Crisis Standards of Care: preparing for the next pandemic

Transcript

• 00:00All right. So we're happening.
• 00:01Yes, we're happening.
• 00:02Fantastic. Well, welcome
• 00:04everybody. Thanks so much for coming.
• 00:06I'll speak just for a
• 00:07minute and then introduce
• 00:08our our guest for tonight.
• 00:10My name is Mark Mercury.
• 00:11I'm director of the Program
• 00:12for Biomedical Ethics here.
• 00:14And I'll start with
• 00:16a very brief story,
• 00:18very brief. So in
• 00:21March of 2020, when the pandemic was very,
• 00:24it seemed very suddenly upon us and
• 00:26we saw what had happened in Italy. We
• 00:28saw what was happening in New York.
• 00:30I reached out to the chief medical
• 00:31officer of the hospital and said,
• 00:33do we have a plan if we run
• 00:35out of stuff like ventilators?
• 00:37And he said, well, no, we don't, But
• 00:39there's some people who are working on it
• 00:41and I'd like you to be part of that.
• 00:42And I said sure.
• 00:43So he assembled and the the
• 00:46Ethics Committee leadership
• 00:46were here with us tonight,
• 00:48assembled a small group of
• 00:49folks who were then reporting
• 00:51to a large group of folks.
• 00:52And we very quickly,
• 00:55it felt very quickly,
• 00:55we tried to work up a price of
• 00:57standard of care, a triage plan.
• 00:59What are we going to do when there's
• 01:00two people who need a ventilator and
• 01:01we don't have only one ventilator?
• 01:03What exactly is the plan?
• 01:05And of course it wasn't just Yale.
• 01:06New Haven Hospital didn't
• 01:07have a specific plan.
• 01:08People all over the country
• 01:09were caught off guard,
• 01:11some more than others.
• 01:11We had no guidance specifically
• 01:13from the state.
• 01:14The health system need to put
• 01:16something together.
• 01:16And it was a very remarkable time for
• 01:18a lot of reasons. We had
• 01:21terrific leadership here in particular
• 01:22by Ben Tolch and who really organized
• 01:24our efforts here to come up with the
• 01:26crisis standards of care and many of
• 01:28the people who worked on those are here.
• 01:30But there was a lot of cooperation
• 01:31between people who were working on these
• 01:33things. And I'll tell you I was
• 01:34leading a double life at the time.
• 01:36I was chief of neonatology and
• 01:40and running this ethics program
• 01:41And so I was doing both.
• 01:43And one of the things that
• 01:45fascinated me is thankfully there
• 01:46was a clinical director for the
• 01:48newborn ICU and and an acting chief
• 01:50during that time as well because.
• 01:52But it seemed like the CDC every
• 01:545 minutes was coming out with new
• 01:57recommendations for what we're supposed
• 01:58to do which babies we isolate how.
• 02:00And thankfully the NICU was
• 02:01largely spared trouble from COVID.
• 02:03Every time you turn around CDC had new
• 02:06recommendations making everybody crazy.
• 02:07But the flip side of that when it came to
• 02:09the allocation of the scarce resources,
• 02:11when it came to crisis standards
• 02:13of care or triage plan.
• 02:15The federal government was quiet as
• 02:17a mouse and we were an occupancy.
• 02:19Where's the CDC on this one?
• 02:21And so we were doing our best.
• 02:22But what happened was there were others,
• 02:25some very smart people from all over
• 02:26the country and all over the world who
• 02:28were working on these same questions.
• 02:30And so we found each other online on Zoom,
• 02:33and we got help from each other a lot.
• 02:34And so it was during that time that
• 02:37I had reconnected with Will Parker,
• 02:40who I've known since he was
• 02:41a young medical student
• 02:43back in the day. Chicago and Will was
• 02:46helpful to me and we've kept in touch.
• 02:48And so I'm delighted that he's agreed to
• 02:50come here today because as you'll hear
• 02:51when I read his his CV,
• 02:53he's got some serious expertise that's
• 02:55going to help us because importantly,
• 02:58we got caught. We worked very hard,
• 03:00very fast to come up with some
• 03:01crisis standard of cares.
• 03:02And we built a plan.
• 03:04But I mean, the Ben who who leads
• 03:06the show would be the first to admit
• 03:08that our plan ain't perfect.
• 03:10We need this.
• 03:11This plan still needs work.
• 03:12So there's one approach which
• 03:14could be let's just wait
• 03:16until and the next pandemic is
• 03:17upon us and we're drowning to say,
• 03:19well, we should really try and
• 03:20figure out what we're going to do.
• 03:22Or maybe now between crises
• 03:24we can try and figure out what
• 03:26exactly the plan should be.
• 03:27So I would like for us and
• 03:29that's why those of you who
• 03:30have worked so hard on this,
• 03:31I would like for us to keep
• 03:32the conversation going and
• 03:34and and Mike, I appreciate you
• 03:35being here. You were so supportive
• 03:36during so much of this stuff.
• 03:39I know. So a lot of important people who are
• 03:41in that effort were here,
• 03:42and a lot of people who had nothing
• 03:43to do with that effort but may in
• 03:45fact be leaders for the next one.
• 03:47So pay attention and when you
• 03:49have a good idea, share it.
• 03:50So with that, we're going to talk
• 03:52about crisis standards of care,
• 03:53preparing for the next pandemic. Dr.
• 03:56Will Parker is an assistant professor
• 03:58of medicine and public Health Sciences
• 04:00and assistant director of the
• 04:01McLean Center for Clinical Medical
• 04:03Ethics at the University of Chicago.
• 04:06And by the way,
• 04:06I just have to say because
• 04:07I'm sweating in this thing,
• 04:09the mask is because I've been exposed,
• 04:12just found out, not because I'm sick.
• 04:14And so this with the CDC site
• 04:15assures us is the
• 04:17is the adequate precaution indoors.
• 04:19So I'll try not to get close
• 04:20to you, but that's what's
• 04:21going on. And I look around here, there's
• 04:22very few of us wearing the mask today.
• 04:24I think I look good in it. But, you know,
• 04:28so, so Will really brings all the skills
• 04:30to the place. He's a, he's a pulmonary
• 04:32critical care physician.
• 04:33He's a clinical medical ethicist.
• 04:35He's a health service researcher
• 04:36who studies the allocation
• 04:37of scarce medical resources.
• 04:40He's specifically interested in
• 04:41absolute scarcity problems where
• 04:43demand greatly exceeds supplies and
• 04:45algorithms triage patients for treatment.
• 04:48He runs an NIH and Greenwald Foundation
• 04:51funded quantitative bioethics lab.
• 04:53That's not nothing that applies advanced
• 04:55empirical methods to evaluate and
• 04:57design allocation systems according
• 05:00to the underlying ethical principles.
• 05:02This is his academic work.
• 05:04That sentence again,
• 05:06OK advanced empirical methods to evaluate
• 05:09and design allocation systems according
• 05:12to the underlying ethical Princess.
• 05:14Current lab projects include
• 05:16deceased donor organ allocations,
• 05:18policy life support,
• 05:20triage under crisis standards of care,
• 05:22and the allocation of novel
• 05:24scarce therapeutics.
• 05:25Will is a graduate from Williams College,
• 05:28and from then he's been
• 05:29at University of Chicago,
• 05:30where he got his MD,
• 05:31where he did his medicine residency
• 05:33and critical care fellowship,
• 05:35where he got a master's degree in public
• 05:37health, where he got a PhD in
• 05:39public health and completed a
• 05:41fellowship in medical ethics.
• 05:44So Will is the perfect guy to
• 05:46help guide this conversation.
• 05:47I'm so grateful you came
• 05:48all the way from Chicago.
• 05:49And with that, I introduced Dr.
• 05:51Will Parker.
• 05:58All right, you guys hear me.
• 06:00Thank you so much,
• 06:01Mark and program for bioethics for the
• 06:04invitation to give the seminars series
• 06:07and that really kind of reduction.
• 06:08I hope you guys can help me
• 06:10think about this problem,
• 06:11which I think is incredibly challenging
• 06:14and I've been fortunate not to get
• 06:16some support to to try and take it on.
• 06:18So hold on, just look in here,
• 06:21let's see if this works.
• 06:24You know, my,
• 06:25it's difficult to start these talks with.
• 06:27This is where I've gone and been,
• 06:29but I've basically been at UFC
• 06:30the whole time this month.
• 06:31OK, so it'll be very boring
• 06:33with this one new C slide,
• 06:35but there was a transformational
• 06:37experience that I had in medical school.
• 06:39I participated in the fellowship at Outreach
• 06:41in the study of professional ethics.
• 06:43This is the memorial for the murder of
• 06:45Jersey used in front of Europe in Berlin,
• 06:48where we're learning where the
• 06:49the current processes are.
• 06:50Learning about the role that
• 06:52the medical professionals,
• 06:53the medical profession at
• 06:54large played in the Holocaust,
• 06:56learning that they weren't just
• 06:58bystanders but in fact active perpetrators
• 07:00of key elements of the genocide.
• 07:02And this experience,
• 07:04as you might imagine,
• 07:05is not something that leaves you lightly,
• 07:08especially when you get to spend
• 07:10the week hanging out with this guy.
• 07:12This is what you look like then.
• 07:13So I think this is,
• 07:14this is how I remember you in my
• 07:16mind with a full beard and and
• 07:19of of full week of seminars and
• 07:22dedicated tutorial style ethics
• 07:24teaching which was really shaped
• 07:27the way I think about clinical
• 07:29medical ethics and bioethics overall.
• 07:31And so naturally,
• 07:32when I was asked to help draft
• 07:34a crisis standard here,
• 07:36a triad protocol just like Mark
• 07:38was saying for my hospital,
• 07:40I I emailed him and I was like,
• 07:42hey, I'm, I'm sure you Remember Me,
• 07:44but I've been looking at your the
• 07:47Your Yell protocol that you put online,
• 07:49and it's been a very helpful guide.
• 07:52And so I think this story just tells
• 07:56a little bit about where I where I
• 07:57come from and my perspective on all of this.
• 08:00And another amazing thing is that now
• 08:02one of my medical students went on
• 08:05Vasby this year, Mark Kevin Lazenby,
• 08:07who's worked in my lab,
• 08:09let's all come full circle.
• 08:12So with that hopefully Mike's anecdote aside,
• 08:16I just want to talk about my support
• 08:18and funding for this talk I have.
• 08:21I'm unfortunate that I have a KOA
• 08:23from NHLBI that is focused on
• 08:24the heart allocation problem.
• 08:26I'm not going to talk about directly today.
• 08:28And then also from the National
• 08:29Library of Medicine,
• 08:30the Green Wall Foundation that
• 08:32directly supports this work,
• 08:33but no other conflicts of interest.
• 08:36So what I hope to get through today
• 08:39and and open the questions and
• 08:40interruptions at any time is defined
• 08:42prices as the standards of care.
• 08:43What are we talking about?
• 08:44Right?
• 08:45Then go through didactically
• 08:46the ethical values for life
• 08:49support allocation in the crisis.
• 08:51Just make sure we're all on the same
• 08:53page from a normative perspective.
• 08:54And then finally there's four
• 08:56active bioethical controversies
• 08:57and crisis standards of care.
• 08:59I hope that we can pause sort of after
• 09:01each one and have a little discussion,
• 09:03because they especially need
• 09:04help with like the third one.
• 09:06And so I'm looking to get as much out
• 09:09of this for as seminars as I can.
• 09:12All right, So what do we What
• 09:14is crisis standards of care?
• 09:15Bernie Lowe,
• 09:16who's one of the leaders in bioethics,
• 09:18is probably known the most in this room.
• 09:21I presented it this way at a talk to those
• 09:25very apartment and right to the point.
• 09:27This is Memorial Hospital in New Orleans.
• 09:30Several days after the levees
• 09:32break and Hurricane Katrina.
• 09:34You can see that the hospital
• 09:36is completely flooded and they
• 09:38were losing power completely,
• 09:39running out of most of the resources
• 09:41to provide life support and hospital.
• 09:43And what happened in Memorial Hospital
• 09:45is still contentious and debated.
• 09:47It's been made into a Netflix series,
• 09:49but it clearly is not in accordance
• 09:52with the principles of bioethics
• 09:54released at several different levels.
• 09:56And this event and the 1st H1A1 influenza
• 10:00pandemic spurred the Institute of
• 10:02Medicine that is now called the National
• 10:04Academy of Medicine at the time,
• 10:06to form a ad hoc committee and
• 10:08define crisis standard of care,
• 10:10which is a recognition that a disaster
• 10:12is making it so we can't give
• 10:14everyone the treatment they need.
• 10:16In particular,
• 10:17we can't give them life support
• 10:19even if they're in acute respiratory
• 10:22cardiac failure and needed to
• 10:24prevent them from dying.
• 10:26So truly a tragic and horrible circumstance.
• 10:30So how does one approach
• 10:32such a terrible problem?
• 10:34Either when you have an acute crisis
• 10:36names of care like Hurricane Katrina
• 10:38or a perhaps subacute one with a COVID
• 10:41pandemic surge where the patients,
• 10:43as those of us who worked in
• 10:44the ICU that time,
• 10:45seemed to keep coming faster and faster
• 10:48each day and the panic that we were
• 10:50going to run out of life support rose.
• 10:53You know,
• 10:54how do we approach the stereo problem?
• 10:56I think it's actually one of a
• 10:58set of problems as Mark moved
• 11:00to in his introduction,
• 11:01a set of problems where we've
• 11:03we've recognized the scarcity,
• 11:05we've recognized that the the treatments
• 11:07are incredibly important and valuable
• 11:09and life saving and a central authority.
• 11:11Maybe it's a health system like Yale.
• 11:14Maybe it's the entire United States
• 11:17government in deceased or organs has
• 11:20taken control of the resource and
• 11:23is algorithmically allocating it
• 11:25according to an explicit protocol.
• 11:27So there's something written down on
• 11:29paper which takes patients and puts
• 11:31them in a list and triages the treatment.
• 11:34So that's the central focus of my
• 11:36lab and I hope the parallel between
• 11:39the different clinical domains,
• 11:40what I think is the same bioethical
• 11:43challenge fundamentally is clear.
• 11:46So how do we, how do we,
• 11:49starting from the ethics,
• 11:50how do we approach this problem?
• 11:52How do we construct A protocol
• 11:55based on what ethical principles?
• 11:56Where, Where to begin?
• 11:59I think about this this way that
• 12:02several several of my mentors
• 12:04had written and described.
• 12:06Govind Prasad is sort of chief probably
• 12:08among them and I think laying out the
• 12:11space of reasonable ethical principles
• 12:13that should be considered when you're
• 12:15allocating scarce healthcare resources.
• 12:17I think this framework has also been
• 12:20adapted substantially by my mentor at
• 12:22the University of Chicago, Monica Peek.
• 12:24And of course Zeke Emanuel has been
• 12:26involved with this from the beginning.
• 12:28So what I'm going to do now is just go
• 12:31through these four sets of of values and
• 12:34and describe them in greater detail.
• 12:37So the first is that we should treat people
• 12:39equally coming from respects with persons,
• 12:41right?
• 12:42We don't have enough treatment to go around.
• 12:45Everybody's a human being.
• 12:45They all need it, right?
• 12:47They're all in the in the
• 12:49case of crisis and care,
• 12:51they need life support and they'll
• 12:53die of respiratory failure.
• 12:55So we should treat them equally.
• 12:56So a lottery would do that,
• 12:57right?
• 12:58You would just randomly assign the treatment
• 13:01and that sort of respects this principle.
• 13:05So that in here lotteries and
• 13:08actually in a protocol too.
• 13:10That's in contrast with the
• 13:11idea of first come first serve,
• 13:13which is that patients queue up
• 13:15for treatment and then they sort
• 13:17of survive for as long as they can
• 13:19on the wait list before they get
• 13:21they get treated and in practice
• 13:23first come first served.
• 13:25You know,
• 13:25while it might be a good way to
• 13:27allocate dinner reservations,
• 13:28so we can talk about that,
• 13:30I think it's a pretty bad way
• 13:32to allocate scarce health care
• 13:34resources specifically because the
• 13:35people who end up at the front of
• 13:38the line usually use their socio
• 13:40economic advantage to get there.
• 13:41And one of the I think greatest reversal
• 13:44of the structurally racist healthcare
• 13:46policy in recent history was the
• 13:502014 change the kidney allocation system,
• 13:52which which Romenka,
• 13:53who's here at Yale was very involved
• 13:56with this where pre dialysis waiting
• 13:57time started to be counted as points
• 14:00for patients in the king transplant list.
• 14:02So let's say you'd been listed
• 14:04at a transplant center and you'd
• 14:05waited for five years,
• 14:07then you would when you
• 14:08finally got on the list,
• 14:08you'd get five years of credit.
• 14:10Before that you'd start with 0.
• 14:12So it's a cue,
• 14:14but inherently unfair and skewed
• 14:16towards people who can list
• 14:18preemptively before their kidneys fail,
• 14:20who are predominantly privately
• 14:22insured and white.
• 14:24And so once they fix this,
• 14:26this huge racial disparity in kidney
• 14:29transplantation rates went away overnight.
• 14:32So this is an example of where and the
• 14:35idea of treating people equally but with
• 14:37a with a first come first served cue
• 14:40doesn't actually work out in practice.
• 14:43So that's the first set
• 14:45treating people equally.
• 14:46The next set of principles is
• 14:49maximizing total benefits, right?
• 14:51We have a,
• 14:51we have a security healthcare resource.
• 14:53We want to use it not just sort of
• 14:55randomly across the population.
• 14:57We want to use it to maximize the benefit,
• 14:59which can be formalized in a bunch
• 15:01of different ways and just listed to
• 15:03save lives and save life years. Here
• 15:07what you can imagine what interaction,
• 15:12excuse me, quality just these years.
• 15:18So in this example you would
• 15:20if you wanted to save lives,
• 15:22you clearly would allocate to the
• 15:24gentleman on the on the left here who
• 15:26has an 80% survivor of the discharge.
• 15:29But if you wanted to save life years,
• 15:33you also have to know how old the patient is.
• 15:35So here we have an 80 year old with an
• 15:3880% survival discharge and a 40 year
• 15:41old with a 40% survival discharge.
• 15:43In this situation,
• 15:45if your goal had to save life years,
• 15:47the total number of lives gained from
• 15:49the resource you would allocate to the
• 15:51second patient because their expected
• 15:52life years gained from treatment,
• 15:54in this case with mechanical
• 15:56ventilator for COVID-19 pneumonia,
• 15:57is 20 compared to 8 to the other patient.
• 16:01So already the utilitarian derived idea
• 16:04of maximizing total benefits has some
• 16:06problems here because we have to specify
• 16:09exactly what benefits we're after.
• 16:11Next is this concept that there's
• 16:13certain people who enter the
• 16:15allocation being worse off, right?
• 16:16They've been sort of screwed over by
• 16:19society or by their disease process,
• 16:21and we should account for that in
• 16:24the allocation protocol we developed.
• 16:26Now, one idea is the rule of rescue, right?
• 16:29You're going to treat the person
• 16:31who's the sickest 1st.
• 16:33And of course I think we can all
• 16:35imagine in a crisis standards
• 16:37and care scenario where basically
• 16:38everyone will die without treatment.
• 16:40If you treated the sickest people with the
• 16:42highest predicted probability of death,
• 16:44that would lead to enormously low benefits,
• 16:47right?
• 16:47So while sickest first is actually used
• 16:50in liver allocation like the melt score,
• 16:52that's only because those patients actually
• 16:54have high benefits from transplant.
• 16:56In a crisis standards and care scenario,
• 16:59sickest first would lead to the
• 17:01least optimal solution in terms of
• 17:02with respect to maximizing benefits.
• 17:04So that's in general is out.
• 17:07So what other classes of people
• 17:08are worse off?
• 17:09Well, the Youngs,
• 17:10if you develop end stage organ
• 17:12failure or achieve respiratory
• 17:13failure when you're young,
• 17:15a life threatening medical
• 17:16condition and you die young,
• 17:18then you haven't got to
• 17:19live your whole life right.
• 17:20You haven't got to play your
• 17:229 innings of baseball.
• 17:23This is the concept of Fair innings
• 17:25that every person is deserve some of
• 17:27the full life and we should allocate
• 17:30resources in order to ensure that it happens.
• 17:32So this is a more General Healthcare
• 17:35allocation argument than just
• 17:37the absolute scarcity problem we
• 17:39might articulated,
• 17:40probably perhaps passed by Norm,
• 17:42Norm Daniels,
• 17:43but applied here,
• 17:45this would end up with ideas
• 17:47like pediatric candidates for
• 17:49organ transplantation should be
• 17:51categorically prioritized over adults,
• 17:53which is actually the way we do.
• 17:54We do things right.
• 17:56But there's another group of
• 17:58patients who are worse off,
• 17:59and those are people who have been
• 18:01structurally disadvantaged by society
• 18:04and in structural laws and rules.
• 18:08I think.
• 18:08I don't know if many people are having
• 18:10any familiar area in Chicago here,
• 18:12but the map's pretty clear.
• 18:16All of the areas that are dark here,
• 18:20the highest are the highest
• 18:23percentage of African Americans
• 18:24or people identify who are black.
• 18:27And there are also areas that have been
• 18:30structurally disadvantaged by du jour,
• 18:33structural racist policies like,
• 18:35and I'm going to go into this more later
• 18:38in the talk, like detailed
• 18:41well in the color of the law,
• 18:43color of law or redlining specifically,
• 18:47and we'll talk about this more.
• 18:49But you can imagine if you're if you're
• 18:51living in one of these neighbourhoods
• 18:52and the pandemic is hitting you unequally
• 18:54because the city has been designed
• 18:56to make your neighbourhood worse off,
• 18:58should we account for that?
• 19:00So this is the concept of favouring
• 19:03the disadvantage somehow in in
• 19:05in your allocation protocol.
• 19:07And finally, the last category
• 19:09is rewarding social usefulness,
• 19:11which already kind of seems a little
• 19:13icky when you just say it right.
• 19:15But we actually,
• 19:16in order an allocation,
• 19:18use this principle pretty in
• 19:21a very concrete and big way.
• 19:24So if you are a living Kitty donor
• 19:26and your Kitty goes on to fail,
• 19:27you get 4 years of waiting time points.
• 19:30And the idea there is that you're
• 19:33getting paid back for being good
• 19:36in the past right Reciprocity
• 19:38for your previous good deeds.
• 19:39But the the other idea
• 19:44here is that there's some people who are
• 19:46like very valuable to society, right?
• 19:48They have a multiplier effect,
• 19:51like for example a famous CEO who is
• 19:53a job creator or something, right?
• 19:56And if we should give them the
• 19:58resource because then they'll keep
• 19:59them alive and help other people.
• 20:01That seems, I think, why I picked a CEO.
• 20:05That may not be the most popular
• 20:06on on purpose, but that reasoning
• 20:11actually overwhelms.
• 20:13The COVID-19 vaccine allocation
• 20:15aside from elderly patients and long
• 20:18term care facilities who went first?
• 20:21Us. I remember getting a second
• 20:24dose in mid January,
• 20:26well before weeks before any of my
• 20:29vulnerable patients and I realized that
• 20:31the weight on instrumental value and
• 20:34reciprocity was severely miscalibrated.
• 20:35But that's a different talk I see.
• 20:38So hopefully what's become obvious is
• 20:41I've laid these values and criticisms
• 20:43out is that they're inherently in
• 20:45conflict with each other, right?
• 20:47There's there's certain times
• 20:48where they go hand in hand,
• 20:50but if you're trying to
• 20:51maximize total benefits,
• 20:52you're by definition not
• 20:54treating people equally.
• 20:56There's no way around that.
• 20:57So how do you, how do you move forward?
• 21:00This is terrible.
• 21:02Well fortunately Gobin has thought about
• 21:04this a lot and he's a lawyer bioethicist,
• 21:07not at the University of Denver but has
• 21:09spent a lot of time at on the East Coast.
• 21:11And so some of you may have come across
• 21:14in different times is amazing thinker
• 21:16and you know his point is that you
• 21:19have to you can't some some may be
• 21:22better than others and there could be
• 21:24arguments based on more fundamental
• 21:26principles that make may help you
• 21:27rank order the four big categories
• 21:29but you can't ignore them all.
• 21:31You can't ignore ones and you have
• 21:33to do the hard bioethical work
• 21:35to combine them with the multi
• 21:37principle allocation systems.
• 21:38And I think that's very much true.
• 21:40And we'll see as we look at some
• 21:42examples of crisis standards of
• 21:43care and attempts to do just this,
• 21:45invoke multiple ethically relevant
• 21:48principles into a protocol.
• 21:51OK, I got through that quicker than I hoped,
• 21:53which is good because now now we
• 21:56get to the hard part, which is OK,
• 21:58what are the key bioethical controversies?
• 22:00And you know,
• 22:01where this is the bioethic seminar.
• 22:03So we're gonna focus on the
• 22:06life support triage protocols,
• 22:08a lot of hypothetical situations,
• 22:10and engage these deep,
• 22:12deep bioethical issues.
• 22:13I want to say there's entirely
• 22:15another set of equally important,
• 22:17maybe even more important,
• 22:19practical considerations during crisis,
• 22:21tangent care and procedural
• 22:22considerations about load sharing and
• 22:25how how would the triage team work,
• 22:27for example?
• 22:27I'm going to set those all aside so
• 22:29we can just kind of do more thought
• 22:31experiment stuff because that's
• 22:32where we're doing the bio or do it.
• 22:34We're bioethics tonight, right?
• 22:38So these are the four big problems
• 22:40and I'm hoping maybe we can just
• 22:42pause after each one for a brief
• 22:44round of discussion.
• 22:45We never end up getting whatever.
• 22:46I'm worrying when I get to the last one,
• 22:48and that's perhaps the most
• 22:49important that I think we need to
• 22:51resolve before the next pandemic.
• 22:53We need to improve crisis standards of
• 22:55care and deal with these four questions.
• 22:57So first let's take down sofa together, huh?
• 23:01I think we have a lot of friendly
• 23:04people in this room for this particular
• 23:07point so early in the pandemic.
• 23:09Gina Pistacello is now in rush.
• 23:10She's emerging leader in the serious
• 23:13illness conversation space or so.
• 23:16She's now at Pittsburgh, excuse me,
• 23:17since Pittsburgh, Pittsburgh.
• 23:18So watch out for what she's going to do
• 23:21next in terms of clinical medical ethics.
• 23:24She read every single state crisis
• 23:27standard of care protocol in like a week,
• 23:31accurately categorized them,
• 23:32convinced like three other people
• 23:34to check everything she did,
• 23:36and and published the My Eyes Cited
• 23:39paper ever the landmark survey of
• 23:43US ventilator allocation guidelines.
• 23:46And what we found is that everybody,
• 23:48for the most part, was using SOFA.
• 23:50And I'm a pulmonary critical care doctor,
• 23:52so I knew what SOFA was.
• 23:54And this is what, of course,
• 23:55we started to write into our algorithm too.
• 23:58And here's an example of the
• 24:01way SOFA was going to be used.
• 24:04This is from Pennsylvania.
• 24:05It's still on their website.
• 24:07A lot of these are still on the website,
• 24:09even though they've been.
• 24:10We, as we as all show,
• 24:11we've moved on in a big way
• 24:13for some of these ideas.
• 24:15But in order to save the most lives,
• 24:18remember that's the ethical principle.
• 24:20We're going to divide people up into
• 24:23categories based on their sofa score.
• 24:25And I'll explain what the Sofa score is.
• 24:26The next slide where if the sofa scores
• 24:29higher, then we're likely to die,
• 24:31right?
• 24:31So they get more points and it's like,
• 24:33oh, you want less points,
• 24:35lower score is better and people will
• 24:37be rank ordered by their scores.
• 24:39And one interesting thing that Mark and
• 24:41I were talking about on the way over
• 24:43here is by bidding sofa scores together,
• 24:45what you're doing is allowing tie
• 24:46Breakers to kind of kick in more, right?
• 24:48So all right, if you have the same points,
• 24:51two points, and and this primary calculation,
• 24:54then we start to do other considerations,
• 24:57life cycle considerations or
• 24:59fair endings considerations.
• 25:00But hopefully you guys can all
• 25:02appreciate how this is an attempt
• 25:04to take those ethical values and
• 25:06principles I discussed and force
• 25:08it into an actual protocol that
• 25:10could be used in in real life.
• 25:12So
• 25:15what I'm going to focus on is the sofa score.
• 25:18And the problem with the sofa score,
• 25:21the sequential organ failure
• 25:23assessment score is old.
• 25:24It's almost 30 years old now and
• 25:26it's based on expert opinion.
• 25:28So this table, which I see a lot
• 25:30of people squinting their eyes
• 25:31glazing over and I don't blame you,
• 25:33was made-up in the 90s at a
• 25:35critical care conference.
• 25:38It's not based on a regression model that
• 25:41this is to predict like the Apache 2 score,
• 25:44LEPS 2 score or LPS score.
• 25:47Both of those are predictive
• 25:49models designed to predict the
• 25:50outcome Survival ties to discharge,
• 25:52not so far, they just made it up.
• 25:54So it's actually kind of remarkable
• 25:55it predicts anything, right,
• 25:56because that means that means we must
• 25:58know what we're doing in stockers.
• 25:59So the the this first column is
• 26:02the degree of respiratory failure
• 26:04and the more the lower your PA,
• 26:06O2, FI, O2 ratio is the the work
• 26:10of hypoxic respiratory failure.
• 26:11So that's the first column In the
• 26:14in the third column here or the 4th
• 26:15column you'll see this cardiovascular
• 26:17column which is supposed to
• 26:18measure the severity of shock.
• 26:20And for those again in the critical
• 26:22care space or anybody who's really
• 26:23worked in a in a in a hospital,
• 26:25well, we don't use that much,
• 26:27don't need anymore for very good reasons
• 26:29and we have a lot of other vasoactive
• 26:31medicines that are not listed on that row.
• 26:33Speaking to that in practice people
• 26:35do not calculate this according to
• 26:37their original formula in any way.
• 26:40And but that being said about
• 26:42all those potential problems,
• 26:44it actually works pretty well
• 26:45for patients already in the ICU.
• 26:47If you make a couple corrections
• 26:49in that cardiovascular component,
• 26:51you calculate it.
• 26:52And if someone's been in the
• 26:53ICU for 48 hours and you have
• 26:56time to calculate all those,
• 26:57get all those laboratory measurements,
• 26:59calculate the score and take the
• 27:01maximum and worst value in all of them,
• 27:03it works pretty well.
• 27:05So this is the SOFA scores on the X axis.
• 27:07This is a large population of the
• 27:09patients with susceptive infection
• 27:10in Australia and New Zealand.
• 27:12ICU and the locality should have
• 27:14like logistic function right then.
• 27:16The higher sofa score,
• 27:18the more likelier to die,
• 27:19each one of these points turning into
• 27:22like a 5% or so increase in mortality.
• 27:26However, that's not the triage situation.
• 27:29That's the triage situation is
• 27:31that the patients in front of you,
• 27:33you have much you don't have
• 27:3548 hours of information of them
• 27:37already receiving life support.
• 27:38You have to decide whether or not to put
• 27:40them on life support in the 1st place.
• 27:41So
• 27:46when you actually evaluate it as a triage
• 27:48score, SOFA performs quite poorly.
• 27:50So this is the area of the receiver
• 27:53under the receiver operating curve
• 27:55or measure of discrimination.
• 27:56A coin flip is, you know,
• 27:58this has this dotted line here and
• 28:01as you can see sofa's not doing
• 28:03much better than flipping a coin.
• 28:04It's a near sort of lottery situation.
• 28:08And so this was a landmark paper that I
• 28:11think casts a lot of doubts about using
• 28:12SOFA in the crisis Standards of Care
• 28:14is that it doesn't work well in this,
• 28:16in the situation that people are applying.
• 28:19And on top of that,
• 28:22the SOFA score would exacerbate
• 28:24health inequity.
• 28:25It doesn't incorporate age,
• 28:26which we'll talk about next,
• 28:28but it also uses the patient's absolute
• 28:30value of creatinine to compute a renal score,
• 28:33right.
• 28:33So this is problematic for two reasons.
• 28:36One, some patients end up or show up to
• 28:38the hospital with chronic kidney disease,
• 28:40so they have higher creatinines at baseline,
• 28:42but it's not an acute problem and
• 28:45they might walk in the door with
• 28:47like two or three cell phone points
• 28:49just 'cause they have chronic kidney
• 28:51disease that's in no way correlated to
• 28:53their probability of actually dying.
• 28:55And
• 28:57the second problem is that certain
• 29:00populations with higher muscle mass,
• 29:02particularly those people who
• 29:03are self identified black,
• 29:05have higher creatinine bodies.
• 29:06This is this whole estimated GFR controversy,
• 29:09why race was used in the
• 29:11equation to begin with.
• 29:13And so the same patient with the same
• 29:15amount of renal function might get who's
• 29:18black might get two points compared
• 29:20to one for somebody who's white.
• 29:24So a lot of people have gone on
• 29:29to examine the potential bias of
• 29:32surface core against black patients.
• 29:34Most notable here at Yale,
• 29:38where I was very inspired by both
• 29:40of these papers to replicate your
• 29:42findings in the EICU data set.
• 29:44I don't know when they were polished,
• 29:45but you know I was very they're
• 29:47all about the same time.
• 29:48We all were thinking alike,
• 29:50and we all show that black patients
• 29:52would have higher SOPA scores than white
• 29:55patients with the same survival, right.
• 29:57So instead of giving because of
• 29:59that chronic kidney disease point
• 30:01or the OR the creatinine point,
• 30:03a white person will get a sofa of or be
• 30:05more likely to allocate a ventilator.
• 30:08Black person will get a SOFA score of five.
• 30:10So that's a a form of actual
• 30:12statistical bias, right,
• 30:14It's it's miscalibrated.
• 30:15So it was miscalibrated against
• 30:18patients who identified as black.
• 30:21And this is a big,
• 30:22this is a really nice figure from
• 30:24Deepishana's version of this paper,
• 30:28which was using pen and cosmic
• 30:30fermente data and they show that 10%
• 30:34of black patients would be assigned
• 30:36to inappropriate SOFA level, right.
• 30:38So it would effect on 10% of them
• 30:40and most of the city's patients
• 30:42would be shunted into these higher,
• 30:44higher groups.
• 30:45And we found the same thing that for
• 30:48conditional upon their assigned priority,
• 30:51black patients are much more
• 30:52likely to survive.
• 30:53So it's a little confusing,
• 30:55but basically the score is assigning
• 30:58a higher level of mortality risk to
• 31:01black patients than they actually have,
• 31:03which is a form of bias that leads to both.
• 31:05Obviously it's discriminatory
• 31:06and it's black people,
• 31:08but it's also inefficient because
• 31:11it's worse at identifying survivors.
• 31:14So the really we took a a
• 31:19population with COVID-19,
• 31:20a lot of the pre prior studies were
• 31:22pre you know like the pandemic was
• 31:24still going on so there wasn't
• 31:25a lot of COVID data.
• 31:26So this is the same sort of analysis,
• 31:29but in patients who had COVID-19
• 31:32required mechanical ventilator.
• 31:33We also added a little bit more,
• 31:35met the logic breaker here with
• 31:37a very simple simulation where
• 31:38we applied triage rules.
• 31:42And when we did that, unsurprisingly,
• 31:44we found that using a silicate
• 31:47tier system would systematically
• 31:50disadvantage individuals who identified
• 31:52as black without improving efficiency.
• 31:57In fact, it performed substantially
• 31:59worse than young is first or a
• 32:03combination model and not as you
• 32:06can see in the lottery system.
• 32:08Black and Hispanic people,
• 32:09although it's not significant actually
• 32:11have higher survival than white patients.
• 32:13And that's because white patients
• 32:15who end up in respiratory failure
• 32:17with COVID-19 throughout the
• 32:18pandemic tended to be much older,
• 32:20which will be the next topic
• 32:23of the discussion.
• 32:24And unfortunately because of some actions,
• 32:26misguided actions,
• 32:27I believe by, you know,
• 32:29office civil rights from the Department
• 32:30of Health and Human Services,
• 32:31SOFA is now even more dominant in
• 32:33crisis standards care protocols across
• 32:35the country than it used to be.
• 32:36This is a paper from May 2022
• 32:40and most states now have one.
• 32:42You know,
• 32:43remember our first map had a lot more
• 32:45holes because everyone was scrambling.
• 32:47Now it's still in some states had no plan.
• 32:49I don't know.
• 32:51And the plan is SOFA for the vast
• 32:54majority of these sofa in various forms
• 32:57with little other elements of the protocol.
• 33:00So I think this is really deeply
• 33:02problematic and one of the things is the
• 33:04main gap our grant is trying to fail.
• 33:08So in conclusion you know so I
• 33:11think I've said all this sofa's
• 33:13outdated it's not a triage score.
• 33:15It's less accurate than the Young's
• 33:17first and statistically diet but
• 33:19advised means black patients which
• 33:20makes it even more inaccurate.
• 33:22So I I think SOFA,
• 33:24you know should be eliminated in
• 33:26crisis and secure protocols across the
• 33:28country and replaced with a better
• 33:29triage score than one that we're working on.
• 33:34So that's the first problem.
• 33:37I obviously have awesome
• 33:39strong opinionated conclusions.
• 33:40I'm not asking for someone to defend.
• 33:42So Mark, I don't know if you want
• 33:44me to move on to age or if if
• 33:45you want to have any questions or
• 33:47feedback just about that this this.
• 33:49So let me, because I didn't,
• 33:51I I didn't do my job beautifully
• 33:54at the beginning,
• 33:55which was to remind you guys and
• 33:56let you know that we're going to go,
• 33:58we're going to go until 6:30 and then
• 34:01there's going to be a hard stop.
• 34:02So I apologize if there's something
• 34:04you really wanted to ask or say
• 34:05and you didn't get the chance. However,
• 34:08typically the speaker goes in total 5:50 or
• 34:115:00 to 6:00 and then we
• 34:12have questions, but the way
• 34:13Will's outlined this week and kind
• 34:15of stop at each of these important
• 34:16points and have a conversation.
• 34:18So I would say if someone wants
• 34:19to speak specifically to the
• 34:20sofa issue now we can do that.
• 34:22But I want to tell you one other thing,
• 34:24since I'm up here and have
• 34:26the podium, Karen Cold,
• 34:27who organizes these things so nicely,
• 34:29is herself out sick.
• 34:30So we wish Karen a speedy recovery.
• 34:33She reminds me to remind the people in
• 34:35Zoom land this number which Karen, please
• 34:37add it to the chat. Also
• 34:42203-442-9435, that's the
• 34:43number to get your CME credit.
• 34:472O3442, 9435 out of town, please call
• 34:49collect. No, that's not right. And
• 34:51the code is
• 34:56409-624-0962. So that's enough
• 34:58housekeeping. I think it's
• 34:59fine. Mark has something
• 35:00he wants to say about sofa.
• 35:01So why don't we spend like because
• 35:03I know, I know will you would
• 35:04want to get to the other problem.
• 35:05So let's do, let's spend 5 minutes
• 35:07talking about sofa and then move
• 35:08on to the next. Go ahead, Mark.
• 35:17Yeah, I I I should also say thanks
• 35:18so much Amir that especially for
• 35:20the folks who are on the Zoom call,
• 35:21Please wait till you get the microphone.
• 35:24I should have thought of that.
• 35:24Thank you Amir. So, so
• 35:28yeah, I I think we did a good job so far.
• 35:31So it can be replaced with
• 35:32something and I'm excited to hear
• 35:34about why you're working on.
• 35:36But pending that you know I, I,
• 35:39I in parts of some sessions where we
• 35:41talked about other severity illness
• 35:43sports and I I thought practically
• 35:45software was chosen but very simple.
• 35:47But even if you look at Apache
• 35:50two and talk to the developers,
• 35:52they said these were population
• 35:54statistics to sort of adjust in
• 35:56large clinical trials and things like
• 35:58that. They weren't really intended
• 36:00to be a bed side test. Yeah.
• 36:03The question about how an individual
• 36:05person was going to do So do we
• 36:07anticipate that there are any trials
• 36:08that would actually work well in an or,
• 36:12you know, measure that you can
• 36:14use for individual patients?
• 36:15That would be, yeah,
• 36:16I know the next, the next topic
• 36:18when we start talking about age,
• 36:19I think a score, you know,
• 36:21kind of giving away when we're talking
• 36:23about a score that includes age and
• 36:25several important clinical predictors.
• 36:26Like is the patient in shock,
• 36:27the degree of their hypoxia,
• 36:28respiratory failure in combination
• 36:30with perhaps having a four hour trial
• 36:34period on life support to collect
• 36:36more data that if we fit a score,
• 36:39a multigradable prediction model
• 36:40of that Haitian population.
• 36:42I think we can get something that's
• 36:44parsimonious that doesn't require a lot
• 36:46of heavy duty calculation trying to
• 36:48avoid sort of deep learning AI approaches,
• 36:50which I'm always very excited about.
• 36:52But it I think in practice like you said,
• 36:55SOFA was chosen because it's practical.
• 36:57We can sort of see how someone
• 36:59can calculate the bedside.
• 37:00Although if you ever look at those
• 37:02SOFA scores that are epic and then
• 37:04you look at the actual numbers,
• 37:06they're very discordant.
• 37:07So I think SOFA is actually
• 37:10fairly complicated to calculate.
• 37:13So you know personality is
• 37:15not a partners for.
• 37:16I mean the related thing is one of the things
• 37:20we working on this system is trajectory.
• 37:24You know,
• 37:25you you see somebody getting
• 37:26better and somebody getting worse.
• 37:27And that's.
• 37:28Yeah,
• 37:29no,
• 37:29that's why we really should try
• 37:30to get to the platform because
• 37:32then that's that's exactly right.
• 37:34I think one of a lot of the
• 37:36thought experiments around this,
• 37:38imagine a bunch of patients in
• 37:39a room with the one ventilator,
• 37:41and that's not the situation at all.
• 37:43You have is population of ICU patients.
• 37:45And once they're in the ICU,
• 37:47you can actually run much more
• 37:49complicated prediction models.
• 37:50You have a lot more information.
• 37:52You might be able to know very
• 37:54specifically what their survival's
• 37:55gonna be with a lot more certainty
• 37:57than that person who just showed up.
• 37:59Right.
• 38:01Good. Can't we have someone here?
• 38:02Fight for the sofa. Come on. Anybody.
• 38:05Nobody wants to do that. No expense, OK.
• 38:08The Ben's not going to have time.
• 38:10Yeah, it's gone.
• 38:10Move it on. Right.
• 38:12OK How about how old is somebody?
• 38:15Can we can we use, can we use that?
• 38:18Obviously a fair innings.
• 38:18A ******** fair innings.
• 38:20Prudential Lifespan Equity
• 38:21person would say yes.
• 38:23But we live in America,
• 38:26so it's a little bit more complicated.
• 38:29This was Utah's triage
• 38:31score before the pandemic.
• 38:32They actually were one of the rare
• 38:34states that had like something
• 38:35written down like New York did.
• 38:36New York was just all based on sofa.
• 38:39I don't know if everyone knows that story,
• 38:41but most studies never activating it.
• 38:42But
• 38:46Utah's career school score
• 38:47has estimated survival,
• 38:49so saving lives is protocolized,
• 38:52explicitly right?
• 38:533 bins and sort of equally as
• 38:56important as how old someone is.
• 38:58So are they. They're less than 30 years old.
• 39:01They get only one point.
• 39:02If they're over the over 60,
• 39:03they get three points.
• 39:05So being over 60 is the same as having
• 39:08less than a 10% chance of survival.
• 39:10So this is a very large, I would argue,
• 39:14fair innings weight in this protocol.
• 39:18Not that this was none of this is
• 39:21explicitly argued from bio in perspective.
• 39:23Like it just sort of somebody writes
• 39:25it down and then you can kind of
• 39:27see which is what I think is so
• 39:29interesting about quantitative biotics.
• 39:30But then this mid category is
• 39:32kind of problematic too, right?
• 39:34It's, it's an ASA score,
• 39:35so it's capturing the patient's
• 39:37chronic disease state,
• 39:38but it's a different access
• 39:40than estimated survival, right.
• 39:42So the idea is that people who are,
• 39:44the problem potentially with this
• 39:46is that people who have disease are
• 39:49somehow less deserving of the resource,
• 39:51right?
• 39:52That's what this is kind of implying,
• 39:55because if these factors matter
• 39:57for their Bible to discharge,
• 40:00they would be incorporated in
• 40:01this bottom column, right?
• 40:02And if these factors are
• 40:05about life expectancy, OK,
• 40:07And then you can sort of see how
• 40:08these would be combined together.
• 40:10It's still a fair innings argument,
• 40:11potentially.
• 40:11Not really, though,
• 40:12because what if you're a child?
• 40:14This is yours.
• 40:17This is a little muddled both
• 40:19bioethically and practically.
• 40:20And so protocols like this cause a
• 40:25lot of action over the summer after
• 40:28our initial waves by the Department
• 40:31of Health and Human Services Office
• 40:33of Civil Rights where they sort
• 40:35of went through all the CSCS and
• 40:37stripped out mention of age or
• 40:39disability in a primary score and
• 40:41even sometimes in the secondary score,
• 40:43a tiebreaker.
• 40:44So This is why that map is
• 40:47all sofa only sofa,
• 40:49because all considerations of age or
• 40:51disability were essentially removed.
• 40:53I think Doug White was able to keep
• 40:55like his tiebreaker in there somehow.
• 40:57But you know, in general,
• 40:59age was dramatically deprioritized
• 41:01from the OR removed from these
• 41:03protocols where using age to
• 41:05decide how you're going to triage
• 41:07was essentially from a regulation
• 41:10standpoint made impossible.
• 41:11So they did this in like 10
• 41:13different States and this is the
• 41:14type of language they would use,
• 41:16move on to life expectancy,
• 41:18categorical exclusion based on age,
• 41:20disability and functional impairment.
• 41:21There's a lot of concern in the
• 41:23disability community that there would
• 41:25be explicit discrimination against
• 41:26patients with chronic physical
• 41:29or neurological disabilities.
• 41:31Impairment and like would take
• 41:32ventilators away from people
• 41:33who are chronically ventilated,
• 41:35for example,
• 41:37and make sure that people with
• 41:39disabilities are valued based
• 41:40on their actual mortality risk,
• 41:42not the value of their life or their,
• 41:45you know, sort of qualities remaining.
• 41:46Right. And so apparently they changed.
• 41:50Utah, changed their plan.
• 41:52But when I clicked on the link,
• 41:53it's broken. I did a lot of
• 41:54searching last night. I'm like,
• 41:55oh, what did they change it to?
• 41:56But it's probably just this bottom,
• 42:01the bottom one. Now this is
• 42:02kind of like well payment,
• 42:04so we're going to have to
• 42:05really worry about it.
• 42:05But I assume state of Utah is
• 42:08just about estimated survival
• 42:10and throwing all these these
• 42:12other considerations out. So I
• 42:16want to talk about
• 42:16the two potential ethical justifications
• 42:18for using age, and this is a good
• 42:20time to have some discussion.
• 42:22The first idea is that the value
• 42:24of younger lives is higher.
• 42:26This of course has been sort of explicitly
• 42:29rejected by the previous administration's
• 42:31Health and Human Services department.
• 42:33But, you know, this is justified.
• 42:35And this fits into the idea that
• 42:37younger lives in general, not always,
• 42:39but have more like years to gain, right?
• 42:42If you're like like a previous example,
• 42:44if you're 40 years old,
• 42:45even if you have a higher
• 42:47probability of short term mortality,
• 42:49you're much more likely to gain
• 42:51more life years with treatment
• 42:53than some others in their 80s.
• 42:55And then the second idea,
• 42:56as we discussed 4,
• 42:57is that younger lives really are
• 42:59higher in terms of that they haven't
• 43:01got to play in their 90s at baseball.
• 43:04So we owe them because they're worse off.
• 43:08But there's another reason to
• 43:10use age in a triage war.
• 43:12And that age is a strong independent
• 43:14predictor of short term survival.
• 43:16Who was most likely to die
• 43:18from COVID the elderly?
• 43:19Who did we allocate COVID vaccines to?
• 43:221st the elderly?
• 43:25We used age because it was a
• 43:28tremendous predictor of benefit
• 43:31from COVID-19 vaccination.
• 43:33The converse is true here that
• 43:35younger patients are much more likely
• 43:38to benefit to survive from life
• 43:41support if they develop respiratory
• 43:43failure or chronic respiratory
• 43:45failure or chronic failure.
• 43:47So you need to use age if you
• 43:49want to save the most lives.
• 43:51We don't have an alternative number.
• 43:53That's the practical thing
• 43:54that we can do on the bedside.
• 43:56And this is some data that
• 43:58we have under review.
• 44:00We we presented ATS,
• 44:03the American Thrust Society
• 44:04conference last spring,
• 44:05so I'll walk you through it.
• 44:07The X axis is how old the person went and
• 44:10this is the population of like 90% COVID,
• 44:1210%.
• 44:13Others supposed to simulate a pandemic surge.
• 44:15And then the black bars are what
• 44:18percentage of them actually died.
• 44:19So as you can see yes,
• 44:21people get older.
• 44:22The probability of death goes up the
• 44:25the red bars are their predicted
• 44:27mortality by sofa score of all.
• 44:29And remember we've defined this as
• 44:31a crisis standard care population.
• 44:33So they're all quite they're pretty
• 44:34sick and they have higher sofa scores
• 44:36and it just all is pretty much the same.
• 44:39So the red bars are all the same.
• 44:41But if you make a new model that
• 44:43incorporates both sofa and age,
• 44:45you're much more accurate.
• 44:46You're actually predicting
• 44:47who's going to survive.
• 44:48And any critical care physician
• 44:49in the room would say,
• 44:50I'd much rather have a patient who's
• 44:5240 with a Silva of eight than an
• 44:5480 year old with a Silva of three.
• 44:56Right.
• 44:57That age tells you so much
• 45:00clinically about someone's ability
• 45:01to survive critical fullness.
• 45:04This is nothing new.
• 45:04It's why I'm trying to get this
• 45:06published cause the critical care
• 45:07journal's like this is obvious.
• 45:08This is why age is in Apache
• 45:10and all those
• 45:11other scores and then you know like what's
• 45:13all this ethics stuff in the discussion.
• 45:15But we'll we'll get there.
• 45:16We'll get there.
• 45:18Why are you talking about law?
• 45:20Like what's what is happening
• 45:21in this paper Bud?
• 45:23I think you know it sort of jumps off
• 45:25the the page to me that you know if
• 45:27your if your goal is to save the most lives,
• 45:30you have to use age,
• 45:31just like we use age to distribute vaccines.
• 45:34So I think there's a robust
• 45:37ethical justification.
• 45:38And even Dan Salmaisy who
• 45:40used to be in Chicago,
• 45:41who's really against fair
• 45:43innings and saving life years,
• 45:45concedes his first point,
• 45:46that using age as one,
• 45:47as one variable among many to save
• 45:49lives as a robust justification.
• 45:51If you remove age from
• 45:52life support allocation,
• 45:53I would say that's like anti young ages.
• 45:56I'm almost like you're penalizing,
• 45:58you're you're saying the lives of younger
• 46:00people are less valuable than older people.
• 46:02I would argue that's what our
• 46:04current trans protocols would do.
• 46:05And then finally, you know,
• 46:08all these ideas, fair things,
• 46:10parental lifespan,
• 46:10equity saving lives have broad appeal.
• 46:13And I would argue that CSCS ignoring
• 46:14these ideas are problematic.
• 46:16And the nice thing is if you
• 46:18just use it to save lives,
• 46:20you get, you know,
• 46:21kind of knock on benefits across
• 46:22these other principles, right?
• 46:23They tend to go together.
• 46:26So even though your objective with the
• 46:28protocol could be to save the most lives,
• 46:30there will be sort of secondary
• 46:32benefits for the other balance.
• 46:35So that's age.
• 46:37I'd like to hear people's thoughts
• 46:39and comments about using agency.
• 46:42S ES Ben. Oh yeah. Sorry.
• 46:45Wait probably wait for that.
• 46:48So, so I I
• 46:49strongly agree with the
• 46:52argument for using a based on
• 46:56predictive value when when we turned
• 46:59away from sofa aid was was definitely,
• 47:03you know the the comparator
• 47:05we were looking at was more
• 47:08accurate in in our community.
• 47:11The white patients were
• 47:14just fortunately older than
• 47:16David Doss. Yeah same thing.
• 47:18So it would have been you
• 47:20know the perspective of
• 47:22racial equity would have
• 47:23been better than sofa.
• 47:27Yeah and so it's and and
• 47:29also it was much easier.
• 47:31We didn't we couldn't put
• 47:33together triage things just
• 47:35from a feasibility perspective.
• 47:36Age would have been needed to be
• 47:39right and age is of course not chronological
• 47:41age is surrogate for biological age.
• 47:44There's like they're you know it's
• 47:46imperfect right. But it's something
• 47:47that's verifiable and easy. Yeah.
• 47:49I was hoping that age plus sofa
• 47:51score would debias it. It doesn't.
• 47:53You have to do something else.
• 47:54I'll show you later on we get there.
• 47:56But so there's still a sofa.
• 48:00Score's bias is so severe even if you
• 48:02account for the fact that black and
• 48:04Hispanic patients are younger and and
• 48:06in the in the predictive score you
• 48:07still have to over with the disparity.
• 48:09So that's but I think as we said
• 48:11the most logical thing to do
• 48:13is throw soap out completely.
• 48:15You can build a new score.
• 48:16We're trying out SEPA severity
• 48:18illness plus age 'cause we don't
• 48:20want to start with age that like
• 48:22trigger the anti ageist people like.
• 48:24So that's what we're starting.
• 48:28One other point,
• 48:30even with one national triage that I'm,
• 48:32I'm aware of that we've done
• 48:33recently with vaccines,
• 48:36age was universally accepted, right?
• 48:39It's bizarre to me that it was so
• 48:43widely accepted and uncontroversial
• 48:44in the allocation of vaccines
• 48:46that which has been so,
• 48:48so controversial in ICU allocation.
• 48:52Yeah, I think part of it is that
• 48:53if you don't allocate someone
• 48:54life support and needs it,
• 48:55they just will die immediately.
• 48:57Whereas young people, you know,
• 48:59most of them just were able to wait.
• 49:01You guys can wait and get their
• 49:02vaccine later on and they survive
• 49:04except for the ones who didn't, right.
• 49:05And and I think there were you
• 49:08know there was there was trade-offs
• 49:10there with that decision of 65 plus,
• 49:13right for for vaccines obviously I think
• 49:15they were justified because we saved
• 49:17a lot more lives by vaccinating the
• 49:19elderly people than people under 65.
• 49:20But make no mistake that was a
• 49:22choice and there were a lot of
• 49:24people who were sixty with diabetes
• 49:26would Incarnate settings who died of
• 49:28COVID and waited for their vaccine.
• 49:32So any other comments on age just
• 49:35just I I haven't not in the past I
• 49:38might not see you next could you just
• 49:40clarify for us now so so you you make
• 49:42a good argument for using age and
• 49:44I I I agree with that too but can
• 49:47you just clarify for us where the
• 49:49federal government stands on this now.
• 49:51Well, it's a new administration,
• 49:53presumably there's been some shake up
• 49:56this hasn't this actually never went
• 49:58to court And then Scoben's explained
• 49:59this to me like 5 times with the law
• 50:01of stuff and then it was screwed up.
• 50:02But it's never been litigated.
• 50:04So it's not like it's gone to court,
• 50:05federal court and they've said the
• 50:07using age in the CSC violates the age,
• 50:10just anti Age Discrimination Act
• 50:12of 1976 or whatever.
• 50:14And then also like from a
• 50:17constitutional perspective,
• 50:18age is not a protected class in the
• 50:20same way as race and ethnicity is.
• 50:22So a, a,
• 50:23a state could presumably pass a
• 50:25law that says we care about saving
• 50:28life years and that would hold up,
• 50:31although none of this he has like
• 50:32a huge law review article on that
• 50:34can't make sense on this. So.
• 50:35So yeah, that's where it is now.
• 50:37I I don't think.
• 50:38I think the first step from a
• 50:40research perspective and bioethical
• 50:42perspective is just to kind of like
• 50:45hammer this home in the literature,
• 50:46right, And just show like, hey,
• 50:48this is if you're making a triage
• 50:50for how old someone is,
• 50:51is critically important.
• 50:52And hopefully the weight of that evidence
• 50:56will effect policy down the line.
• 50:57But we're really far away from having
• 50:59sensible CSC policy these days.
• 51:01And just a quick note,
• 51:02because I think a lot of people
• 51:03in the room know,
• 51:03certainly other people who are
• 51:05working on our policy here
• 51:06because in the pediatric world,
• 51:07the sofa isn't really for the kids.
• 51:10So we used a different store called
• 51:11the PLA Two and for newborns
• 51:13there was nothing available.
• 51:14So we actually sort of jury rigged
• 51:16something for the purposes of our.
• 51:17Yeah, our here. Yeah, You know, they don't.
• 51:21So there we go first.
• 51:22Yeah, favouring the young person,
• 51:25the old becomes hugely important
• 51:27when for example here in Yale it's
• 51:29essentially the same ventilators that
• 51:30we use for the 80 year olds and two
• 51:32year olds and the 23 week preterm baby.
• 51:34Now that may be changed by the time the next.
• 51:36Sure. Yeah it's a little,
• 51:37I guess it's a little U-shaped
• 51:39in the sense that a 23 week old,
• 51:42you know may may have a you
• 51:44know 50% mortality or something.
• 51:45So that that that those types of
• 51:48considerations would happen and obviously
• 51:50with with COVID since infecting you
• 51:53know 99% adults then in terms of
• 51:56causing critical illness anyway,
• 51:58we kind of got a free pass on that
• 52:00but that's another issue with the
• 52:01age that we have to deal with right.
• 52:02Thanks. Here's another question.
• 52:06So I'm a first year my student never
• 52:08heard of sofa before this but I would just
• 52:10hopefully you'll never hear again.
• 52:11No, it's going to be around.
• 52:12It's been around for 30 years.
• 52:13People like people would go stick or sofa for
• 52:16This is why?
• 52:24Well this is that's a great question.
• 52:26This is a cohort defined
• 52:28as critically ill people.
• 52:30So everyone here needed a
• 52:32ventilator or needed basolactin
• 52:34medications to treat their shock.
• 52:36So the by construction
• 52:37this is a sick population,
• 52:39the population that you would
• 52:41be running crisis standard care
• 52:43protocol like you have to have,
• 52:44you have to have at least like
• 52:46a SOFA by construction of three
• 52:47or four if you think about the
• 52:50score in order to get in there.
• 52:51But yes, there isn't as much correlation
• 52:54between age and sofa as you'd expect.
• 52:56But remember this is part of the problem of
• 52:58it just it's before the life support starts.
• 53:02So you're just using like how how bad was
• 53:06there pulse oximetry to its own problems too,
• 53:10right before they started on the ventilator.
• 53:12And so everyone's was bad.
• 53:14The old people and young people
• 53:16are about the same right there.
• 53:18If you recalculated everything,
• 53:20couple days into the ICU stay age and
• 53:23silk would start to be more better
• 53:25correlating and like you would see this red,
• 53:27these red lines kind of
• 53:28**** a little like that.
• 53:29Does that make sense? Yeah.
• 53:31Thank you.
• 53:32All right,
• 53:32great question.
• 53:32So,
• 53:41so with age, the issue is so I
• 53:43work in the emergency department,
• 53:45they tell me about 30 patients of theirs
• 53:46and stuff like this is an 80 year old,
• 53:48blah blah. And I said stop.
• 53:49Is this an 80 year old who was playing
• 53:52golf earlier today or is this an 80 year
• 53:54old who scrolled up in a ball in the
• 53:55nursing home with three times of cancer?
• 53:57Because it's very different.
• 53:59But then immediately we're interested
• 54:00in ableism, you know, And so you know,
• 54:04in medicine we rarely think about
• 54:06age in any other situation.
• 54:08We're always talking about functional status.
• 54:09For 20 or 30 years,
• 54:11it's all about functional status.
• 54:13But then I just get, you know,
• 54:15twirled up and and and stuck.
• 54:17Yeah, age doesn't even want his own.
• 54:19But it it should matter.
• 54:21Yeah, I think it's obviously there's for
• 54:24any given numerical age someone is 80,
• 54:27let's say that that has there's a
• 54:30distribution of what that means
• 54:32for surviving critical illness.
• 54:34Some 80 year olds probably are
• 54:37actually like the average 70
• 54:39year old or 65 year old, right.
• 54:42But that being said,
• 54:44I think you using the average
• 54:46value for the average 80 year old,
• 54:48so including your two extreme examples
• 54:51in the middle is part of the triage
• 54:54score is ethically justified because
• 54:56our goal is to save the most lives.
• 54:58And you know if you look at
• 55:00the relationship between COVID
• 55:05anti pneumonia or critical illness
• 55:08in general and survival or mortality,
• 55:11it just stopped like that.
• 55:13So particularly after 80 is
• 55:16when things really skyrocket.
• 55:18But no, your point is well taken.
• 55:21Chronological age is an imperfect
• 55:23variable here but I would
• 55:25argue it's one complicated or
• 55:29question I have is you
• 55:30guys so much more did that
• 55:31was the type of the cycle plan
• 55:32as well as the subdivsines
• 55:35that's there. Yeah.
• 55:36The sofa score does not have any history.
• 55:39The the sofa score is just based
• 55:41on lab values and mild signs and
• 55:44medications that the patient's receiving.
• 55:46So the sofa score does not you know,
• 55:49which is nice.
• 55:50This is why he's appealing, right.
• 55:51It's like this kind of objective
• 55:54descrip description the patient's
• 55:56physiological state without any
• 55:58stigmatizing points for their past medical
• 56:00history or other medical conditions
• 56:05And So what
• 56:05if you are fit with other than that
• 56:08is is the fact that we do have other
• 56:10than that the which the substance use
• 56:13as well as many on the site that we're
• 56:17dealing with after that the COVID.
• 56:19So I always call people to play into
• 56:22evaluating persons coming into the
• 56:23hospital where they can save them
• 56:25because they're younger and they got it,
• 56:28it might go out and do something with the
• 56:31guidance, right.
• 56:32This gets into what benefit
• 56:33are you trying to maximize?
• 56:35Is there should you think about
• 56:37other things than just who's
• 56:39alive at the end of the day,
• 56:41I think it's really tricky when you
• 56:43start to do quality adjusted life years
• 56:45calculation and cost effectiveness.
• 56:46People love to do right,
• 56:48Discounting certain types of life,
• 56:51you know, how do you even assign that value
• 56:53if someone with substance use disorder,
• 56:56should they have like the
• 56:58priority lower about 20%?
• 56:59I don't think that that's hard to build
• 57:01that and justification to that nice thing
• 57:03about lives just sort of objectively,
• 57:06I mean treats back to
• 57:07treating people equally.
• 57:08Everyone's a person even if you
• 57:10have chronic medical conditions.
• 57:13One thing you mentioned
• 57:14right at the beginning of your
• 57:17talk and wondering if this might be
• 57:20accurate is is user regression model.
• 57:23Instead of having a triage store where
• 57:27you with a triage store you're making
• 57:30arbitrary decisions about what categories
• 57:33can predict mortality, and with a
• 57:38regression model you find out what.
• 57:41You find out what factors predict mortality.
• 57:44And it may be that in certain
• 57:48cases age is important.
• 57:50But you know, I guess is if
• 57:54somebody comes in short of breath,
• 57:57it's going to be way more important
• 57:59that they have in your renal failure
• 58:01than if they're 70 years old.
• 58:03I mean the the age is going to be,
• 58:07well, maybe,
• 58:08but that's what the regression will test.
• 58:10So that's exactly right and that's
• 58:11exactly the approach we're taking.
• 58:13We're developing a development data
• 58:14set where we're fitting a multi
• 58:16variable prediction model which will
• 58:17probably just be a simple regression.
• 58:19From that regression,
• 58:20we'll make a triage score.
• 58:23All it does is all you do is
• 58:24convert the predictions from the
• 58:26model to numbers that's what.
• 58:28And then propose that and the relative
• 58:31weight of age to an urog renal
• 58:34failure will be something I make up.
• 58:37It'll be based on the you know
• 58:39Cisco relationship between
• 58:40those variables and the outcome.
• 58:41So yeah thanks for that comment.
• 58:43That's that's perfect.
• 58:44That's the plan.
• 58:45And that makes this prevents us
• 58:47from being anti ageist, right.
• 58:48Because that's just what the,
• 58:51you know the the fiscal relationship
• 58:53between age and ICU survival,
• 58:55controlling a pot for all the
• 58:57other important medical variables
• 58:59that we can measure at the time.
• 59:00It's not age alone.
• 59:02So I I I want.
• 59:04I'm worried that in in my mind that
• 59:06maybe and some others it's easy to
• 59:08complete 22 important but separate issues.
• 59:11One is that the age is going
• 59:13to predict survival.
• 59:14But there's the separate,
• 59:15the fair.
• 59:16The fair eatings argument is really
• 59:18a separate discussion isn't it?
• 59:19It's not just about how age predictions,
• 59:21even if two individuals
• 59:23with the exact same likelihood of
• 59:25surviving COVID or whatever it is,
• 59:26one is 80 and one is 30, Those of us,
• 59:29and I'm with those who advocate
• 59:31for the fair eatings argument,
• 59:32we still say that then we should
• 59:34favour the 30 year old over the 80
• 59:36year old regardless of the predicted,
• 59:39the predicted mortality is the same.
• 59:42Yeah, I mean that's what I was trying to
• 59:45do with this slide is sort of separate
• 59:47those two out and prevent that conflation.
• 59:49You know, I think that for for
• 59:51those of us who are closet fairings,
• 59:53people like Mossad, we,
• 59:56I think we just make this argument right.
• 59:58The second one,
• 59:59the one that is much harder to
• 01:00:01push back against because we can
• 01:00:03forget we can fit regression models.
• 01:00:05We can.
• 01:00:06Isolate the independent prediction
• 01:00:08of a effective age,
• 01:00:10and I should have said controlling for
• 01:00:12all other measurable clinical variables
• 01:00:15that we can gather and just say all
• 01:00:18we're trying to do is save lives here.
• 01:00:20We love old people.
• 01:00:21Their value,
• 01:00:22the life of an old person and
• 01:00:24a young person is the same.
• 01:00:25We're not going to do fair ending stuff,
• 01:00:27and in practice,
• 01:00:28though you will have life years and fairness
• 01:00:31benefits when you put that in place,
• 01:00:33if that makes sense.
• 01:00:35Even though you're not building that explicit
• 01:00:38tiebreaker mechanism like you described,
• 01:00:41Mark into your score.
• 01:00:42At the end of the day,
• 01:00:43at the end of the simulation,
• 01:00:44you're going to save a ton more life
• 01:00:47years if you use age in this way.
• 01:00:49Does that make sense?
• 01:00:53I was wondering I guess like on
• 01:00:55a slightly different note in the
• 01:00:57context of the COVID vaccines,
• 01:00:59when you said that the elderly got confers
• 01:01:02because there was a stronger benefit, Yeah.
• 01:01:04To what extent is, I'm not familiar as
• 01:01:07familiar with a lot of these models.
• 01:01:09To what extent is therapeutic benefit
• 01:01:11included in these models or is that like a
• 01:01:13case specific thing or disease specific?
• 01:01:15Well, yeah, I mean the for for the vaccines,
• 01:01:18it's you basically say who's most likely
• 01:01:20to die from COVID, who's on vaccinate.
• 01:01:23It's the oldest people, right?
• 01:01:25So by protecting them with the vaccine,
• 01:01:28it's like the the exact opposite
• 01:01:30of this situation.
• 01:01:31Then you dramatically lower their
• 01:01:33risk of death from COVID and you
• 01:01:35save more lives here.
• 01:01:36Everyone who doesn't get treated
• 01:01:39with life support dies by definition
• 01:01:41because they're in respiratory failure,
• 01:01:43cardiac failure, right.
• 01:01:45And so then you need to identify
• 01:01:48the people most likely to survive
• 01:01:50to save the most lives.
• 01:01:52I should move on to the slack ones
• 01:01:54or if it gets harder, harder for me.
• 01:01:56But Mark, do you want to say something or.
• 01:01:58Yeah,
• 01:01:59so one thing that seems like there's
• 01:02:01like certain effort to remove
• 01:02:03judgment a lot of these metrics.
• 01:02:07So, so for example,
• 01:02:09we're taking something that's
• 01:02:11incontrovertible like how old somebody is.
• 01:02:13But,
• 01:02:13but as you sort of applied before,
• 01:02:15you know,
• 01:02:16you get some divisions in the room
• 01:02:18and they can probably
• 01:02:19predict a pretty algorithm too,
• 01:02:21like who's calling? And so I'm
• 01:02:24wondering, have you thought about using
• 01:02:25and they probably use the person's H or
• 01:02:28they're pure H but a lot of things too,
• 01:02:30like like Karen is standing out
• 01:02:32like frailty or *****. Yeah.
• 01:02:33So have you thought at all about
• 01:02:36it taking a Bayesian statistical
• 01:02:38approach where somebody says, well,
• 01:02:39I've got a pretest probability of XYZ
• 01:02:42and now, you know, like some data.
• 01:02:44So actually having the for your absolute
• 01:02:47position state their prior belief,
• 01:02:49I mean just to exhibit doesn't,
• 01:02:51because I didn't mean that that
• 01:02:53statistic doesn't really stand
• 01:02:54alone in the absence of other.
• 01:02:55That's cool. That's a really cool idea.
• 01:02:59I try to.
• 01:03:00I'm trying now to keep things simpler,
• 01:03:02but I really like that.
• 01:03:03So you would need of course
• 01:03:05a data set of predictions,
• 01:03:06which would be hard to obtain,
• 01:03:08of subjective predictions.
• 01:03:09You would need a data set perspectively
• 01:03:11collected of prediction from the ER,
• 01:03:14for example, before they debated some other
• 01:03:16like what's the probability of survival?
• 01:03:19That's cool.
• 01:03:19All right.
• 01:03:20So now this one is a really big problem,
• 01:03:23very perhaps the most contentious thing I
• 01:03:27think in in the current biological debate,
• 01:03:30and that's how to address
• 01:03:31structural inequity.
• 01:03:31I showed you this earlier,
• 01:03:32right?
• 01:03:32Where people died in Chicago
• 01:03:34was based on structural factors,
• 01:03:36based on a history of redlining.
• 01:03:39People with disadvantaged communities
• 01:03:40were much more likely to acquire COVID-19
• 01:03:43because of where they were living,
• 01:03:46because of where they had to work.
• 01:03:49They had to be.
• 01:03:50They're essential workers.
• 01:03:50They're out acquiring COVID-19
• 01:03:52living in congregate living settings.
• 01:03:55They didn't have the luxury of
• 01:03:56locking themselves in their
• 01:03:58room and zooming all the time.
• 01:03:59They had to be out of that in the world.
• 01:04:01And all of this is because the
• 01:04:03city is designed on purpose,
• 01:04:05or was designed on purpose,
• 01:04:06I should say,
• 01:04:07by the federal government to look like that,
• 01:04:09right?
• 01:04:09That's what redlining is,
• 01:04:11a systematic investment disinvestment
• 01:04:14campaign that was explicitly racist.
• 01:04:16If you haven't read this Mapping
• 01:04:20Inequality website,
• 01:04:21I strongly encourage you to see it.
• 01:04:23I The words are repugnant,
• 01:04:26but it makes it quite clear that
• 01:04:28our cities were designed by the
• 01:04:30federal government to be racially
• 01:04:33segregated on purpose, right?
• 01:04:34And we have to deal with this
• 01:04:37in sort of everything we're
• 01:04:39addressing from clinical medical
• 01:04:41ethics and bioethics perspective.
• 01:04:43But the question is how to handle this,
• 01:04:47this history of structural racism,
• 01:04:50this history of disadvantaging
• 01:04:53certain populations on purpose when
• 01:04:55we're making a triage score for,
• 01:04:57like,
• 01:04:58crisis care.
• 01:04:58And what I'm gonna go through
• 01:05:01is 4 different ideas I have.
• 01:05:03Kind of taken from the machine
• 01:05:05learning literature actually,
• 01:05:07about different goals you could
• 01:05:08have when you're making a protocol,
• 01:05:11and I'll go through these one by one.
• 01:05:13The 1st is demographic parity,
• 01:05:16which is each member of any
• 01:05:19racial ethnic group has the same
• 01:05:21probability of receiving truth, right?
• 01:05:24Probably the only way you can do that
• 01:05:26in practice mathematically is a lottery,
• 01:05:28a random assignment.
• 01:05:29It turns out that works pretty well,
• 01:05:31right? Almost as well as using sofa in terms
• 01:05:34of saving lives because of sofa's bias.
• 01:05:37But if you it, it's far from the optimal
• 01:05:39solution in terms of maximizing benefits.
• 01:05:42So a lottery while we achieve
• 01:05:45equal allocation does not
• 01:05:48respect maximizing benefits.
• 01:05:50So then the next idea is non discrimination.
• 01:05:53But make sure your SOFA is not biased
• 01:05:55against the racial and ethnic group.
• 01:05:56Be very sensitive that certain groups,
• 01:05:58particularly for black patients for example,
• 01:06:00have been structurally disadvantaged
• 01:06:02by our society and we have to be extra
• 01:06:05careful to not make things worse when
• 01:06:07we're allocating scarce resources.
• 01:06:10Right.
• 01:06:10And I hopefully have made the argument
• 01:06:13and convinced you that SOFA would violate
• 01:06:16this principle of non discrimination
• 01:06:18and it would exacerbate the disparities
• 01:06:21that we've already seen in the COVID-19
• 01:06:24pandemic if implemented to triage.
• 01:06:26So that's the the second principle,
• 01:06:28which is these are kind of in,
• 01:06:30you know,
• 01:06:31oriented in terms of more and
• 01:06:33more equity potentially.
• 01:06:35So how do you debias the score that's biased
• 01:06:38against a particular racial ethnic group?
• 01:06:40Well it turns out that using race
• 01:06:44ethnicity directly to fix SOFA
• 01:06:47like -1 if the person's black for
• 01:06:50example to sort of correct the
• 01:06:52bias I described earlier is very
• 01:06:54challenging for multiple dimensions.
• 01:06:57The state of Minnesota tried to do this.
• 01:06:59They they ran a regression model and
• 01:07:01they put all the clinical variables
• 01:07:04including H for probability of death
• 01:07:07from COVID-19 and and they also
• 01:07:09included a term for that was Bipoc.
• 01:07:12So basically non white.
• 01:07:13Anybody who identified as non white
• 01:07:15and that term statistically and
• 01:07:17independently predicted COVID-19
• 01:07:19mortality because it's capturing,
• 01:07:22even though it's a social construct,
• 01:07:23correlated with other unmeasured
• 01:07:26clinical variables.
• 01:07:27So they put that into their score.
• 01:07:29If you were, you're the same person,
• 01:07:31the same age, same medical comorbidities.
• 01:07:33If you identified as Bipoc,
• 01:07:35you'd be more likely to get monoclonal
• 01:07:37antibody treatment if you got COVID.
• 01:07:39This of course was grossly
• 01:07:41misinterpreted by certain people
• 01:07:43and manipulated for political gain.
• 01:07:46That's a completely erroneous statement.
• 01:07:49But this is the political challenge
• 01:07:50that we have to deal with these people.
• 01:07:52There are people like that in our
• 01:07:54country that we have to handle.
• 01:07:56And also from a constitutional perspective,
• 01:07:59with the recent affirmative action
• 01:08:02decision explicitly using someone's race,
• 01:08:04it's like one of, you know the in
• 01:08:06in general to allocate anything,
• 01:08:08maybe run, it's a legal challenge.
• 01:08:10And finally,
• 01:08:11there's the practical one where,
• 01:08:13you know, if it's like you're
• 01:08:15trying to give ventilators,
• 01:08:16and if somebody who looks to your
• 01:08:18eye that you would racialize them,
• 01:08:21as White says,
• 01:08:22oh,
• 01:08:22I'm black and I know your score
• 01:08:24gives me higher priority,
• 01:08:25How do you handle that 'cause this
• 01:08:27is a life or death situation.
• 01:08:28And I think that practical issue
• 01:08:31of are you actually counting on
• 01:08:33triage teams to racialize people
• 01:08:35and to socially constructed groups,
• 01:08:37that seems very problematic.
• 01:08:40So how do we get it on 'cause we have
• 01:08:43to one the what?
• 01:08:45What people have done is
• 01:08:46just modify the sofa score.
• 01:08:48That's what state of Colorado's done.
• 01:08:50So get rid of the renal component.
• 01:08:52I think it's best to just throw
• 01:08:54it out all together and come up
• 01:08:56with a new score that perhaps much
• 01:08:58better captures acute renal failure.
• 01:09:00It's the extent that we can measure
• 01:09:02them in triage scenario compared
• 01:09:04to this score which rolls in acute
• 01:09:06and chronic renal failure together.
• 01:09:08But in the pulmonary data for the the grant,
• 01:09:11which I think I took out,
• 01:09:12'cause I have way too many slides already,
• 01:09:14we used area deprivation index,
• 01:09:16which I'll talk about in a second,
• 01:09:17where someone lives as a way to
• 01:09:20achieve the outcome that Minnesota was
• 01:09:22going for without explicitly using
• 01:09:25someone's racial or ethnic identity.
• 01:09:29So the next idea,
• 01:09:32aside from non discrimination,
• 01:09:34is to actually look at that map and say like,
• 01:09:37can we even the playing field here,
• 01:09:40right? Can we spread?
• 01:09:41Can we mitigate the severe inequity of the
• 01:09:45pandemic by how we're allocating scarce
• 01:09:48life support treatments, and should we?
• 01:09:52There's tools, objective tools to do this.
• 01:09:54This is the area of deformation
• 01:09:57index as you see this map of Chicago.
• 01:09:59I don't know, I didn't explain that.
• 01:10:02This is where Druryville,
• 01:10:04it's like the wealthiest area
• 01:10:05city is right by Navy Pier.
• 01:10:07This is like a park like way very wealthy.
• 01:10:10Here's Hyde Park sort of an island that's
• 01:10:13where Chicago is wealth and privilege.
• 01:10:16And then here's the South and West side
• 01:10:18structured disadvantaged neighborhoods,
• 01:10:19right.
• 01:10:19So the homeowner,
• 01:10:20you can sort of see in that homeowner
• 01:10:23or the mapping inequality website
• 01:10:25how Hyde Park was constructed
• 01:10:27literally by the federal government
• 01:10:29to be to be blue on this map.
• 01:10:32And so you can,
• 01:10:33you can take someone's home address,
• 01:10:35map it to this area of information index.
• 01:10:38And what people like Doug White
• 01:10:39have suggested is that you literally
• 01:10:42would subtract points because
• 01:10:44they're coming from a structurally
• 01:10:47disadvantaged neighbourhood.
• 01:10:48And the idea is that we're trying
• 01:10:51to correct the structural inequity
• 01:10:53in the present day crisis.
• 01:10:55We recognize that things are way worse
• 01:10:57for certain communities than others.
• 01:10:59And we're taking one point off for that.
• 01:11:01And it turns out that there's an
• 01:11:04implicit ethical happening here,
• 01:11:05which is, you know,
• 01:11:06not really argued for in the paper.
• 01:11:08But correcting this,
• 01:11:09correcting that map,
• 01:11:11making it the spreading the burden
• 01:11:13of COVID around is about 1/4 of
• 01:11:15as important as saving most lives,
• 01:11:17which I think is interesting.
• 01:11:18This is an example where one of these
• 01:11:20protocols can reveal the underlying ethics.
• 01:11:22Here's the narrative description they
• 01:11:24use in the paper about how sofa based
• 01:11:27only system would prioritize this patient.
• 01:11:30The second patient will be prioritized
• 01:11:33in their novel system and they,
• 01:11:36you know,
• 01:11:36hand kudos to Doug White and and Pittsburgh.
• 01:11:40They actually did this when they were
• 01:11:42allocating their monoclonal antibodies.
• 01:11:43They got around that problem with
• 01:11:46you can't use race and ethnicity and
• 01:11:48they actually used where someone was,
• 01:11:52where someone lived.
• 01:11:53Calculate their ADI and give them
• 01:11:55twice the chance if they came
• 01:11:56from a high ADI neighborhood.
• 01:11:58And that led to higher rates of allocation
• 01:12:00than people who identified as black,
• 01:12:02which was their goal.
• 01:12:05So why?
• 01:12:07What are the potential
• 01:12:08criticisms of this approach?
• 01:12:09Well, you know,
• 01:12:11there's you're using these
• 01:12:12narrative descriptions.
• 01:12:13They didn't like the thesis.
• 01:12:14Hickett,
• 01:12:15Hickett handling 2 guys who
• 01:12:16were involved with the National
• 01:12:18Academy of Medicine and you know,
• 01:12:19defining what crisis standards of care were,
• 01:12:21they really didn't like the
• 01:12:22narrative description of the patient,
• 01:12:23right.
• 01:12:24You're making one patient really
• 01:12:25appealing based on like being
• 01:12:27a bus driver or whatever it was
• 01:12:29an essential worker and another
• 01:12:31person you're really painting as a
• 01:12:337 year old who's had been able
• 01:12:34to live their whole life.
• 01:12:35They're kind of like bleeding and
• 01:12:37fair innings there too. And of course,
• 01:12:40the triage team is not supposed to,
• 01:12:42you know, think about those
• 01:12:43social factors in triage, right.
• 01:12:45And that's what's the thrust
• 01:12:47of their main argument.
• 01:12:48They also talk about ADI not being granular
• 01:12:50enough to identify with disadvantaged.
• 01:12:52So one story about this is we very
• 01:12:54explicitly allocated our vaccine
• 01:12:56to our primary service area first,
• 01:12:58like around the University of Chicago.
• 01:12:59And so that meant our wealthier patients who
• 01:13:02live in the suburbs had to wait their turn.
• 01:13:05And that's not something
• 01:13:06they're used to doing.
• 01:13:07So once they found out the allocation system,
• 01:13:09they say, well,
• 01:13:10if I buy an apartment in Inglewood,
• 01:13:12which is one of the nearby
• 01:13:13disadvantaged neighbourhoods,
• 01:13:14can I get them by vaccine?
• 01:13:16So not a lot of them are really
• 01:13:19nice people who care about such.
• 01:13:22I don't describe all of
• 01:13:23our our patients that way,
• 01:13:24but you know of course the
• 01:13:26the bad apples and the ones,
• 01:13:27the emails that you remember
• 01:13:29and so we said no,
• 01:13:31you have to just stay in your
• 01:13:33stay in your house for one more
• 01:13:35week and you'll get it.
• 01:13:36So you know but I think in in practice
• 01:13:38aside from those extreme examples
• 01:13:40it would be it's very granular.
• 01:13:42This is a census block like you
• 01:13:44could look around you should play
• 01:13:45go on the website and look around
• 01:13:47and you can you know neighborhoods
• 01:13:48that you know are systematically
• 01:13:50worse off will be red on there.
• 01:13:52It's pretty good.
• 01:13:53And there's always this possibility of
• 01:13:56introducing social factors in triage
• 01:13:58of unintended consequences downstream
• 01:14:00the facts that you haven't anticipated.
• 01:14:03So these guys are OK with allocating
• 01:14:05vaccine and preventative medications
• 01:14:07based on error deprivation index or
• 01:14:10where someone lives as a way to address
• 01:14:12structural inequity but not life support.
• 01:14:14So here's what people think.
• 01:14:16And then finally,
• 01:14:17I think the the last idea,
• 01:14:19which is perhaps the most controversial
• 01:14:22and often is the criticism of efforts to
• 01:14:25correct the present day structural equity,
• 01:14:27is that you're really trying to correct,
• 01:14:29like, you know,
• 01:14:30hundreds of years of wrongs on
• 01:14:32a particular population.
• 01:14:34And is that really the best place to do that?
• 01:14:36And so that's the criticism
• 01:14:38of a reparations argument.
• 01:14:40But it's distinct from trying to make
• 01:14:44things more fair in the current crisis,
• 01:14:47if that makes sense.
• 01:14:49All right, So with that,
• 01:14:51I want to make sure we have some,
• 01:14:53some at least 10 minutes
• 01:14:55for discussion on this.
• 01:14:56Or maybe I can, I can just,
• 01:14:59why don't I just keep talking and we'll and
• 01:15:02we'll talk about the last two together.
• 01:15:04Because I always,
• 01:15:05never, never this one.
• 01:15:07And I think this is the perhaps
• 01:15:09the approximate.
• 01:15:11You know,
• 01:15:12the Bob Trude wrote this article
• 01:15:14in the Hastings report very early
• 01:15:16on the pandemic and pointed out
• 01:15:18that essentially all the thought
• 01:15:19experiments people
• 01:15:20were using were incorrect, right.
• 01:15:21The way a pandemic would work
• 01:15:23is that the ICU would fill up,
• 01:15:25then a new patient would show up,
• 01:15:27be in respiratory failure,
• 01:15:29and your decision would be to treat
• 01:15:31that person and withdraw life support
• 01:15:33from someone already receiving it.
• 01:15:35You very rarely would you
• 01:15:37have this three patients,
• 01:15:38one validator and you know,
• 01:15:41this is sort of an example, right?
• 01:15:44The one thing I don't think I wrote
• 01:15:46here is that this person who's in the
• 01:15:48ICU to sit his patient in the ICU,
• 01:15:50you would know with a great much higher
• 01:15:52degree of certainty that they're what
• 01:15:54their probability of survival is
• 01:15:55than this person who just showed up.
• 01:15:57You know, you don't know much about them,
• 01:16:00that's whether they're 5050, right?
• 01:16:02Whereas where you can have a lot more
• 01:16:05confidence but I think that confidence
• 01:16:07around their survival function is
• 01:16:09much smaller and this is way so.
• 01:16:12Despite these crisis standards of care
• 01:16:15being enormously long documents full
• 01:16:17of they're very hard to parse through.
• 01:16:19Almost none of them like really
• 01:16:20get into the weeds on this except
• 01:16:22for the New York plan which has an
• 01:16:24incredibly strict sofa based system.
• 01:16:26Like if your sofa doesn't go down,
• 01:16:29ventilator's out,
• 01:16:30so that's not been tested or validated.
• 01:16:33Whereas Maryland would have a very
• 01:16:35high barrier to withdraw off the
• 01:16:37the patient surrogates like said
• 01:16:39they don't withdraw life support,
• 01:16:40then they would have this chance to appeal,
• 01:16:43which of course would probably undermine
• 01:16:45any active reallocation in practice.
• 01:16:48So what we're doing in the grant
• 01:16:50is actually building a simulation
• 01:16:52model of sufficient complexity and
• 01:16:54depth to simulate what would happen.
• 01:16:57And one of my main hypotheses is
• 01:17:00that without some withdrawal rule,
• 01:17:02without some mechanism to remove
• 01:17:03life support and reallocate it
• 01:17:05to the waiting list,
• 01:17:05it's going to be first and first serve.
• 01:17:08So you can make this fancy triage
• 01:17:12store and it's not going to matter
• 01:17:14because it's just going to be who
• 01:17:16showed up first and then there's
• 01:17:18going to be very and with sort of
• 01:17:20randomness as people die if there's
• 01:17:22an available event when you arrive.
• 01:17:27All right. So with that,
• 01:17:31let's we can spend the rest
• 01:17:33of the time on discussion.
• 01:17:34These are my big conclusions.
• 01:17:36I think life support triage protocols
• 01:17:39across the US remain poorly defined.
• 01:17:42Well, the practical ethical perspective
• 01:17:44get rid of sofa triage scores,
• 01:17:46to use age, but only with the intention
• 01:17:48of saving more lives in the short term,
• 01:17:50just like we did for vaccines.
• 01:17:52Not not necessarily for any fair innings
• 01:17:54purpose and structural inequities
• 01:17:55need to be directly addressed,
• 01:17:57but exactly what the correction link
• 01:17:59should be needs to be determined
• 01:18:02and then withdraw of life support.
• 01:18:04Maybe the critical triage process
• 01:18:06should not be ignored.
• 01:18:07And before we go to questions,
• 01:18:08I just want to thank you to all
• 01:18:11my collaborators and mentors.
• 01:18:13You know,
• 01:18:13Govind's like this guy whose
• 01:18:14papers who've been reading forever
• 01:18:15and then he finally,
• 01:18:16he's a real person and will talk to you,
• 01:18:18which was like an incredible experience.
• 01:18:20And then Monica Pete,
• 01:18:22who's a HealthEquity scholar and my
• 01:18:25main mentor for all of this work.
• 01:18:27And Robert Gibbons is my PhD advisor
• 01:18:30and Elvis Long and a simulation
• 01:18:32model expert at the University
• 01:18:33of Chicago who's my KO8 mentor.
• 01:18:35So
• 01:18:40yeah, QR code is my, it's my Google
• 01:18:42stock page if it's not broken.
• 01:18:44So you can see some of the other
• 01:18:45things they've written and thank you.
• 01:18:46Let's let's talk for them.
• 01:18:51That was that was fantastic.
• 01:18:53I'm actually having my friend task
• 01:18:55trying to stay away a little bit.
• 01:18:58This was, this was really wonderful,
• 01:19:00you know, in terms of trying to deal
• 01:19:02with the issue of structural inequity,
• 01:19:04how to address them.
• 01:19:05I mean Mike, who's here,
• 01:19:07Mike and and you know Williams,
• 01:19:09they led the group that consisted
• 01:19:10of some of our folks who built our,
• 01:19:12our protocol as well as some members
• 01:19:14of the community all working together.
• 01:19:16Is it a fair, Stephen,
• 01:19:17Doctor Ivy that we never
• 01:19:19really cracked that nut?
• 01:19:21If you did,
• 01:19:22I would love to know what you decide.
• 01:19:30Thanks Martin. So so the health
• 01:19:32system was very concerned about the
• 01:19:35perception of the draft or public
• 01:19:38development community so it bans
• 01:19:41suggestion system and and members
• 01:19:43of the committee put together the
• 01:19:45transmitting we by members of the
• 01:19:48community like we intentionally reached
• 01:19:51out to people with local media the
• 01:19:55disabled community community staff
• 01:19:57took the New Haven but in British
• 01:20:00Portland you landed in Greenwich a
• 01:20:02number of ministers and and rabbis
• 01:20:04and you know so we really tried to
• 01:20:07intentionally reach a large number of
• 01:20:09people to break it wasn't that people
• 01:20:14but to explain what and it's not easy to
• 01:20:18explain necessarily what you're doing.
• 01:20:21To reunite people but
• 01:20:22it it seemed to go well.
• 01:20:24I I don't think we cracked the code
• 01:20:27of how to address Yeah I mean we had
• 01:20:29I had we had a similar experience
• 01:20:32presenting our trash for to our
• 01:20:35community Advisory Council for our
• 01:20:37hospital and what they were very
• 01:20:40forceful about is removing all the major.
• 01:20:42I didn't really go into this,
• 01:20:43but there were a lot of original
• 01:20:45plans that if you had major chronic
• 01:20:47conditions like you were on dialysis,
• 01:20:48that huge deprioritization and they're like,
• 01:20:51no, that's good, that's out.
• 01:20:52And so that was a very useful ex expe.
• 01:20:55Every time I presented them,
• 01:20:57I learned so much.
• 01:20:57I mean, I really do think that
• 01:20:59that should be part of what health
• 01:21:01system I think they should.
• 01:21:02But I do worry about you have these
• 01:21:04councils and groups and people leaders
• 01:21:06in the community that you collect.
• 01:21:08But it's somewhat arbitrary,
• 01:21:09like these are just people, you know,
• 01:21:11they're also usually people who
• 01:21:14are in social, socio,
• 01:21:15economic status positions
• 01:21:17that are pretty high, right.
• 01:21:19Like we have the guy who runs Howard
• 01:21:21Brown Clinic on the South side on ours.
• 01:21:23And yes,
• 01:21:24they may have the right race,
• 01:21:26ethnicity, diversity,
• 01:21:27make up that you want to
• 01:21:29represent the community,
• 01:21:30but do they really represent the socio
• 01:21:32economic spread or the community overall?
• 01:21:34You know,
• 01:21:35it's just like these groups you put together.
• 01:21:37So that's the that's the
• 01:21:38one problem with that.
• 01:21:38But I agree you for for thinking about
• 01:21:41ideas that you hadn't thought of.
• 01:21:43It's so helpful.
• 01:21:43I present all the time,
• 01:21:44although,
• 01:21:48yeah, so just a big comment if you would and
• 01:21:50then Ben will be the last common question.
• 01:21:52So it's up real quick and I'll move to Ben.
• 01:21:54Sure. It says run 4 minutes. I was 6 thirds.
• 01:21:57I think that's fast, but yeah.
• 01:21:59Yeah, please. Thank you.
• 01:22:01I I was just curious if you can describe the,
• 01:22:04the process that goes into choosing the
• 01:22:07data set used to build a regression model.
• 01:22:10Yeah, yeah. So I took all the
• 01:22:13clinical informatics slides out of
• 01:22:14here because it's an ethics talk.
• 01:22:16But we are constructing A collaborative
• 01:22:19networks from based on where my
• 01:22:21people are trained by one of my
• 01:22:23old mentors across the country,
• 01:22:24ICU doctors who like are data
• 01:22:27scientists too generally.
• 01:22:28And we're all clearing our data
• 01:22:29in the same format.
• 01:22:30So what will happen is we'll collect,
• 01:22:33we'll collect all all the observation
• 01:22:36electronic healthcare record that
• 01:22:38would be relevant for a critically
• 01:22:40I'll person and build a regression
• 01:22:42model based on the data from their
• 01:22:45like 42 hours before they start
• 01:22:47life support OR and then the first
• 01:22:50six hours afterwards with the idea
• 01:22:52that like the ER would have this
• 01:22:54temporary supply to stabilize patients.
• 01:22:56Because my hypothesis is that
• 01:22:58that would dramatically improve
• 01:22:59the accuracy of the triage car.
• 01:23:02But the nice thing is we can
• 01:23:02track both of those.
• 01:23:03And So what we're setting up with the
• 01:23:06collaborative network is like develop
• 01:23:07the data in the University of Chicago.
• 01:23:09Northwestern tested it.
• 01:23:10John Hopkins for example.
• 01:23:12We're about other collaborators
• 01:23:13and that adds a lot more.
• 01:23:15Whenever you make a model,
• 01:23:16you gotta keep your test data set separately.
• 01:23:20So that's the plan.
• 01:23:21Final question is Doctor Solch but you're
• 01:23:23just you know one with respect
• 01:23:26to the the community for the
• 01:23:28the night measures we actually
• 01:23:32sort of presented to them the
• 01:23:34possibility of using the area
• 01:23:35deprivation index as a modifier
• 01:23:37of. So that's where we were at the time
• 01:23:40and they we're we're not enthusiastic
• 01:23:43about that and and the more I thought
• 01:23:45about it the less enthusiastic I've
• 01:23:47I've become overtime you know I I
• 01:23:50I do I am concerned that that that
• 01:23:54bringing in you know non clinical
• 01:23:57factors really opens the triad
• 01:23:58vertical up to legitimate criticism
• 01:24:02and and also illegitimate criticism
• 01:24:04and and undermines the entire project
• 01:24:08baby out with the bathwater I guess right.
• 01:24:10You know, is the idea like sofa?
• 01:24:12Getting rid of sofa is sort of step
• 01:24:14one that will do most of the work.
• 01:24:15But if we try to do both at the same time,
• 01:24:18then you know, I I agree and I worry
• 01:24:25about just very arbitrary weights
• 01:24:27to like this mapping, right? Why?
• 01:24:30Where does that 4th come from?
• 01:24:32Why twice as many chances
• 01:24:34to get monoclonal antibody?
• 01:24:36Like I think that has to be
• 01:24:38really well justified.
• 01:24:38Harold Schmidt from Penn is thinking about,
• 01:24:41you know, you look at the map and
• 01:24:43see how the pandemic's hidden that
• 01:24:44the communities and then you design
• 01:24:46the weights proportional to that.
• 01:24:48So that's an idea.
• 01:24:49But I think the nice thing about
• 01:24:51having a simulation model is
• 01:24:52you can just try like
• 01:24:56see what's ethical after you look
• 01:24:57at your results. That's not the
• 01:24:59way you're supposed to do it.
• 01:25:01No, no. We said that sort of where
• 01:25:03where we ended up in Omicron when when
• 01:25:05we actually had our our most severe
• 01:25:09shortages were allowing 2 positions. They
• 01:25:16have a a lower threshold to to to
• 01:25:22with Cold War withdrawal and was
• 01:25:26usually withdrawal interventions and
• 01:25:28and so that sort of incorporated
• 01:25:31something that that Mark mentioned
• 01:25:33you know allowing clinicians
• 01:25:36to to use their clinical judgement.
• 01:25:38And and also you know your your point
• 01:25:40that it's actually less about
• 01:25:43allocating 11 ventilator among
• 01:25:44three patients than having some
• 01:25:46kind of mechanism to to discontinue
• 01:25:50intervention where where seeing
• 01:25:52that they're not beneficial. Right.
• 01:25:54I I think if you don't have this then just
• 01:25:56for comfort that's but we'll have that's
• 01:25:58the nice thing about having this a mod
• 01:26:00you can actually test that hypothesis.
• 01:26:02So I completely agree. OK.
• 01:26:04Well, thank you so much, Will.
• 01:26:06And please
• 01:26:09please join me in thanking
• 01:26:10Will, first of all.
• 01:26:16But, but so let's you know to realize that,
• 01:26:18I mean this is the program for biomedical
• 01:26:20ethics and we need to approach this with
• 01:26:22some ethical principles in mind and we
• 01:26:23have to agree on those first. But to have
• 01:26:25somebody here who's got really the
• 01:26:27ethical expertise as well as the clinical
• 01:26:29expertise as well as the quantitative
• 01:26:31public health expertise in an individual
• 01:26:34and also give some marvelous presentations,
• 01:26:35this was a real treat.
• 01:26:36But thank you so much. I
• 01:26:37think this is going to be
• 01:26:39helpful. And I do hope to
• 01:26:40the ones who are leading the charge
• 01:26:41here and the ones who are going to
• 01:26:42lead the charge someday soon, I do hope
• 01:26:45this would keep this going.
• 01:26:47We'll keep this going.
• 01:26:48Thank you all very much. Good night.
• 01:26:56OK, good.