• 34:36amplified by and maybe, you know,
• 34:38you talk a lot of homeostasis.
• 34:39It's something was keeping things in check.
• 34:42And also by the way,
• 34:43their own perceptions of their body,
• 34:45how they react to their feelings
• 34:47within their body is changing too.
• 34:49But there's a whole science of
• 34:51sort of people's perception.
• 34:52Some people can really feel
• 34:53their heartbeats all the time.
• 34:54You know, it's like is it amplifying our
• 34:56sensitivity to things within our body?
• 34:58And then how do we that may be a
• 35:00whole different group that that's
• 35:01a whole thing for that we need
• 35:03to understand and not dismisses.
• 35:04That's all in your head,
• 35:05yet it's in your head, it's in your body too.
• 35:08We're going to take one more
• 35:09question and then we're
• 35:10going to go on break.
• 35:13Really. So fantastic. Thank you.
• 35:20But so I totally agree.
• 35:23This is a story that I hear so often
• 35:25in the patients who come to see me
• 35:27is that I had this thing and several
• 35:29people said this, it was annoying.
• 35:31I had COVID it kicked it up
• 35:33to being a very big problem.
• 35:35So I see that a lot.
• 35:37So I think that's an interesting thing and
• 35:39I'd love to see you start to measure that.
• 35:41My question has to do with the amount of
• 35:44suffering in that graph that you showed.
• 35:47Is there a way,
• 35:48or have you considered a way to find
• 35:51out how much the uncertainty about what
• 35:54they have and how long it's going to
• 35:56last and whether they'll ever get better,
• 35:59how much that plays a role?
• 36:00Because I think yes,
• 36:02people are physically suffering,
• 36:03but there's a lot of psychic suffering.
• 36:05I mean, we don't know what it
• 36:07is and we don't even have it.
• 36:08So is are you measuring that?
• 36:11Yeah. I I think that in some of this
• 36:13measurement of people feeling fearful
• 36:15and anxious and uncertain and losing
• 36:16hope and we we have some of those
• 36:18dimensions of course they there can
• 36:20be reverse causation and people
• 36:22who you know it's like is that a
• 36:24modifying factor or is it a consequence
• 36:26of what they're they're feeling.
• 36:28I think if we continue to
• 36:30follow people longitudinally,
• 36:31maybe we'll get a better sense that
• 36:33certainly the way the the reception
• 36:35people have had within the medical
• 36:36system has tend to be in be an
• 36:39exacerbating factor obviously with
• 36:41regard to their their desperation
• 36:43feeling that no one believes them
• 36:45or understands them or or wants to.
• 36:47You know, people are just lost patience.
• 36:48With me it's enough already like you know,
• 36:50it's like it's been enough time.
• 36:52You know, you should be getting better
• 36:53and people just don't feel better.
• 36:55But I think it's a really good point.
• 36:56Lisa, what's something we should
• 36:58be looking into?
• 36:59Thank you.
• 37:00I know we're a little bit.
• 37:01Thank
• 37:06you.
• 37:07So we'll we'll only break for 5
• 37:09minutes to set up for the next session,
• 37:11so grab some coffee every minute.
• 37:13It was worth it. Harlan.
• 37:14I'm glad. I'm glad.
• 37:15And the discussion and the questions?
• 37:17We need to get you all in the same room
• 37:19to discuss these topics at length.