Thank you so much for that kind introduction, I'm really flattered by it and I'm really flattered and really honored to be giving this Lewis lecture. I wish I could be there in person and I look forward to the time when we can all see each other in person again and put this all behind us. I appreciate everyone dialing in today. I'd like to talk to you a little bit about things that we've been involved with over the last decade or so and I've titled this talk improving the safety of ACS treatment from reducing bleeding to defining transfusion thresholds because I think the two issues are really interrelated and you may wonder, why is an interventional cardiologist interested in transfusion medicine? I'll take you through a little bit of the history of how I got involved but it's really exciting work that we and others around the country are doing to try and define exactly what the role of blood transfusion is in patients who are undergoing coronary intervention as well as those with acute coronary syndromes. I don't have any non-research relationships with industry. I have some funding to the institution for my role on a randomized clinical trial. Of course, I get a salary from the American Heart Association for being the editor-in-chief and I do want to thank Jeff Carson for letting me borrow some of his slides. This is Grand Rounds so I think it's important for us to start talking about cases because this is how we used to learn in medicine and I hope this is how we continue to learn. I'm going to present to you a 67-year-old farmer who presents to you with dyspnea and congestion. He has a history of various infections that he survived but his symptoms have worsened to the point of the fact that he's having stridor. His physician treats him with bloodletting and bleeds him about 11 units of blood thinking that the more he bleeds him, the better the patient will get and unfortunately the patient died soon after. Now, if you're a student of history like I am, you will recognize this clinical presentation as being George Washington and his personal physician was Benjamin Rush shown in this mural leaning over him. Benjamin Rush was one of the signers of the Declaration of Independence and a very big proponent of bloodletting as a treatment strategy. Now, if you train an interventional cardiology when I did in 2003-2004, which was the heyday of combination antithrombotic therapy and large bore access in the femoral artery, you would think that perhaps we were proponents of bloodletting. But I think we've gotten a little bit more refined about what bleeding means in this patient population and how we can potentially even change that. For those of you who are also students of history of medicine, you may recognize this person on the right hand side. This is James Blundell. James Blundell was an obstetrician gynecologist who in 1820 performed the very first successful human-to-human transfusion shown in the painting or the drawing to the left.  Now, remember at the time that he did this, the discovery of blood groups was several decades away. Landsteiner hadn't actually developed that classification scheme until the early 1900s. 1901, if I remember correctly. So this is a pretty risky thing to do. And we've had a tremendous amount of information about the role of blood transfusion and improving the safety of blood transfusion, except in one area. And that is really what is the role of blood transfusion and what is the optimal transfusion threshold in patients who have ischemic heart disease. And finally, let's talk about a little bit more of a contemporary patient. Here's a 75-year-old female with a history of hypertension type 2 diabetes, AFib, who presents with ACS. In fact, there may be patients in your emergency department right now who fit this profile. She's 59 kilograms. I don't know if that actually exists in Texas, but maybe it does. She's anemic with a hemoglobin of 8.5 and her creatinine is 1.6. Normal ejection fraction. And I think consistent with the guidelines, she's scheduled for invasive risk stratification. And as part of that decision-making process, the questions that we face as clinicians is, her hemoglobin is low. Should she be transfused? When she goes to the cath lab, what access site should be used? And if she undergoes PCI, what combination and duration of dual anti-clinotherapy should she receive? All of these decisions take into account the balance between worsening the ischemic risk and trying to balance that against reducing the bleeding risk. So I think this is not an unusual situation. As we all know, the ACS process begins with plaque rupture, which is shown for you on the right-hand side of the screen. And the patient in the cath lab, they undergo iatrogenic plaque rupture with deployment of the balloon. That leads to generation of thrombin, which leads to platelet activation and platelet aggregation. And it is our treatment for ACS that is directed at these various different elements. We have anti-thrombin therapy. We have inhibitors of platelet activation, both intravenous and oral. Certainly, we don't use a lot of platelet aggregation inhibitors anymore, like 2B3A inhibitors. And then, of course, the injured vessel wall needs to be treated either with stenting or with ultimately bypass surgery. When you couple those two things together, which is invasive procedures with potent anti-thrombotic therapy, that sets up the sort of perfect situation for a patient to have a bleeding event. And I think that what's happened over time is that the bleeding risk is really front-loaded in patients with acute coronary syndrome or those undergoing PCI. It's really the highest at the time the patient undergoes the procedure. They may get treated upstream with some anti-thrombotic therapy. But that arrow points to where the invasive procedure happens, which is where the bleeding risk is the highest. And then in the old days, in the bare metal stent era, that bleeding risk would dramatically fall once that 30-day point is reached because we were treating patients with dual anti-therapy for 30 days. So a lot of the focus has been on the inpatient setting and how we can reduce that risk for bleeding and potentially reduce the risk for transfusion in the inpatient setting. Only recently now have we taken it outside the hospital and I'll talk about that towards the end of the talk. If you look at the overall landscape of complications in patients undergoing PCI, and these are data from Fred Mosuti looking at the CATH PCI registry, the most common complication that happens in patients who undergo PCI, regardless of indication, and here I'm just showing you the STEMI and the non-STEMI, is bleeding. And in fact, if you look at what's happened with our current technologies, we've really been able to push the ischemic complications pretty low. The overall death rate from STEMI or non-STEMI is below 10% now. Shock, even though a lot of us are talking about shock, it's been one of the most popular things to think about, really shock is relatively rare when you compare it against other complications. Now, if those of you have been around for a while like I have, think about what these numbers mean in the context of the ISIS-2 trial and what 14-day mortality was in that step. And you can see that we've made a tremendous amount of progress with respect to ischemic complications, but bleeding is still something that we wrestle with in patients who come in. And we've also gotten a little bit more sophisticated about what that bleeding means. First, we've been able to show our group as well as others. These are data from the Washington Hospital Center that I'm showing you here, showing that it's the risk of bleeding or the incidence of bleeding is highly dependent on the patient's presentation. Patients who have stable angina or even unstable angina, really the vast majority of their bleeding complications can be related to the access side, but their overall risk of bleeding is relatively low. Think about the patient who comes in for a half with a positive stress test, for example. Now, if you take the patient and you increase their baseline level of risk for ischemic complications to the non-ST-segment elevation ACS, then their risk for bleeding goes up. If you take that to STEMI, it's even higher. And it's the patients who have shock who have the highest risk for bleeding. Now, that's probably because the patients with STS-segment elevation MI, for example, they're generally all undergoing invasive procedures and the patients with shock are undergoing invasive procedures plus large warframural access. So it may not necessarily be inherent to the ischemic risk of the patient as much as it is how we react to that ischemic risk and how we treat those patients. The other thing that we've learned about bleeding complications is that the proportion of bleeding that occurs related to the access side also changes with the clinical presentation of the patient. So while the patients with stable angina coming in for an outpatient cath and outpatient PCI have the lowest risk for bleeding, they have the highest proportion of that bleeding related to the vascular access site. When you go to the patient with the non-ST-segment elevation ACS, again, higher risk for bleeding compared with stable angina, but interestingly the proportion changes now. The vast majority of their bleeding is actually not related to the vascular access site, it's related to non-access site bleeding and mostly it's GI bleeding. And then if you change again to the STEMI population, the highest risk for bleeding, again, the vast majority of their bleeding risk is related to the vascular access site. Now the reason I bring this up is later on in the talk when we start talking about bleeding avoidance strategies, particularly as a related vascular access site, you're going to get the most bang for the buck in the patients who have vesti-segment elevation because they have on the highest risk for bleeding, so the most to gain. And number two, the vast majority of the bleeding events are related to the access site. So the access site strategy is going to have the most effect in these patients. And what I'll show you is that randomized trials have in fact shown that. Now we were among one of the first groups, certainly we were not the only ones to show that there was a strong association between bleeding complications that occur in the hospital and subsequent 30 day mortality as well as 30 day death or MI, as well as six month mortality. And even some groups have shown one year mortality and Steve Mnukin showed very nicely that there's an association between bleeding complications that occur in the hospital and subsequent stent thrombosis. We showed in this study looking at 26,000 patients that there's a gradation of risk, a dose response if you will. The worst, the bleeding complication, the higher the risk for bleeding or the higher the risk for death. Now, if you look at this red line, and I apologize for anyone who might be red, green color blind, but the red line, which is gusto severe bleeding, that's probably not surprising that it's going to have a very robust relationship with 30 day mortality. Remember, gusto severe bleeding isn't cranial hemorrhage or bleeding causing hypotension. What is surprising now is the fact that relatively lesser degrees of bleeding, so-called mild bleeding in this particular study also seem to be related to 30 day mortality. I want to think about what those mechanisms are. And you'll notice that I'm choosing my words very carefully here. I'm using the word association. Because again, outside of fatal bleeding or bleeding into your brain, this is probably an association and not directly causal. So as we start thinking about the future of antitrombotic therapy development, this is really the landscape that we're looking at. If you're worried just about the thrombosis and not about the bleeding, you're really just treating half of the patients. Now, as I mentioned, it's important to think about mechanisms of what this relationship is. Because if we understand the mechanisms, then we can start thinking about what the strategies are. So the first mechanism, of course, is confounding, right? None of these studies really randomized patients to bleeding versus no bleeding. Amazingly, there actually is a study that has done that. Many of you may know the feast trial. This is a trial of patients with symptomatic claudication where patients were randomized to serial phlebotomy over the course of six weeks compared with usual care. May not surprise you to learn that the serial phlebotomy strategy did not make a difference with respect to a mortality in that particular study. Now, I'm being a little bit facetious, of course. I mean, when patients bleed in the hospital, it's not a set amount of blood that they bleed. But confounding, I think, is something that we have to be aware of.  Severe bleeding we already talked about.  It's really the more mild or moderate bleeding that we're dealing with. And what is it that may underlie that mechanism? And I think it has to do with the fact that when a patient has a recurrent ischemic event, we intensify therapy. When a patient has a bleeding event, there's really not much we can do. We have to stop antithrombotic therapy. And that likely puts patients at risk for recurrent ischemic events and subsequent mortality. In fact, my colleague Tracy Wong has shown very nicely that patients who develop in-hospital bleeding, even if they survive that bleeding event and survive hospitalization at one year, they're less likely to be on evidence-based secondary prevention. So what can we do? Well, there are treatment strategies that we can use. Juditious use of blood transfusion. I'll talk a lot about that in this talk. Prevention is really important. Maybe we should just prevent bleeding to begin with. And the most potent way to do that, of course, is to make sure that these vascular access site strategies are evidence-based. So ultrasound, routine ultrasound for femoral access and of course an interest of mine, which is radial access, which probably is the most effective way to reduce bleeding risk in patients undergoing PCI. So this is really what I'm gonna focus on for the rest of the talk is blood transfusion and radial access. So let's talk about blood transfusion. I got interested in this a long time ago when I was a fellow. I was interested in, boy, it almost seems like a lifetime ago. Many of you may remember Medicare managed care and it was being rolled out. There was a lot of concern about the fact that it may cherry pick patients. So me and my research mentor at that time, Jamie Jollis, were trying to do a study of looking at Medicare managed care. And we looked at one year's worth of claims in community dwelling older patients who were Medicare beneficiaries. And what we found was that the number one procedure that they were being billed for was blood transfusion. It was more common than getting a head CT. So we were really amazed. And about that same time that we were looking at these data, the trick trial had come out. And those of you who are critical care trained may be familiar with the trick trial. That's the trial that showed that, you know, an aggressive transfusion strategy is no better than a conservative transfusion strategy and patients were critically ill.  It's a very old study by now, but I've not seen it repeated. Paul in Canada surveyed a series of different physicians, cardiologists, critical care specialists, surgeons and presented sort of contrived clinical scenarios. A patient who had trauma, patient with GI bleeding, a patient with sepsis and a patient with acute coronary syndrome and asked at what level of hemoglobin would you transfuse a patient presenting with these different clinical scenarios? And what he found was that the vast majority of respondents said that if a patient came in with ACS they would transfuse that patient if their hemoglobin was 10 or less. Whereas for other clinical situations they were willing to tolerate a much, much lower hemoglobin threshold. And the question is why is that? Well, in other clinical specialties and this is the trick trial that I mentioned previously this is Paul Abert's trial, 838 clinically critically ill patients not a lot of cardiology patients in this trial randomized to maintain a hemoglobin of seven versus nine with red blood cell transfusion. And what they found was that a liberal transfusion strategy that is maintaining a hemoglobin of seven was really not that much better than a restrictive transfusion strategy or maintaining a hemoglobin of seven. And so it is now basically part of clinical care in ICUs not to transfuse unless the patient is actively bleeding symptomatic from their anemia or if their hemoglobin really drops to seven or below. Now there are several other studies looking at transfusion triggers. If you look at, for example, patients who have acute GI bleeding a situation that probably all of us would say it's really important to make sure that the patient is typed and crossed and has several units available for transfusion to reduce their risk of dying. Well, in a randomized trial very difficult to do in the US this was done outside the US patients who were having an acute GI hemorrhage were randomized again to a restrictive or a liberal strategy maintaining a hemoglobin of seven versus nine. And what they found was amazingly something that would be counter to what all of us would think which is that the risk of re-bleeding was higher if they got a higher transfusion rate. So in other words, maintaining a hemoglobin of nine resulted in a significantly higher rate of re-bleeding. And the probability of survival was also lower. So even in a situation where a patient is actively bleeding from a GI source it appears that an aggressive use of transfusion is at the very least not better than a more conservative transfusion strategy than in fact maybe worse with respect to re-bleeding or the probability of survival. Now in cardiology though, it's a little bit different, right? Because our patients sometimes have known fixed coronary stenosis. And we know that hemoglobin is one of the variables in the oxygen delivery equation. And in this meta-analysis by Mark Sabatine looking across both STEMI as well as non-STEMI what he found was that there is a U-shaped relationship. Anemia clearly was associated with an increased risk of adverse outcomes. So if your hemoglobin was less than 10 you can see the point estimate for cardiovascular death by 30 days was significantly higher, two and a half times higher than if you had a normal hemoglobin. And if you adjusted for potential confounders you found the exact same thing. And in fact in this particular study they also looked at transfusion rates. And the transfusion rates interestingly were not that high, only about 5% STEMI about 3% non-STEMI and what they found again even for accounting for transfusion that this particular relationship seemed to hold. So perhaps there is a strong body of evidence to have a different transfusion threshold for patients who have fixed coronary stenosis or coronary artery disease particularly those who are coming in with acute ischemic heart disease like acute coronary syndrome. Now interestingly if you look at the literature on patients who have coronary artery disease and transfusion you can either look at that patient as being half full that is they don't need a lot more blood or half empty, they need a lot more blood. On the half empty side there was a study that looked at transfusion or actually anemia in patients who had a hemoglobin that dropped below 10 and found that there was a spike in cardiac output. It may interest you to know this is a study done at Duke by Jim Warren and Eugene Stead. I don't think you can do this study anymore but what they did was they took medical students put P.A. catheters in them and then gradually phlebotomized them to their hemoglobin drop below 10. And if their hemoglobin drop below 10 they found that the cardiac output spiked so they said that's a maladaptive response you gotta keep the hemoglobin at 10. Other studies have found the same thing there's a Harlan Krumholz's look at the co-operative cardiovascular project showing increased mortality of an inadequate was less than 33 that translates to about a hemoglobin of 10 or 11. On the other hand, we have plenty of data particularly in Jehovah's Witnesses for example where you can push the hemoglobin as low as six after bypass surgery and find no increase in mortality. So when you have this degree of evidence showing that you can either transfuse a patient or not transfuse a patient you have perfect clinical equipoise. And because of this uncertainty there are about 12 million units of blood transfused annually in the United States that's approximately three to six units for every single citizen in the city of Houston to get a unit of blood. So we really don't have any idea how we should be using blood transfusion in patients with ischemic heart disease. We have a lot of observational data so Matt Sherwood when he was a fellow with us he's now at Inova in Virginia looked at 1.3 million patients in the CAF PCI registry undergoing PCI and found that there was a tremendous amount of variability by sight and who gets transfused. Some sites weren't transfusing anyone undergoing PCI and some albeit a minority of sites were transfusing patients before PCI 8% of the patients or more of the patients undergoing PCI. So you can see there's this tremendous amount of variability in practice probably driven by the fact that there isn't a solid evidence base to guide us. When you put together all of the observational data or at least the largest observational studies being done a lot of these were done by our group again Matt Sherwood study we've looked at patients with acute coronary syndrome and found again that there's a strong relationship not causal perhaps, but an association between the aggressive use of blood transfusion and an increased risk for mortality as well as other ischemic events. And in our study in non-ST segmental elevation ACS patients what we found was that the relate that relationship existed down to a hemoglobin of eight. So in other words, if you transfused a patient and their hemoglobin was below eight there was no effect on mortality. If you transfuse the patient and their hemoglobin was above eight when you transfuse them that's where that manifestation of increased risk for mortality started being evident. So perhaps there are different triggers in the ACS population. Maybe it's not seven versus nine maybe it's eight versus 10 and that's something that we have to think about. The guidelines, these are the European guidelines previously said that you can transfuse an ACS patient if their hemoglobin is below eight. And in fact, I'll blow this up for you to see that recommendation. In patients with anemia and no evidence of active bleeding blood transfusion may be considered in the case of compromised hemodynamic status a hematocrit less than 25 or hemoglobin level less than eight. It's a 2B recommendation with a level of evidence C that level of evidence really does come from that observational study I just showed you that we did and published in JAMA in 2004. But the reality is these are inconsistent no adequately powered randomized trials. But despite that a restrictive policy may be considered and you can see how there's a lot of hedging in that language because there's no large adequately powered randomized trial. Now I had the privilege of participating in the clinical practice guidelines from the American Association of Blood Banks back in 2016, Jeff Carson led this and we actually chose not to recommend actually we chose not to make any recommendation at all. We couldn't recommend either for or against a liberal or restrictive transfusion threshold in patients with acute coronary syndrome because we just didn't have the data to support that recommendation. Kind of a side trivia note, we actually met here in Washington DC when we were developing this document and I remember very distinctly after that day all day of discussion we went to dinner and it just so happened that Anthony Fauci was sitting at the table next to us and I leaned over to one of my colleagues from Canada and I said, hey, that's Anthony Fauci and she said, who's that? And who would have thought that he would have such a huge role to play just a few years from then? So let's talk about this. Why would transfusion potentially be harmful to you? We know why it may be beneficial, right? Again, hemoglobin is part of the oxygen delivery equation to increase the hemoglobin theoretically you should be increasing oxygen delivery. Well, it turns out that stored red blood cells are not the same as the red cells that are floating around in our arteries and veins. Now I'm not a hematologist, I'm not even a basic scientist but Jonathan Stamler when he was at Duke spoke very slowly so that I would understand this and it turns out that stored red cells are low in two, three GPG so they don't actually deliver oxygen very well. They have a high oxygen affinity. In fact, they're depleted of nitric oxide almost immediately after they get stored and nitric oxide plays a fundamental role in oxygen exchange. I learned that respiratory exchange is about oxygen carbon dioxide but there's a third gas involved. So in the vein the hemoglobin molecule exists as a T isomer and in the lungs it takes up oxygen as well as nitric oxide to form something called S nitrosohemoglobin and it changes the confirmation to the R confirmation and at the level of the capillary and the deoxygenated organ the first thing it does is it releases the nitric oxide which causes capillary vasodilation and facilitates the delivery of oxygen into that tissue. If you don't have nitric oxide you impair oxygen delivery and maybe that's the lesion within stored red cells that may explain the fact that there's been it's been very challenging to show a benefit of blood transfusion in various patient populations. So packed red cells have something called the storage lesion which is characterized by depletion of nitric oxide which makes them function as nitric oxide sinks which can lead to vasoconstriction, platelet aggregation and ineffective oxygen delivery. It is also associated with increases in CRP and IL-6 and packed red cells may also exhibit procoagulant properties. The longer you store the red cells the greater the storage lesion. Now what's interesting is let's think about maybe we can give more fresh blood. So instead of blood that's been stored in the blood bank for a long time there's been a big push to say let's use fresher blood. Maybe we can overcome the storage lesion. Well, here's a randomized trial looking at critically ill adult patients randomized to fresh versus standard issue blood. Shockingly, there was no difference. So it sounds like once that storage lesion occurs there's probably no reversing it. And in fact, there was no benefit in this particular population of giving them fresh blood over standard blood. Similarly, this is one of my favorite randomized trial. This is Nancy Heddle's trial that she did in Canada. It's a very, very pragmatic study. These patients, only patients with type A or type O blood were randomized in this study. And the reason is that those are the blood types where blood sits on the shelf for a short amount of time or for a long period of time. So it's almost a natural randomization. And these patients were randomized in the blood bank in a one to two randomization to either receiving short-term stored red cells or long-term stored red cells. And what they found in fact, and I should also mention that the entire follow-up of this study was done through the electronic health record. So again, one of the first pragmatic trials to be done, you can see a very, very large sample size, almost 25,000 patients. And what she found was no benefit to new blood over older blood with respect to in-hospital mortality. So this whole idea of using fresh blood to overcome the potential adverse consequences of blood transfusion really has not panned out. And really it doesn't really matter if we make an effort to transfuse new blood, it makes no difference at all to the patient outcome. So now I showed you the slide previously, right? Because this is the trick trial. And again, to remind you, no difference in restrictive versus liberal transfusion strategies and critically ill patients. If you look at the subset of patients who had quote, cardiovascular disease. So only 257 patients in this trial had ischemic heart disease. Very few patients with acute coronary syndrome. In fact, a lot of these patients had a heart failure. You can see that the curves reverse themselves. So all of a sudden now, maybe the liberal transfusion strategy starts looking a little bit better. It's a subgroup analysis of a overall negative trial. So you wouldn't expect there to be a significant difference. But the change in that the curves is really intriguing, suggesting that maybe there is something to a different threshold in the patient with ischemic heart disease and that seven versus nine may not necessarily be what we need to think about in these particular patients. Well, you all are probably familiar with this trial, the reality trial, which is presented at ACC 2020. This was a trial led by Gabriel Steg and Gregory de Croc in France where they took 666 patients with acute myocardial infarction and randomized them to a restrictive versus liberal strategy. Their hemoglobin cutoffs were eight versus 10. Again, based on the observational data, eight versus 10, not versus seven versus nine. Now the primary endpoint of this trial was actually not a clinical endpoint. It was cost-effectiveness. I suspect that the journal editors made them put in the clinical outcomes and what they found at six months in short-term followup at 30 days was really no significant difference. So they were not able to show a benefit of a restrictive strategy or a liberal strategy in patients with acute myocardial infarction. They did follow the patients out to 12 months and look at this, the curves crossed. They crossed at about the four month time point. Now it looks like the liberal strategy is starting to look a little bit better. Again, not significantly better because again, this trial was not powered for clinical endpoints, but really an intriguing crossover of these curves. You can't really rely on this because again, it's not powered for clinical outcomes. The 12 month outcome wasn't the primary outcome. So we still don't know, but again, a lot of circumstantial evidence starting to really support equipoise in this particular patient population. And you can see here that the hazard ratios again, shown for you at less than five months versus five months. Now we did a pilot randomized trial at the Durham VA called CRIT pilot where we randomized about 60 patients with acute coronary syndrome to maintain a hemoglobin of eight versus 10. Jeff Carson did a smaller study called Mint where he randomized 110 patients with acute coronary syndrome to the same transfusion cutoffs. Our study showed potential harm with a liberal strategy. His study showed potential harm with a restrictive strategy. So again, perfect equipoise. And Jeff was able to use that to leverage NIH funding to get the Mint trial funded. Now he called this pilot study Mint. He calls this big study Mint. I thought he should call the pilot study Junior Mint, but we have different senses of humor, I guess. But Mint trial is ongoing now. It's 3,500 patients. This is going to be the definitive study. Looking at what the optimal transfusion threshold is in patients with acute coronary syndrome or randomizing 3,500 patients with STEMI or non-STEMI, type two MIs are allowed to a restrictive strategy, maintain a hemoglobin of eight versus maintaining a hemoglobin of 10. And the primary endpoint is at 30 days where over 2,600 patients into this trial, this is a very, very challenging trial to enroll in. It's not your standard acute coronary syndrome trial. These patients are very sick.  And a completely blinded look at our event rates seem to show that we are at least on target with our event rates. And potentially, these are patients who are going to have the most of benefit from the results of the study. For our sites that are participating in this, I wanna publicly thank them. It's a very challenging trial to enroll in and we hope to have data within the next few years. So let's switch gears now because we don't know what to do about bleeding. We don't know what to do about transfusion. Maybe prevention is the best thing. And so let's talk about some prevention strategies. And again, the strategy I wanna focus on is really radial access. For those of you who know me, you know how I got started with radial access really pushed by the fellows who realized that if they go radial, they don't have to stay late to pull sheets. So one of our more enterprising fellows who ultimately went into EP said that, I came into work one day, the board was full and he said, we're just gonna do all these patients radial. And I said, okay. And that's how we got started. Now, we looked at close to 600,000 procedures in the CATH PCI registry to look and see, what is the overall prevalence of radial access in 2004 to 2007? And what were the associated outcomes? What we found was that there was no difference in procedure success between radial and federal, but there was about a 60% reduction in bleeding and vascular complications. And despite that potential benefit, radial access at that time between 2004 and 2007 in the United States only accounted for about 1.3% of all PCI procedures. The vast majority of hospitals were doing 0% radial at that time. And it was around 2008 or so where the rival trial was starting up. And I had the privilege of being the US National Coordinator for the rival study. For those of you who don't, who are not familiar with rival, rival was a 7,000 patient randomized trial of patients with acute coronary syndrome who were undergoing invasive risk stratification, randomized to undergo radial access versus femoral access for their diagnostic calf. This was a sub-randomization within the larger current Oasis 7 study. That study was looking at different doses of clopidogal versus aspirin. So really we had to use the end points that were part of that study, which was a composite of death MI stroke or non-cabbage related major bleeding in 30 days. We also were forced to use the definitions. So in this study, to qualify for a major bleeding event, you had to have, let's for example, if you bled into your eyes, but you didn't go blind, that didn't count. If you bled, but you didn't receive a transfusion, that didn't count. So these are major, major bleeding events that were occurring in this particular study. Surprisingly, we were shocked. We were shocked because there was no difference. We thought for sure, radial was gonna make a difference with respect to the bleeding end point. But what we learned was something that I told you at the very beginning, which is that most of the patients enrolled in this trial were non-ST segment elevation MI patients and the vast majority of their bleeding events were not related to the vascular access site at all. So in fact, our intervention was not gonna actually show a difference. In the patients who had ST segment elevation MI, which comprised about a third of the patient's randomized, there was a significant reduction in the primary endpoint. And then when we looked at a pre-specified endpoint of major vascular access site complications, a very, very vascular access site sensitive outcome. That's where we started showing a benefit and over 60% reduction with radial access versus femoral access. In fact, the follow-up of this trial was 99.9% complete. Only one patient out of 7,000 was lost to follow-up. The US enrolled about 180 patients in this trial. We were the ones who lost someone to follow-up. And I remember hearing that statistic and I leaned over to one of my colleagues from France and I said, wow, it's really embarrassing. I mean, we put very few patients into this study and we're the ones who lost someone to follow-up. And without missing a beat, he said, that's the risk you take when you do trials in third-world countries. So here we have a pretty solid body of observational data, at least one trial that shows a benefit with respect to vascular access site complications. The question is, why are the rates so low? So with Christian Helfer to the Pewds Sound VA, we surveyed 200 interventional cardiologists in the United States. And what we found was that they identified two barriers to radial adoption in the US. The first was there was no US-based trial data. So a lot of people looked at rival and said, well, that's not relevant to the way I do business because I'm not an interventional cardiologist in Europe. I'm in a US interventionist. And the second was that there were no training opportunities for people post-fellowship. We held a think tank at the FDA. And you may wonder, why is the FDA interested in this? Well, they were interested in this because they thought that it was an approach to make antithraumatic therapy safer. So we held the think tank. It was open to the public. The NIH was there. Lots of patients were there. Lots of practitioners were there. And at that day-long meeting, the outcome was, you got to do our US-based randomized trial of radial versus femoral. Easy, right? Well, it's not so easy. The number one challenge is randomization. At that time, people who were doing radial access were doing it for a reason. Maybe they had a bad experience. They may be unwilling to randomize to femoral. Someone who was not very well trained in radial may in fact be unable to randomize to radial. The second challenge was funding. This is not a new gizmo. It's not a drug. Who's going to fund this trial? We needed a pragmatic way to do the trial. And the third challenge was finding those cardiologists who were proficient in radial access who were also willing to randomize. Now, I filled out site surveys. You filled out site surveys. We're all challenged by recall bias. At that time, if a cardiologist said that they were doing 50% radial approach, they were probably really doing 30%. So now it just so happened that my mentor at the time, Bob Harrington, had received funding from the NIH to build randomization into the CAF PCI registry. So a very pragmatic approach to randomized trials. As part of that grant funding mechanism, he had to start a randomized trial by the end of the grant period. And this was the perfect question to study. Now we had to find who the patients were. And what we found after talking to a lot of thought leaders in the space was that females were the ideal patient population to randomize. First of all, women are significantly underrepresented in all of our interventional trials. The same goes for radial trials. And they present a unique challenge. Almost every model of bleeding shows that females are at increased risk for bleeding, but they have smaller radial arteries, potentially higher procedural failure rate. So that's the first challenge. A radial operator would be willing to randomize a female patient to either radial versus thermal because we don't know what's better. Particularly if we have procedures success or access site crossover as part of the co-primary endpoint. The second challenge, we did have this platform for a cheaper trial. Now we could use this National Cardiovascular Research Infrastructure, so-called NCRI, which embedded randomization of the CATHPCI registry to actually do this study. And what this does is leverages the workflow of the registry participants into a very streamlined way of collecting data. We could also go to that registry and find out who's doing radial access in women. And I'm showing you the exact data. We said show us the sites that are actually doing radial approach in females. And this is the data output. When we took these first 60 sites and made them our sites for our randomized trial, some of these sites had never participated in clinical research previously. So let me show you how this worked. A site participating in our trial identified a patient, consented them, randomized them within the database. Now as part of the normal registry workflow, this site ended all the demographics, medical history, procedural data into the CATHPCI registry. They were doing that anyway. The back office software built through the grant funding would auto-populate a clinical trial case reportable. So when the study coordinator went to fill it out, 60 to 70% of it was already complete. In clinical trials, we pay for time. So this reduced the time that the clinical study coordinator had to spend filling in data. They would fill in the only the unique pages for the trial and that created the analytic database. Two of the sites that weren't participating, participating in the CATHPCI registry filled out all the variables manually, hats off to them, it was a tremendous amount of data. And this is what led to the safe PCI for women trial. This is still unfortunately the only randomized trial performed solely in women in the interventional cardiology space. We randomized 1,800 women to radial versus femoral with a primary efficacy endpoint of bleeding or vascular complications and a primary feasibility endpoint of access site crossover. Here are the results. We found a 70% reduction in bleeding or vascular complications at the expense of about a 6% access site crossover, mostly for radial spasm. Now that's interesting, but what's more interesting for the clinical trialists in the audience is that we did this 1,800 patient study with clinical endpoint adjudication for only $5 million.  In this study, we turned that on its head, 97% of our sites enrolled at least one patient and 71% of our sites enrolled at least 10 patients. This is just now a body of literature that has almost 19,000 randomized trials showing that radial access not only reduces major bleeding compared with femoral access, but also reduces mortality. Again, to take you back to the beginning of the talk, mostly in the STEMI population who have the most to gain from radial access. Even in the Safari trial, which was powered specifically for mortality comparing radial versus femoral, although stopped early because of lack of funding, they showed no difference. But if you look at the trial endpoint of bleeding, there's a very, very intriguing separation of curves. Again, suggesting a benefit of radial access and to Michelle LeMay's credit, he did do a meta analysis of his study Safari along with the other 13 studies, almost 11,000, almost 12,000 patients. Again, showing that there is a significant reduction in mortality favoring radial access, again, driven by the STEMI population. So how has radial access changed in the United States? We were 1% in 2007. While I'm pleased to say that in the second quarter of 2021, radial access accounted for 63% of all diagnostic cats and 54% of all PCIs in the United States. We've done this over the period of about a decade, a little shorter than the 200 years it took for the British Navy to put sauerkraut and limes on all their ships. And I think this proportion is gonna continue to increase as fellows who are trained in radial access go out into practice. We also now have a guideline recommendation. You all are probably familiar with the revascularization guidelines. We now have a class one recommendation with a level of evidence A for ACS patients and for stable ischemic heart disease patients to reduce the risk of death vascular complications or access site bleeding. Now we're of course in the DES era. So now we're gonna shift our bleeding risk to the outpatient setting because now patients we need to take dual anti-platelet therapy for a prolonged period of time. And so the bleeding issue has not gone away. It's just shifted from inside the hospital to outside the hospital. And I was pleased to be part of the author group that came up with this high bleeding risk criteria specifically related to dual anti-platelet therapy after drug-illuding stents. This is led by Phillip Urban. You can see major and minor criteria. Let's go back to the patient that we described at the beginning. This patient has the following high bleeding risk features. She's older, she has renal disease, she has anemia and she's on oral anticoagulation. So now we have to adapt our strategies not just for in the hospital for what happens after she leaves the hospital with respect to treatment approaches. So here's what we did with this patient. We went left radial. She had a significant LED lesion. Gender went intravascular ultrasound guided PCI in placement of a drug-illuding stent. She was treated with dual, actually not anti-platelet therapy with dual antithrombotic therapy with just the oral anticoagulant and clopidogrel, no aspirin as the trials have shown. Guideline directed medical therapy for secondary prevention. And amazingly her anemia workup showed that it was likely nutritional. About a third of anemia in the elderly is related to nutritional status. And I have a personal stake in this. My mom actually had nutritional anemia. And so it is something if you notice that in your patients, particularly if they're older, make sure you do a complete workup so you can identify the cause and get it treated. Post-PCI bleeding complications are common and they're associated with increased morbidity and mortality. I didn't show you the cost data but take my word for it. So I think as cardiologists, it's incumbent on us to try and reduce both the ischemic and bleeding risk. We don't actually know yet what the optimal transfusion threshold is in the patients with acute coronary syndrome. Lots of observational data, some pilot data, but the ongoing mint trial will really be the definitive answer helping us to try and guide transfusions in these patients. With respect to prevention, radial access is really the most effective strategy. And we really all should be using a radial first approach in our patients. And now the bleeding risk is shifting from procedural risk to long-term risk and antithrombotic therapy after PCI. But this is a constantly moving and shifting space, stent platforms, anti-platelet strategies are all evolving to try and reduce long-term bleeding risk without sacrificing efficacy. So again, it was a huge honor for me to present at the annual Lewis lecture. I wanna thank Honey for the invitation. I wanna thank all of you for dialing in.


Okay, we're back here live at VMware 2013, 2012 was last year, four years going. Dave Vellante and I, I'm John Furrier. Dave Vellante, this is theCUBE, our flagship program. We go out to the events, I start to see them from the noise. I'm here with Dave Vellante. Yes, John, we were started here in 2010. This is a great location, we're thrilled to be with Steven DeWitt, who's the Senior Vice President and General Manager at Global Marketing for HP's Enterprise Group, longtime Cube alum and friend of theCUBE, Steven, welcome back. Great to be here. Great to be at VMworld, you know. I remember a number of years ago as Diane was creating VMware, at the time I was building a company called Cobalt Networks and I remember Diane's passion around the opportunity for virtualization, it's awesome to see the journey that they've been on, the impact that they've had in the market, really the highs and lows of that journey because, you know, we're all on a journey. There's a lot of big things in front of us, the cloud, alternative models, new underlying technology, et cetera, but this is a great example of an entrepreneur's vision turning into a multi-billion dollar business. Yeah, and then, you know, the thing about HP, and I think uniquely, other companies do it, but HP is particularly unique in this regard. When a trend occurs and you didn't necessarily invent that trend, you are able to leverage that. You know, you sell probably more VMware licenses than anybody, obviously VMware sells more, but the distribution channel, so you'd be able to take advantage of that. But now that's changing, you know what I mean? That world is changing, we're talking about multi-hypervisors, we're here at OpenStack, you guys are behind that, so where are we in that whole journey and what do you see as the big shifts that are going on and how are you going to capitalize? Well, two things, first off, you hit a very key point, you know, Hewlett Packard has been around for a long time, you know, if you think of the business challenges that we were solving in the 30s, in the 40s, in the 50s, in the 60s, really what draws the tie between all of those is technology was necessary in order to solve something that brought the human experience forward. When JFK challenged the world to put a man on the moon, and all of a sudden, okay, we got propellant that we can put in a rocket and send there, but how do you actually build a system that can go to the moon and return people back safely? It's not like there was equipment out there, I'm going to run down to Radio Shack and pick that up. You had to invent, you had to create the technology that enabled that experience to happen, and HP was there, you know, turn the clock forward, Bob Evans creates the 360, compute is born, it now starts to move from big rooms into the enterprise. You know, you get in the mini era, then server technology comes, HP was there, the personal computing era comes, HP was there. HP's an engineering company, I mean, when you really peel back the onion and you look beyond the headlines and the, you know, the hyperbole out there, it is an engineering company at its roots, and the culture of the company is very much what are the problems that we need to solve and how do we organize to solve them? You know, this is an interesting show that's both flattering and I think speaks to the reality of the market right now, take converged infrastructure, something we talk a lot about at HP. Why do we talk about it? Because it's something we pioneered, it's something we have a lot of passion about because we believe the convergence of infrastructure is necessary for the Star Trekky in future to emerge, where the human interaction with infrastructure is frictionless, it's not about the language of networking or the language of storage or the language of server technology, it's the language of us, it's the language of applications. And that's, you know, that's a big step forward and certainly represents the hill that we have to climb through the rest of this decade. Steve, you give the history lesson on HP and obviously the trends here is a modern era and the theme has changed, I mean, 10 years of VMworld, a lot of changes in the marketplace has been some changes at the executive level of HP. Microsoft has an executive change, Steve Ballmer, legend retiring and I'm sure he's not really happy about it, he wants to stay in fight, I mean, knowing Microsoft and you know those guys. So there's massive change going on and so if you go back to HP, kind of the Carly Farina time point to today, you're seeing HP has turned into an operating company and you mentioned they're an engineering company, they are an engineering company, so I got to ask you, what's going on at HP to make it an engineering company in the mind of the customer and to the employee base? Yeah, it's an excellent question. You know, look, history will judge the leadership of Hula Packard through Carly and Mark and Leo. You know, all three of those leaders were very gifted go-to-market leaders. All of them, I think, radically matured HP's field capabilities and that was an investment that was key. Hey, showing up, being there, having that relationship with your customer is consistent with HP's values.  But in our world, innovation, no offense to Procter & Gamble, a great customer and partner of ours, but innovation in our world isn't adding lemon fresh scent or, you know, taking a particular sliver of a brand and changing the packaging, et cetera. Innovation in our world is about the fundamental transformation of a work process, a way things are done.  No question about it. Does that fill your point? Absolutely, and if you think about the new style of IT, what faces an IT leader today, there are, the differences are massive. You have to, we're in a post-angry bird's world. You know, you're dealing with millennials, you're dealing with a brand that has to be able to prosper in a mobile connected universe. You know, it was just a handful of years ago that we would take virtualization, for example. It was a handful of years ago that an IT professional could walk into their senior management and beat their chest. Hey, I've been able to virtualize 50% of my compute infrastructure. And as a result of that, we've been able to derive these savings, which can be used there. That's a great argument. That's an argument that drove a lot of VMware's growth. Great consolidation, yes it is. Unbelievable story that's out there, but now what IT needs to do is they need to transform their apps. You know, they need to go from legacy hardwired apps to mobile apps. They need to go, they need to build apps that are built on top of a data lake so that whether you're using an app on your phone or a core business app, that it's getting the benefit of all of the data underneath in a way that's very transparent.  I like data ocean because it's a lot of, it's big. But you're talking about top line revenue, right? I mean, you're talking about not just being a cost saver, but in consolidating, but driving business. Is that what you're getting at? Absolutely, and look, there's no question Hula Packard's revenue portfolio, like it has done in the past, will evolve to match the opportunity of the market today. If you look at our last reported results, you know, we break our PPS business, our enterprise business, our services business, et cetera. You are going to continue to see the evolution towards rapid innovation, co-innovation with our partners. You know, we just launched an enterprise campaign at Hula Packard, which is featuring companies like NASCAR, which you'll see on television in the days ahead. In a story that will talk about how an entity like NASCAR is able to bring the power of the data ocean to the fingertips of their ecosystem, their drivers, their teams. There's a lot of waves to surf in that ocean. There's a lot of waves to search there, but that is representative symbolic of the new positioning of HB. So let's talk enterprise group, right? Obviously, you know, obviously the performance on the earnings was, you know, we know this, Dave and I have been covering these products. We know they are their service storage network. We know all the folks who run them, and they're selling shipments, products, and then obviously the services, and software has been a big part of it, but the modern era of cloud and big data, there's a lot of co-mingling going on, right? So can you shed some light into the vision and how you get the innovation as you got blending? Okay, is that George Kedifus' group? Because he's done a good job in looking at what George has done. He's solid, looking good, grabbing those up, even with Vertica. Vertica's had a great quarter. Vertica had a fantastic course, so did autonomy, so did the security portfolio. You know, I think it speaks to convergence. At the end of the day, end customers are looking for solutions that don't have friction. They're looking for solutions that can be tied to their business priority and their application strategy, evolution, life cycle, et cetera. And they want a partner that can provide a full service across that environment. Not every company is the same. Not every large enterprise company is going to make their journey to the cloud the same way. You know, an operating model for a media and entertainment company like DreamWorks is very different than an operating model for a transportation company like United. So they have to look at the context and the opportunities of clouds, application transformation, and they need to be able to plot that journey along the path of their business. But cloud is central to the enterprise group. Oh, I think cloud is central, really. Cloud is a metaphor for the radical transformation and how infrastructure is being delivered is fundamental. One of the things that was announced last week, as we talked about Bill Vecti moving in to lead the enterprise group, is, you know, we began a handful of years ago under Dave bringing server storage and networking together. We recently announced a Converged Systems Group under Tom Joyce that will drive the integration of a number of our solutions as well as our go-forward orchestration. But that's just infrastructure convergence. Then you have to bring the other aspects in so that ultimately you can get to a world. This is not a siloed model. That's about applications and experiences. It's not a siloed model. I think what you were saying before was right on. It's the applications that have to now transform beyond what you're saying about consolidation. So let's unpack that a little bit. You guys talk about the new style of IT. We've been talking about sort of modern infrastructure. You've got this data. Most organizations today, I would argue, are not data-driven. And I would say most when it comes to mobile, most organizations are kind of paving the cow path. So they're not sort of taking a mobile first. So is that what has to occur for this transformation to happen at the application level? A modern infrastructure, a data architecture, an application development, DNA that is rapid, maybe DevOps-like. And then on top of that, a set of applications that are truly driven for the consumer user experience. Yes, unequivocally across the board there. And there's so many big nuggets in what you said. Start with that experience first off. We've all witnessed the growth of Google. 12 years ago, they had no revenue, no customers, and all they did was bring the human, I'm trivializing an enormous amount of data science and algorithms, but what they were able to do fundamentally is bring the human experience and data together. That sort of capability is ripe in the data center now. IT's job is to service the business units. Business operates at the pace of business. Business doesn't operate at the rate of silicon evolution. Business doesn't operate at the rate at which you can drive converge management. This is the transition period where the underlying building blocks are evolving, modern architecture. And remember, we haven't even talked about the huge change that's coming in infrastructure with the emergence of memrister and silicon systems on a chip and board level photonics. Systems, infrastructure itself is going to go through a massive transformation. But at the end of the day, what we believe is a true north is that IT operations are all about the speed of business, the ability to deliver the application intelligence as well as all of the requirements inside of the enterprise for security, compliance, regulatory, whatever your industry is, in a way that is orchestrated by the human, not defined by the different languages of the building blocks that make it happen. We're going from pieces parts to a truly integrated system and that is also building a new class of service provider or delivery vehicle for that capability. The cloud service provider, the managed service provider, the role of the legacy VAR channel are all morphing dramatically in response to this so that that local customization and that local, whether it's knowledge of your city or your vertical, et cetera, will still be provided in this new world. Steven, you know, HP's lucky to have you because you have an entrepreneurial background, you have a great passion, you've got a good nose for the trends, but also you great marker and an operator and as an executive. So I just want to say that it's awesome for HP to have you. But I got to ask you a question that's got a little bit of tap of your entrepreneurial background and your HP knowledge. We just had D Raj on the founder of Nutanix, he's been in the business for a while, knows this up and down like you do. And he said something, we made big bets with Nutanix. All the Naysayers for a full year said no, no, no. So there's a lot of Naysayers out there on HP. Break up the company, do this, do that. So my always signal to read to T. Lee's was whatever the Naysayers say, if there was a good feel for it, it's probably not going to happen. So I feel that the Naysayers are off based on the breakup HP philosophy. Because if we all do the Naysayers sit all the time, there'd never be innovation. So what's your gut feeling on those Naysayers out there on HP? Oh, break up the company, spread the cash around. Look, I know. Innovation's impossible. Unequivocally, brain dead. You know, part of the power of HP and certainly the unique position of HP in the market is that our R&D horizon goes from your fingertips through the data center and back. From the human experience, to the IT operations, to the application development world, to the unique realities of businesses in their world and their language. Being a portfolio company gives us unique perspective. Having that services footprint gives us unique perspective. Look, let me get the data out there. We've looked at it. Everybody's looked at it. Could you ginsu up the company? What are the synergies? What are the dis synergies? The simple reality is, and hopefully the promise of all of this, is that we are moving truly to a customer driven world. To a world that is about the unique dynamics of the enterprise or the small business or the carrier, et cetera. This is what we'd always hope we would get to. So the promised land that we're evolving towards is a good promised land. It's up to the company to evolve. It's up to the company to make sure that its portfolio is appropriately aligned. And we're doing that. And there's proof in the pudding. And I think the naysayers are getting it. Yeah, the PC market has changed. There's no doubt about it. We're not carrying big lunkables around. Now we're actually, I would argue, moving into a true era of personal computing, where the potential of your state being delivered against any piece of glass isn't a pipe dream, it's a reality. Now we need to get there. And companies like HP are well positioned to do that. Google, Apple, Microsoft, other players. We will do as we've done in the past and we will knit it together. And the people that have been successful, look at the history like D. Raj, they blow past the naysayers and stay on the course. And that's, I think, the key. And I agree with that. I think HP is going to break through it. So you've got to be mile markers along the way that say that what you're doing from a strategy perspective is working. Meg was extremely clear with the street and the investor community in general about the transition and the path that we were going to go on. And we've been on that path. Over the last handful of months, we've dramatically reduced the debt on the operating company. If we execute well through the remainder of our fiscal year, we will exit this fiscal year debt-free on the operating company. Yeah, it's consistent. It's got the cash machine fixed and you can go debt-free. Your balance sheet matters. HP's just too big to go private. And I think Michael Dell's move is interesting because that to me validates HP's on the right course. And let me give you my example. By him going private allows him to retool his product strategy.  The people are still going to use something. No question. Like a usable computer, Star Trek phone, whatever they're going to do. Compute exists. So the whole idea that the PC is dead is ridiculous. I mean, it'll always be personal. It might be free. It might be embedded. So to me, the Dell signal's interesting because that means he's going to take it private. We organize the research for the long haul. He's not giving up on the PC. So I think that to me is a signal. I think it's a signal. Michael is a, I have tremendous respect as a fellow entrepreneur at the great success that he's achieved. You are trading one master for another master. And that is something. Look, your financial construct will influence and dictate your strategical. The private equity master. No, no, no. Michael will own three fourths of the company. Silver Lake only will have a quarter. So it's going to be his Michael's playground. So it is Michael's playground. You have some fun, I think. Do you want to try on that compete? Look, it's going to be a challenge. And at the end of the day, you got to love that. The one thing though, that regardless of who's got command and control, you got to engineer, you got to build. You got to have the vision of what you're building towards. And you've got to have the ability to co-innovate with your customer base. I think I have the employees behind it. I think one of the things I like about what Meg is doing right now is that it's zero tolerance to people who aren't on the plant. And I think that's the way I would say it without going into detail before I get more facts. But I think the bigger picture to me is huge customer base, huge revenue, great R&D. Can the people, the troops, pull it together? Oh, I think Meg has done an amazing job of lighting up HP. And she hasn't done it with platitudes and shtick and things like that that you see so many leaders doing it. She's done it with transparency. She's done it with a culture of accountability and look at the end of the day.  Meg has been very much behind the curtain, not really putting herself out there. And has put a level of energy and intensity around the company that is awesome. I can say, and look I can't speak on behalf of the entire HP community. But as a proud HP'er, she's done a fantastic job but it's what we do from this point forward. Let's get her on theCUBE someday. So, when you see her next, then hey, you got to do an interview with John and Meg. All right, I'll put in the camera. So final word, we're getting the hook here, but I want to just give you the final word. HP, we're here at VMworld, 10 years. You know, known Diane Greene, you said it from the beginning. Where's this going, this world? This is the, give us the Steven perspective. The next, the next 20 mile stair. Okay, the next, the next, the next valley. Yeah, put on your invent hat. Well, so a couple of things. Just one thing really quick on, to close on VMware. You know, the first five years, five, six years, we watched hand in hand. I wasn't at HP at the time, but I watched VMware go through the prove it out phase, working in some of the most complex environments on the planet. If you look at 2005 really until now is when the industry has said we get it, we understand it, we internalize the value proposition, and we know how to make that magic happen. And it's been awesome to be a partner with VMware through that, certainly from an HP perspective. We've driven billions of dollars of business and have saved customers a ton. I think moving forward, and we certainly have a very tight commitment and alignment to VMware on this, we're all going to put futures out there. We're all going to talk about our true Norse. We're all going to talk about the priorities for our engineering. And in a lot of cases, we're going to be very aligned. And in some cases, we're going to have some divergence. And, you know, I think it's Gartner, who does the trough of disillusionment? That's Gartner. Yeah, it's Gartner. Well, things go through hype cycles and things come out. Just as virtualization went through a hype cycle and came out, there are so many big themes around orchestration, around open stack, around so many other supporting aspects to all of this. The next two or three years, there's going to be a lot of vetting out of that. But I can... It's the inflection point. The winners and losers will start to emerge. The bets will be materializing, and we'll see. And you got to look where people are putting their dollars, where their R&D is going right now, what they're telling you about that trough north, because that trough north is going to be in your fingertips by 2015. And we're excited about that. Steven DeWitt, Senior Vice President of HP's Enterprise Marketing, entrepreneur, tech athlete, great executive at HP, great to have you on theCUBE again, CUBE alumni. Always great to talk to you. We'll be watching you guys. Continue to watch the Enterprise Group and George Kadeva's Group software both kicking butt. True north is cloud and big data. Modern infrastructure and applications.  Congratulations, and we'll maybe see you in Barcelona. This is theCUBE. Be right back after this short break with our next guest.


Okay. Yes. Thank you for the mind in me. Okay. Professor, we have seen the case for two lotteries or three lotteries. But what if there are more number of lotteries, like any arbitrary in number of lotteries, then you can generalize what I've said in a straightforward manner. So it's always a combined lotteries, always a linear combination of the simple lotteries. And it always results in a simple lottery where that will lie inside the polygon that is identified by the simple lotteries. Thank you. Yeah, so Professor. Yeah. So we could express the same thing by an M times N matrix, right? Yes, so the generalization I mean. So as you have seen, there is this P and K, which is essentially a matrix. And yes, so you can write it in the matrix form. And can this sort of, yeah, can this sort of algebra way of looking at it be useful in a particular way that's essentially this is essentially all linear algebra. Yes, yes. Okay. So can I go ahead? So let me look in the chat if there are questions. Okay, so. All the fine lotteries have same number of outcomes in general. Yeah, so all the lotteries are defined on the same space of all the lotteries are defined on the same space of outcomes. It's always defined on this set C. All the lotteries have the same outcomes. Of course, some of the lot of these lotteries that I'm combining can have zero probability for some of the outcome. So in general, each of the lotteries that I combine will have a subset of possible outcome that eventually includes the full set C of all outcomes. So do combine lotteries share some properties with Dirichlet processes, but I think they are much simpler than Dirichlet processes. But yeah, I mean, it's a very simple notion. NLK depend on more than one probability other than alpha k. Yes, of course, say now that we have find out that we can combine lotteries and get another lottery. We can combine combined lotteries and get another lottery. So any combination of lotteries will give you a lottery. So you can define as complicated. So this combination is an operation that you define on the space of lottery. And this will be very important for what we are going to discuss now. So let me resume and go ahead with defining preferences. Okay, so the way in which we define preferences is based on axioms axioms are simple requirement that look like say natural requirements. So the first one is that we are going to require that whatever preference relation we define on combined lotteries is going to be the same on the set on the corresponding reduced lotteries so that it is enough to define the preference relation on simple lotteries. So this also means that we are assuming that an agent with these preferences will be indifferent between a combined lottery and the corresponding reduced lottery. Okay. So this is looks like a very innocent assumption, but actually it is less innocent than it looks. Okay. But it looks like a natural requirement. Okay, second thing, second axiom that we are going to assume is the axiom of continuity. So continuity, I mean the best way to give you an intuition of what continuity means without entering too much mathematical detail is imagine that you have three lotteries and that lottery L1 is at least as good as L2 and at least as good as L3. Sorry, this should be in the reverse order. Okay. So this lottery L1 is at least as good as L2. Oh no, sorry. So this is in the right order. Okay, and L3 is at least as good as L1 and then two is at least as good as L3. There is a so continuity means that there will be a combined lottery between L1 and L2 such that L is equivalent to L3 in the sense that L, so the agent with these preferences would be indifferent between L and L3. Okay. So this implies that say the this set of lotteries between L and L2 are the lotteries which are at least sorry, between L1 and L are the set of lotteries which are at least as good as L3. And those between L and L2 are those for which L3 is at least as good as all this. Okay. And essentially these sets are closed sets. Okay, so this is continuity. Continuity is looks like also an innocent assumption but it is a lot of very strong consequence. One of them is that essentially preferences over lotteries are satisfied this axiom of continuity, then you can represent these preferences by a utility function. Okay, which means that for any two lotteries, if one is at least as good as the other, then the utility of the first should be at least as large as utility of the other one. Okay, so so this you have to think a little bit about it, but it's a really straightforward and direct consequence of the axiom of continuity. So the third axiom that you are going to impose is the independence axiom. And these also looks like a reasonable assumption to make. So we say that preference relation over lotteries satisfies independent axiom if however you define these three lotteries. If lottery L1 is at least as good as L2, then when you combine lottery L1 with L3, the combination will be at least as good as the same combination with the same weight when you take lottery L2. Okay. So, and this should be true for any alpha that you choose between zero and one. Now this is really very strong consequences and as a matter of fact, if you take these three axioms, then what the consequence of this is that the preference relation must be, must have a representation in terms of a utility function that has the expected utility form. So this means that there must be numbers un such that the expected value of every lottery is just the expected value of this you under the probability of the lottery. And what is this you when this you is is essentially the utility of a lottery that gives you outcome and with probability one. Okay. So why is this so. Well, this is so because the independence axiom essentially tells you that this lottery. So if you prefer lottery L1 to L2, then you should prefer this lottery here, which is the combination of L1 with L3 to this lottery here, which is the combination of L2 with L3. Okay. And now the same notion can also be extended to indifference. So if you are in difference between L1 and L2, then you should be indifferent between these two combined lotteries. Okay. And so what distance you is that what distance you is that the indifference lines, the lines over which you are indifferent between lotteries should be straight line and should all be parallel straight lines. Okay. And this is precisely the type of indifference curves that you have with unexpected utility because just simply because it is linear. Okay. So, so it is clear that an expected utility will have indifference curves that are parallel straight lines. But the converse is also true. I mean if you, if you have a preference relation over lotteries that satisfies independence axiom, then the indifference curves must be straight line parallel straight line, which means that there must be utility function that corresponds that represents this preference Now this also tells you how you, you can experimentally measure utility functions. This is numbers UN by making experiments. Okay. And what this is discussed in the lecture, but essentially you do different experiments until you find utility functions, lotteries over which the individual is indifferent. And then you know that the utility function that the reference relation should be straight line that the indifference curve should be straight lines passing through these two points. And, okay, so it's again a good time to stop for questions. And so, is everything clear. So could you show like two slides, I mean slides again. Yes, I can show again the slides. So, this one. One more. Next one. This result here is called the expected utility theorem. Yeah, so I had the question on this only I mean, does it like imply that you will have this kind of expected utility form, like if all three axioms I mean is it necessary. Yes, it's necessarily insufficient. So, the, if you have these three axiom, you have these three axiom, even only if the preference can be represented as I expected utility for, you know, expected utility for. Okay, and this expected to this expected utility form says that the indifference curves will be straight lines. Yes, will be straight lines with this. This you can easily check now because if you take two different lotteries. And you make say, if you make if you take two different lotteries. Imagine that you take lottery L1 and L2, and you are indifferent between them. Then you can combine these two lotteries into another lottery, which will be on this straight line. Okay. And because this is a combined lottery, then you will be indifferent between L1 L2 and this new combined lottery. Okay. So, other questions. So, no other questions that have been clear. Okay, so this is a very important result. So, okay, so let's, let's go ahead. Okay, so now the question is, these expected utility really describes how real people behave. Well, there's been a number of paradoxes that have been discussed that show that in particular situations. Do not really behave in the way that the expected utility would recommend. Okay, so, so take this example for example, so here is a situation where you are asked whether you prefer lottery L1 to L1 prime. So, L1 is the lottery that gives you C2 with probability one. Let's say that C2 is half a million dollars. Okay, so lottery lottery L1 gives you half a million dollars, whereas lottery L1 prime gives you with probability. So, 99% gives you two million and a half.  And typically, if you, well, if you ask to people, which of these two lot that they prefer. They prefer to get the half a million for sure. Okay, because in lottery L prime, L1 prime, there is a small risk 1% that you will get nothing. Okay. Now think about a second question. Okay, you are asked whether you want prefer you prefer lottery L2, which gives you zero probability of C1 with probability 11% to get half a million and 89% to get zero. See whether this is prefer to lottery L2 prime, where instead you get with probability with 10% probability you get two and a half millions. And we will probably 90% you get zero. Okay. So, really, what they, what people will choose between these two lotteries is probably lottery L2 prime, because, well, because you have a 10% probability of winning two and a half millions. You have to 11% probability of winning just half a million. Okay. However, this behavior here is inconsistent with this behavior here, according to the expected utility. Why? Because essentially, if if you prefer lottery L1 to L1 prime, then you should prefer L2 to L2 prime. Why is this so? Because if you do the math, you figure out that the points, the lines that join these two curve these two lotteries L1 and L1 prime and L2 and L2 prime. They are parallel lines. Okay. So whatever is the expected utility that you are using, the indifference curves will always be parallel straight lines. Okay. So however you draw these parallel straight lines, then you should always have a preference relation where if L1 is preferred to L1 prime then L2 should be preferred to L2 prime. Okay. I hope this is, this is clear. If you want, I can just try to make. The more indifference curve, for example, is in this direction, parallel lines in these directions, then and the increase say downward, then it is clear that if you prefer L1 to L1 prime, then you should also prefer, should prefer L2 to L2 prime. This is because the utility and no matter how you change your utility, how you change these straight lines, whatever the straight lines are, you will always either choose L1 and L2 or L1 prime and L2 prime. Okay. So this is just one of the paradoxes of say choice under uncertainty. There are other paradoxes that also tells you that somehow one can distinguish between what is called risk and what is called, say, true uncertainty. So risk is when you know what are the probabilities, uncertainty is when you don't know what are the probabilities. Okay, so there are paradoxes that tell you that when you have uncertainty on the probabilities, then this expected utility, even though you can define a probability distribution over the uncertainty. But still, the expected utility paradigm runs into problems. Okay. I'm not going to into all these details. These are discussed in the book of Moscow L. I hope you can find a copy in your library. This is given in the reference in the website. But if you look at chapter six in Moscow and all these questions are discussed. So questions. So in the previous slide. Yeah. Like, can you explain why it has to be parallel in previously. Why do I mean, did you slide up to this. Elias paradox. Okay.  Yes, the two green lines are part of them. I mean, is it because of the choice you made. Yes, it is because of the choice I made. And then why do you say that you can see there are really extreme choice because one of the pairs is close to this corner here and one of the pairs is close to this corner here. Okay. So, like, then you say in difference curves are perpendicular to this. No, no, they are not perpendicular. What I'm saying in difference curves are straight lines. They are parallel straight lines. So whatever they are, whatever is the orientation of in difference curves. Consistency requires that if you choose at one, then you choose should choose at two. Excuse me. You said we can find a similar examples in chapter six. I wasn't sure what was the source for that which book you mean by that. It's the book of muscle and Winston and the author, which is mentioned in the website. Yes, thank you. Excuse me, Professor. Yes. So for each parallel line we have a sort of orientation and if we go in that direction, the utility increases. So, no, okay, so the, so let me try to make. So, if you. So let's say, if you have a particular say particular utility function here would be something probably like this know because this is what you prefer the least. This is what you prefer the most so maybe the indifference curve of the utility would be like parallel lines in this direction. Okay, like this. Okay. The utility essentially increasing in this direction here. Okay, in this direction. Can you see the figure. Yeah, it's clear. Okay. So, but so the, what the expected utility theorem tells you all these three action turns you is that the utility function is that the preference defined by the utility function always corresponds to utility lines or if you want indifference curves that are parallel straight lines. Okay. Okay, perfect. No, no is clear. Okay. Okay. And again, it is say it is easy to, if you want, if you have a say, an individual that is making choice over lotteries with unexpected utility. It is easy to figure out what these two, what these lines are essentially because essentially you just need to find two lotteries over which the individual is indifferent. And then you have to find in which direction, in which perpendicular direction, the utility increases. Okay. So indeed, so this expected utility is useful and these axioms are useful formalization of how you would like to design a system that takes rational decisions. Okay. So, because, of course, when you would like that if this individual takes rational decisions then you would like these three axioms to be satisfied. You would not like say for example that your preference relation as discontinuities in the sense that you say prefer something to something else but then if you make something one of the optional little bit more risky than your preference changes completely. And so you want continuity, you want these independence axiom and also you want to say combined lotteries have a preference relation which is defined through the preference relational simple lotteries. Okay. Okay, so if there are no other questions. Then, let me proceed. So. Okay, so the last thing I want to discuss today is how do you define risk. Okay. Yeah, conclusion of the last paradox. I mean, is there any conclusion. I mean, any conclusion. The paradox you mentioned. Yeah, so the conclusion is that if you want to design a system that takes decision over uncertain outcomes in a rational manner, then the expected utility is the way to go. But that real people that we have. I mean, it may not be expected utility may not be a good model for how real people behave, because there are some special circumstances where you can show that it does not give reasonable outcomes. Like the alias paradox. Is this clear. But like the alias alias paradox came because we choose the individual to be rational right it's not because the individual is real. Yes. Yes. So the alias paradox tells you that the way which are rational. What would make decision in that situation is seems paradoxical from our psychological point of view. Okay. It means that the expected utility in some situation does not really describes what how we take decisions under and there is a whole field of behavioral economics that is studying what are I mean what are the systematic deviations of the way in which people take decisions with respect to the expected utility part of life. Okay. Okay, thank you. Okay, so let's discuss the risk and in particular this conversion so now one important point is that when, when we discussed preferences over simple outcomes without risk. What we said is that you can represent preferences with an utility function, but essentially that any monotonic transformation of the utility function would correspond to the same preference relation. This is not so in when you have a risky. When you have a risk involved. And why this is not so is that essentially the shape of the expected utility will tell us what is the propensity what is the attitude towards risk of the individual with those preferences. Okay. Let's see how this works. So. So now, let me consider that the set of possible outcome is actually the real line. Okay. And so you can think that the possible outcomes are monetary outcomes are amount of money. You can think as if we are discussing lotteries like horse lotteries. Okay. Normally, lotteries are equivalent to real random variables. And then the expected utility is nothing but the expectation value of the utility computed over this random variable that is just the integral over the probability distribution of the utility of the utility function. Okay. So, one interesting one useful thing that you can do is to define what is a lottery. So let me show a lottery that gives you with probability one. This, the expected value of the utility function. Okay, so let me put it more clearly so this L bar is a lottery such that the utility of this lottery is equal to the expected value of the utility of the law, the true lottery. So you can for every lottery, you can define this lottery L bar, which is essentially the equivalent certain lottery. Okay. So now, if this lottery L bar is preferred to the lottery L, then you say that the individual is risk averse. Why is this because essentially this lottery L bar is a lottery that does not involve risk. You just get L bar with probability one. Instead, the lottery L gives you as an outcome, a random variable X that can take different values with different probabilities. Okay. So, the difference between these two lotteries is that essentially the lottery L involves risk and the lottery L bar does not involve risk. Okay, so if you prefer the lottery that does not involve risk, then you are risk averse. Okay. And if instead you prefer the risky lottery, the random lottery to the lottery L bar, then you are called the risk lover. Okay. Now, it is easy to see that and this is essentially a consequence of Jensen's inequality that risk aversion occurs even only if the U of X is concave like in this in this plot here. Whereas your risk lover only, even only if the U of X is convex, so it has upward curvature. Okay. So let's see this a little bit more in detail. So, one way to see this is imagine that we have a lottery that can give all the outcome X one or X two with probability say one half, then the expected value of X will be this. Okay, you can compute what is the expected value. What is the expected utility. So the expected utility is the average of these two points here. Okay, with probability one half. With probability one half you have this point which is U of X one with probability one half you have this point which is U of X two and you end up with this point which is the expected utility. Okay. Now this is clearly less than the utility of the expected value of X, which correspond to this point here. Okay, if this is expected value of X, then this corresponds to this point. So you see that in this case and this would be the utility of the lottery L bar. Okay, because the L bar gives you as an outcome the expected value of X with probability one. Okay, so this is the case of a risk averse individual because the difference between the utility of the lottery L bar and the expected utility of the lottery L is positive. Okay, and you call this difference, the risk premium. So how much. So you call say this. You call this. Yes, this difference essentially is the risk premium. Okay. Also you can introduce what is called the certainty equivalent. Sorry. Yeah, so so you can introduce what is called the certainty equivalent to the sea of L. And this is the value of X, such that the utility of this value of X is equal to the expected value of you. And if you go to this graph, then you have to take this value of the expected value of you, and you compute this value of sea of L. Okay, so you see that when you are risk averse. This certainty equivalent is less than the expected value of X. So, so this difference is what you are willing to pay in order to get rid of the risk. Okay, and yes, this is called the risk premium. Okay, this is the expected value of X minus sea of L. And so of course, if you have instead utility function that has the opposite, the opposite curvature that it goes like this, then, then all these relations are inverse, in the sense that the risk premium becomes negative in the sense that you are willing to pay in order to play this lottery. And if your utility function instead is just a straight line, then the risk premium is exactly equal to zero. And, and this is when this utility functions are called the risk neutral. Okay. As you can see, there are a few questions on on the chapter. So, let's. Okay. Okay, so. Yes, so. So, not to be the science designers should design lot that is using expected utilities. Well, say, Well, this is, say, a rational way of designing lot Teresa. And the other can say, when you think really think about lot Teresa, there are also other considerations that because essentially here we are considering lot Teresa. Just monetary outcomes. No.  In order to to run this lottery. Okay. You can buy a ticket for a particular horse on a better of a particular course or horse. And now of course, you can buy one ticket to ticket three tickets so how much the ticket cost. Should take into account that the number of tickets that can be both or can be very large. And so these are slightly more complicated problem, and it's very similar to the problem in finance. In finance. What people have to do is to decide what is the right price of asset that can be. For example, an insurance contractor. But once you publish this price, then people can buy as much as they want of this asset. Okay. So, the way which we will discuss at the end of the course how you fix prices in that case but more or less. Any type of concept. Entering play. Okay, there was a discussion on Jensen's inequality and it has been clarified for then there is a question on the masterful book. Yes, which has been answered. So, very good. So, other questions. Hello, Professor, can you come to the slide. Yes, then. Yes. One more back. Yeah, this fourth point what does it like what is saying like what is physical fourth one yeah. So essentially this lottery L one L bar is the lottery. Such that the utility of this lottery is equal to the expected value of your own. So it's a lottery. Yes, it's a lottery that gives these outcome with probability one. Is this kind of thing or like what sorry with probability one means what like means it is sure event kind of right if you do this lottery is sure. Sure, yes. It's not a random lottery is is a, it's just a lottery that gives you something with probability one. Okay. Yeah, you can think of it as a lottery that is a probability distribution which is a delta function. Okay, thank you. Yes. Okay, so I think our time is over. So, thank you very much. So, and well, on the website, you will find a little bit more about this subject. You. I mean, there is a discussion of, if you think about utilities, utility functions. Utility function which have particular properties of risk aversion. And you can, given a certain utility function, you can ask yourself whether this describes a risk averse risk neutral and how this risk aversion changes with with amount x. Okay, but I think the main concepts are the one we describe now. Okay, so next. Next lecture will be next week. Yes. Yeah, may I ask you one question. Yes, please. Yes. Okay. Okay, so I think we will have the function like concave because, okay, according with dancing, but because the, the investor or the other person who is defined this utility will be received a risk will be benefit for for the risk free. Because of the risk free that this is expected to, to receive right, it's a premium risk, sorry, for the premium risk, right. Because this is recent bears, and this will be probably lose or probably get with uncertainly we, and this is a very high risk, right. Essentially, so if you are risk averse, you are willing to pay in order to get rid of the risk. This is what you do when you buy an insurance.  So you go to the insurance company and you get an insurance. This means that if it doesn't happen, then, when you're, but if it happens, then the insurance company will pay for your car. For my car. Let's say, and so we are choosing to pay something which is not random, which is sure, which is the insurance premium over you about a lottery in this case a lottery which can have a negative outcomes for you. If I'm, I'm out, outcomes for me. Okay. That was in my risk premium is negative. No, the risk premium is positive because it refers to the concavity of the curve. Okay, the concavity of the course. Okay. And what is the relation with this normalization distribution in the concavity in the course to the line of the normalization graphic. Sorry. It's them. It is like this light on this reason they're dancing. Oh, sorry, sorry, sorry, sorry, sorry, let me get to the slides. So you mean this graph. Yes, because this is the normalization graph, and then this is the concave graphic that is the James inequality. Yes. The normalization graphic of distribution. This is the utility function. Yes. So the black line is meant to be the utility function. The yellow line is meant to be the probability density function. This can be anything. Anything. Okay. Okay. Thank you so much. Okay. Okay, so thank you very much. So let's take a 10 minutes break before joining. We don't a second lecture so I'll assign you to break out rooms in a random way. Okay. See you later. Okay. Very good. So people are joining back.