Hi everyone. It's a little after 8.30, so I think we'll get going and anyone who's still on their way in can come through. Thank you for being here. This has been an interesting year in chest infections, and we have two wonderful dynamic speakers to tell you about what's been going on in their fields in the last year. Dr. Marcos Restrepo is professor of medicine at the University of Texas at San Antonio. If you are tired or feeling a little sleepy from last night, you will be awakened because he's a dynamic speaker, and he's going to tell us about pneumonia in high-risk populations, COPD, and immune deficiency. Thank you very much, Dr. Machein, and thank you very much for the opportunity to be here. Aloha to all of you. Aloha to all of you. Thank you. I would like to say thank you to Dr. Peter Masson and Dr. Darcy Marciniak and the leadership team and my boss in the chest infections part for the journal to invite us to do this presentation for you today. So what I'm going to be talking about today is this, I have no conflict of interest related to this presentation, and I hope to tackle two high-risk populations with pneumonia that embarks what our research is about and how this, I think, may potentially impact your care. These two populations are one on COPD patients and one on immunocompromised patients. So let's start with the COPD patients. This is a study that we published this year in Archivos de Bronconeumologia, and this is based on some data that we have also published in chest on aspiration pneumonia that I highly recommend you to review it. What we know is that COPD is strongly associated with the development of CAP, and when every person that sees a patient with COPD that gets admitted to the hospital with pneumonia, at least my fellows, my residents, and the clinicians at my institution, they always think pseudomonas. Please tell me if you really think that this is the same case at your institution. Is COPD equal to pseudomonas in patients that present with community-acquired pneumonia? No? Okay, I live in a different world. I live in San Antonio, Texas. That is not in the United States, like it seems where you are. But when we look at this data, we ask around the world, our colleagues, and they always said in my institution, every patient that comes with COPD that have any severity of the disease, we always think that we need to cover for pseudomonas, and I will show you why I think you are not telling me the whole truth when I ask you this question a little bit later. So the objective of this was to assess the microbiological patterns associated with risk factors that determine the empiric antibiotic therapy in hospitalized patients with COPD. To do this question for this study, we utilized the GLEAM platform. How many of you are familiar with the GLEAM platform? We have no GLEAMpers in the room? Oh, I'm touched. Okay, I wanted to really say thank you to all of you that were GLEAMpers in the room because this was a beautiful opportunity for young investigators to participate, and we sent this to all the people in CHESS, ATS, European societies, all over the world, and we have this point prevalent study that was the GLEAM, the Global Initiative of MRSA Pneumonia. The original paper was published by my colleague, Estefano Liberti, in the group of investigators that happened to be in Austin several years ago, and we came out of the room after several experts talked about community acquired pneumonia and the term HCAP, and then we said, this cannot be right. We cannot be having these high resistant rates of these multi-drug resistant pathogens all over the world. It must be something that we could do about it, and we created this study. It was in Starbucks across the street in the Austin Convention Center. Four little guys, two from Colombia, two from Italy, suggested that we needed to do a point prevalent study of adult patients with hospitalized with community acquired pneumonia, and what we did was we took a picture of everyone that was hospitalized with community acquired pneumonia in a 24-hour period. So we asked you guys to go to your hospital in your bed and say, do you have pneumonia? Yes? Would you like to participate in this study? And then with your phone, exactly at bedside, you could ask all the risk factors and check for the microbiology on these patients and enter the data, and we all got the data and analyzed the data. So the results on this study that I'm going to show you is based on that platform, and what we invited young investigators to be part of this thing, because the only thing that you needed to do was request for your own ethical approval at your institutions. So what we found was that these are the common variables and characteristics among patients with COPD, and the one on the right represents the most statistically significant difference that are there. The majority of the patients were older, 72 years old, and you see that we asked questions about their respiratory comorbidities, non-respiratory comorbidities, chronic treatments. You see that 46% of the patients had in-health corticosteroids at the time of presentation. The majority were male. They had prior contact infections. You see prior pseudomonas aeruginosa was only positive in 5%, but this prior contact with the healthcare, with the low respiratory tract infections in the past 12 months, hospitalizations in the past 12 months, having received IV antibiotics or oral antibiotics, happened to be in about one-third to half of the patients. And some of the patients, one-third, had a severe community acquired pneumonia according to the need of mechanical ventilation. When we look at the microbiology, guess what? The number one group of pathogens were nothing. We could not find anything in 69% of the patients. So in patients with community acquired pneumonia, around the world, 222 centers, 54 participating countries enrolled almost 3,000 patients around the world at just one point in time that was repeated four times. The majority of the patients around the world had no diagnosis identifiable of a pathogen. When we identified the pathogens, the gram-negative bacteria was number one, pneumococcus was number two, pseudomonas aeruginosa in patients with COPD was number three, H. flu, and you see the others. When we account for 69 plus the 26 that we identified in this group, we have 95% of the patients. So if you just focus on these four remarkable pathogens, gram-negative bacteria as a group, pneumococcus, pseudomonas aeruginosa, haemophilus influenza. So the next part of the question was, okay, which risk factor is associated with this specific group of pathogens? And don't cry on this slide, but what I want to show you is look at all the risk factors on the bottom. It says in health corticosteroids here, for example, and then steroids, IV steroids or oral steroids, systemic steroids, homooxygen, priorestabilococcus aureus, and here in gray is no pathogen, and in green is pseudomonas aeruginosa. So every planet is a pathogen, okay? So pseudomonas aeruginosa is a single planet. And when you see this broken line, it was a statistically significant association, but only in the bivariate analysis. When you see the straight line, there was a multivariate analysis as association of that pathogen. The story is that there are only one pathogen that was associated independently with the risk of having this risk factors were independently associated with that pathogen. All the others were just associated, but only in the bivariate analysis. So the people have an idea, no? Yes, when they see pseudomonas, this is happening in the patients with community acquired pneumonia presentation. So what we did with this analysis was we did a multivariate analysis specifically assessing what were the odds, the risk factors associated with pseudomonas aeruginosa community acquired pneumonia among the patients that have COPD. And you notice that here we only have three variables. Prior pseudomonas aeruginosa, 14 times more likely to have another event of pseudomonas aeruginosa. Hospitalizations, almost four times, and bronchiectasis, three times. So we just have a very, very nice session about bronchiectasis in the prior session. So what we did here was a decision tree analysis. We put these three variables together and prioritized them among those that happen very frequently. Hospitalization in the past 12 months, bronchiectasis in 10% of the patients, and the prior pseudomonas aeruginosa. And you see here, for example, patient with COPD that was hospitalized in the past 12 months, look at the green, pseudomonas, that had bronchiectasis, it jumps to 40%, that had prior pseudomonas aeruginosa is almost 70% of the patients. So if you have these three factors in one individual, this was likely that this patient was going to have pseudomonas aeruginosa. Look at on the contrary, patient with COPD, not hospitalized, not bronchiectasis, and not prior pseudomonas aeruginosa, the likelihood is zero or close to less than one. So very, very tiny possibility that you will have pseudomonas aeruginosa. So how will this be helpful for us? Guys, we do have some universities and some settings where the rapid testing could be available immediately. But in the majority of the settings, we rely still on the risk factors. And we need to decide what antibiotics are we going to use at the time of presentation empirically to cover for that patient. Therefore, if nothing of these factors were present, the likelihood that pseudomonas aeruginosa was there was very low. But in contrast, you see that if I missed the bronchiectasis or the prior pseudomonas aeruginosa, I will be in trouble because it's likely that this patient will have bronchiectasis. So what we did was generating a scoring system. We provided in the prior pseudomonas aeruginosa, we gave three points. Hospitalization in the 12 months prior was one point, and bronchiectasis was one point. And we call it pseudomonas aeruginosa score for COPD. If you see here, there is, if none of these points are present, zero or one, the likelihood of pseudomonas aeruginosa are really low. If the patient had two or more, look at the jump on pseudomonas aeruginosa. And it was a modest representation on the ROC curve. So we said, okay, what are we going to do with this? Who cares about having a past COPD score if the people might not use it? So what we need to know is, how are the people right now utilizing antibiotics at their institutions? So this is the data from actual empiric anti-pseudomonas antibiotic use for COPD patients. In gray, no anti-pseudomonas antibiotics. And the other ones are non-conventional anti-pseudomonas, fluoroquinolones alone, anti-pseudomonas betalactam alone, or combination therapy. You notice that a lot of the patients, two-thirds to almost 80% of the patients are receiving antibiotics against pseudomonas. So I apologize when I ask you this question, but no one elevated your hand saying that at your institution, when you receive a patient with COPD, no one thinks about pseudomonas. These people are thinking a lot. And they're utilizing antibiotics against pseudomonas in the vast majority of the patients. Second, when we look at the past COPD score, you see that among the ones that had the most scores, the people were utilizing a lot of antibiotics. That's good. But people that have no score or one point were still using on half of the patients or two-thirds of the patients. So then we said, okay, what can we do to implement a change in practice that could potentially be looked at as green, correct, no use of anti-pseudomonas. Like you guys, you didn't think about pseudomonas. You are not going to cover a pseudomonas. Most likely you are going to do a cephalosporin, third-generation cephalosporin, plus a macrolide. Correct use, 39%. 5.4%, correct use of anti-pseudomonas antibiotics. You got it. Okay, you put the antibiotic and you found that pseudomonas. In 1.2%, undertreatment, oh my goodness, pseudomonas was there in 1% of the patients. And overtreatment, 54% of the patients were getting extra antibiotics against pseudomonas aeruginosa. If we apply the past COPD score in this population, we could convert. The correct use could go much higher because pseudomonas is not there. The correct use against pseudomonas will be 3.3%. The undertreatment grows from 1% to 3%, and the overtreatment, it can drop significantly to 6%. So what does it mean? The negative predicted value of not having prior hospitalizations, not having bronchiectasis, and not having prior pseudomonas aeruginosa gives you a lot of possibilities that you will not need anti-pseudomonas coverage. I don't know about your institution, but it looks like PEEP-Tazo. I don't know what they gave us with PEEP-Tazo, but everyone uses PEEP-Tazo for everything. And if we don't have PEEP-Tazo, we use FFP no matter what. But we do not think about other alternatives that may not cover pseudomonas. So what we think we provide to you is an alternative, only in patients with COPD. I'm not trying to extrapolate this to every single patient that do not have this, because as you notice, we walk you through that thing. From the COPD perspective, COPD patients with community acquired pneumonia represent a differential microbiological profile with unique risk factors. The decision to cover against pseudomonas is critical among COPD patients and is associated with antibiotic overtreatment. And we think that this COPD scoring system can help you unless you have a rapid testing to decide when to use antibiotics. Very briefly, I'm going to talk to you about the second highest specific group of patients with community acquired pneumonia. By definition, the immunocompromised host has been removed out of the guidance. So every time you see an immunocompromised host, you will not think that this patient has community acquired pneumonia. This is why I call it pneumonia in immunocompromised patients, in immunosuppressed patients. In this study that several of our attendees were part of our group with Julio Ramirez, we did a consensus statement regarding the initial strategies. So this guideline was published in CHEST, and this is an alternative to decide how to manage patients that have immunocompromised status that present with pneumonia. You see the different diagnostic characteristics here in the references to support this thing. In our group with the GLEAM, we look at what was the prevalence of these immunocompromised conditions with patients presenting with community acquired pneumonia, and it was almost 20%. So one out of five patients may present with an immunocompromised condition that is any of this among your patients that present with pneumonia. What we did here is we defined two groups of pathogens, the one that we call the core pathogens and the non-core pathogens. The core pathogens are the same bugs that we see in patients with community acquired pneumonia, pneumococcus, haemophilus influenzae, Legionella, and the viruses. And we compared these among the non-core pathogens that we know as opportunistic pathogens, patients that happen to have highly resistant bacteria or some bacteria that we don't see regularly, non-tuberculous mycobacteria or MTV, certain viruses like CMV or herpes simplex or varicella, some of the fungi like PJP, and the parasites. Therefore, what we wanted to do with this is this document, it was an alternative to give a treatment for these patients that happen to have certain risk factors with those conditions. The follow-up paper, the one that I want to discuss today, is the one that was published this year that is Immunocompromised Host Pneumonia Definitions and Diagnostic Criteria. And we moved from having a big list of conditions to move to the host per se, trying to identify what is happening in the host that make it immunosuppressed, that make it vulnerable to have certain pathogens and certain conditions like that. And here it defines an algorithm that goes into two groups. I would like to recognize that this was a big effort led by Dr. Chenk, Dr. Crothers, and Scott Evans, and some of my colleagues here, Dr. Charles de la Cruz, Dr. Barbara Jones, and many others like Bob Dixon that are participating in this nice workshop. So what I want you to take out of this message and that can change your practice is, guys, we need to do a much better job identifying which patient is immunocompromised and which one is not. If we start from we know the immune defect, we have patients come with symptoms, we need to find a new or worsening infiltrate that by the group we said, if you have nothing on your X-ray or CT scan, you do not have pneumonia. So you need to have an image that showed pneumonia. Second, you need to have an identification of a pathogen. And if this pathogen is one of those that I showed you in the past two lists, you will have a patient with immunocompromised pneumonia. And here, we would like to compare it with those hosts that we don't have a known immune defect. But the majority of the time, when we do not know the immune status, what happens is we have to rely on see a patient with pneumonia, do the cultures, and then all the studies end up, oh, pneumocystis, this patient must be immunocompromised. So we go backwards in time to try to identify what was the immunological factor that happened in that patient in order for us to identify that immunocompromised status. So this is what this algorithm is about, the host without immune defect. We identified this pathogen that is usually opportunistic, and then we need to identify whether there is a new or worsening pulmonary infiltrate, and we evaluate the immune system. These are the three alternatives divided in innate immunity and adaptive immunity that we try to really sell to you guys as the new alternative to really assess the host with immunocompromised, that presents immunocompromised with pneumonia. And here is the list of recommendations. I want you to remember this thing because there will be a test at the end. No, I'm just kidding. But we have the three arms, innate immunity, cellular immunity, and humoral immunity. The majority of the test goes from a CBC with a differential diagnosis, for example, absolute lymphocyte count. It could be really depressed, very likely, like what we saw in patients with COVID-19. These patients have an immunological status that were present. A peripheral blood cytometry recommended for assessment of these patients, HIV test, bone marrow you see that is refined for patients, especially when you're thinking about a malignancy or a lineage failure. You see this genetic screening for primary immunodeficiency or anti-cytokine autoantibodies, immunosuppression drug concentrations, especially for cellular immunity and humoral immunity with B cell deficiency or dysfunction, and immunoglobulin pattern concentrations among these patients, particularly those that have an antibody deficiency. And also a response on immunoglobulins after vaccination. And you have probably seen what happens with COVID-19. We give these patients a vaccination, but some of them end up in the hospital. Why? Because their immune system was low at the beginning. They were not able to mount any immune response. The B cells were depressed, and they were not able to have an effective antibody response, and the T cells might have also been affected. And this is why we needed to really assess the response to vaccination. How can we collect these samples? This is a nice representation of the amount of fluid that you will need, 3 to 5 mLs, for pathology, cytopathology, and hematology, virology, 5 mLs of BAL, and the microbiology studies will require a higher volume of 10 milliliters. And you see the list of the possibilities of all the different culture and identification tests that we would like to support. So with this, I'm just going to walk you through the different lists of bacteria that have been recognized according to the literature that were matched with the type of the defect. You see the humoral immunity and the typical conditions that many of these patients have. So we switched the pattern from going to one disease, you just have cellular immunity, to the host have different lines of immune defects. This is why you need to go a little bit broader, because they may be affected at the same time some humoral immunity with B cell-mediated and antibody deficiency, and some of them have combination of these factors, like cellular immunity. This is the list of the viruses, and this is the list of fungi. And finally, and I will have all this for you in the paper with the citation. So with this, I would like to conclude that the host immune defense disorder is a systemic process, and that we need to change our paradigm to go from the list of diseases and try to really say that you're immunocompromised to looking at the host and why this patient is likely to have these special pathogens, and why these pathogens maybe facilitated the difficulty in clearance and killing and the detection. The definition of pneumonia has been revised with a focus on the host response, and the pathogen identification is key to really select the appropriate therapy, as we publish in the Chess Journal. With this, I would like to finish with a quote saying alone we can do so little, together we can do so much. You notice the list of investigators that work with us. This is a team effort. And we need you all. And we would be more than happy to really count newer investigators that would like to come and work with us and try to answer these key questions that are really impacting the care of our patients. Thank you so much for your attention. I suspect you have questions on the tip of your tongue, but we're going to hold them to the end. In my excitement to introduce Dr. Restrepo, I realized that I didn't introduce my own self. And I'm PJ McShane. I've been honored to be the associate editor of chest infections for chest in the last year. And so I am now going to transition to our next speaker who I have a special stake in this game because I do a lot of NTM at Tyler. And I know that Dr. Charles Daly is an undeniable world expert in the nontuberculous mycobacterial field. As you probably know, we're using drugs that are very old to treat nontuberculous mycobacteria. And there has been, you know, a big sort of exciting interest from pharmaceutical companies in this field to make new drugs. The FDA has sort of changed what they consider to be treatment success. Instead of culture conversion, they want to see patient-reported outcomes. And so there has been a lot of effort and investigation into making a patient-reported outcome specific for nontuberculous mycobacteria. I am especially excited to hear Dr. Daly's take on some of this research because no one knows an NTM like he does. And so he is an expert in caring for these patients, and I'm excited to hear how he reconciles the patient-reported outcomes with his clinical experience. Oh, Dr. Daly is at the National Jewish Professor of Medicine in Pulmonary Disease. Thank you, Dr. McShane, and thanks to the leadership for the invitation. Now, I have to figure out how to get out of this system. Oh, let me. Okay. So we're going to make a big change, a switch here, because this is a very different topic. I was asked to discuss this, and I am a poor stand-in for the lead author, which is Emily Hinkle, who could not be here at the meeting. This was an article that was published this year in CHESS, and it was about patient-reported symptoms and health-related quality of life during the first six months of treatment for my pulmonary disease. And you may say, how does this impact my practice? Well, it's not going to today, but it could significantly do so in the future. You know who I am? If you've been to any other sessions, you know my disclosures. So first, what's the problem? Well, the problem is NTM. As you all know, there's a high prevalence of pulmonary NTM, and it seems to be increasing in many countries in the world. The prevalence tends to be higher in older age groups and in women. NTM prevalence is higher than TB in many countries, including the U.S. and Canada. By far, the greatest risk factor for pulmonary NTM is brachiectasis, and the disease does appear to be increasing. This comes from Kevin Winthrop and Emily, looking at a National Managed Care Claims Database. It's quite a large one, 27 million people annually. It's actually the Optum database, for those familiar with this. This is the prevalence of pulmonary NTM from 2008 to 2015, and you can see it's pretty dramatic increases. The top line are those who are 65 years of age and older. So one thing you can see very clearly, higher rates in older individuals with significant increase in prevalence over time. The line right below that are those enrolled in Medicare. So they're both proxies for age. The third line from the top are women. So we see higher rates in women than men, and we can see, again, significant increases. So this is something that all pulmonologists and ID docs are going to be seeing more and more of, because these increases have been going on now for approximately 20 years. So these are the recommendations on how we should treat our patients who have MAC. We have two phenotypes, nodular bronchiectatic disease and cavitary disease. For nodular bronchiectatic disease, we recommend three drugs, macrolide-based, azithromycin preferred, and it can be administered three times weekly, but if you want, you can give it daily. For cavitary disease, we recommend three or more drugs. That fourth drug would be usually parenteral amikacin. Some places it could be streptomycin. Here we give the oral drugs daily, but you can give the aminoglycoside three times a week. And regardless of the phenotype, once we embark on treatment, we're trying to get to the point of 12 months of culture negativity. This can be quite difficult to do. This is a long course of therapy. There are very frequent adverse effects that are associated with it. The other thing is the outcomes with this approach are suboptimal. For macrolide-susceptible non-cavitary disease, the range in culture conversion is about 70 to 80%. For cavitary disease, that tends to fall. It's been as low as 50% in one report. And once you cure the patient, or you think you have, microbiological recurrence is very common. 25 to 48% of people recur. That's after a year of being culture negative. And the reinfection rate is quite high. So most of those recurrences, or 46 to 75%, are reinfection. Now if you have the unlucky patient who has an isolate that is resistant to macrolide, things are very different. David Griffith published years ago this report showing that if a patient had macrolide-resistant MAC, they did not have surgical resection, they did not receive a prolonged course of an aminoglycoside. Culture conversion occurred in only 5% of these people. I mean, it looked to be incurable. But we showed in Korea, with highly selected patients with macrolide resistance who did have surgical resection and aminoglycoside use, we were able to increase it to 15%. But in the original article from Dave, he showed that if patients had surgical resection and 6 or more months of parental aminoglycoside, culture conversion could be raised to 80% again. But at great cost to the patient. They had to undergo thoracic surgery and over 6 months of aminoglycoside use, certainly there's going to be toxicity related to that. So we need help. Our patients need help. These are not the outcomes that we really want and would strive to have. And there's good news, I think. Things are coming. I mentioned this at a couple of talks at this meeting. This is really the drug pipeline that I'm aware of. Phase 1 through phase 3 drugs for NTM. Many of these have MAC activity. Some of them have M obsesses. But in the phase 2, where you're seeing these single drugs, to move them to phase 3, something has to happen. And Dr. McShane alluded to this. You're not going to get a drug approved in the United States based on culture conversion. It's going to require something else. And the something else are here. To get a drug approved, and this is congressionally mandated to the FDA, the drug has to make the patient feel better, function better, or survive longer. Culture conversion doesn't do it. You and I, that's the goal. Because then our patients go, I want to convert. Because I know once I convert, I have 12 more months of therapy. That is not from a regulatory perspective what is considered important. So when we say we want to make the patient feel better, there are barriers to doing that in the NTM world, which is we don't have standardized instruments, and there's a lot of variability from asymptomatic to wheelchair in our patients with MAC. So one of the things we would have to do to overcome these barriers is to develop patient reported outcome instruments. In terms of functions, we really lack any standardized measure. Unfortunately, the six minute walk has not been one that has worked in clinical trials. Pulmonary function testing usually doesn't change in our non-CF patients very much when we treat them for MAC. For us to move forward with this, then we would have to identify some kind of functional marker. We and others are trying to do, using cough monitors, Fitbits, we're trying to find ways to measure increased activity during the course of a trial. But we don't have any of them validated yet. And then survival. Well, our patients with MAC don't die during the two years of a trial. So mortality is not going to work either. So those drugs that I showed you in phase two, they need help. They need something to allow us to get them approved. And unfortunately, culture conversion is not going to be it. There was just this past month, released by the FDA, updated guidance to industry regarding this. So if you're a company and you have a new compound, they're telling you this is what you need to do to get a drug approved for NTM. So I'll just quote. The primary efficacy endpoint should be based on clinical outcomes such as patient reported outcomes or PROs, instruments assessing symptoms of NTM pulmonary disease, and that microbiologic endpoints such as sputum culture conversion are not generally recommended alone as primary endpoints. So this is the challenge. So we need a patient reported outcome instrument. But what is a patient reported outcome? The U.S. FDA, and there are other definitions for this, define a PRO as any report of the status of a patient's health condition that comes directly from the patient without interpretation of the patient's response by a clinician or anyone else. And that's a very important concept. PROs typically measure all of these kind of things. The things you and I would think about is they're going to measure symptoms. But they can also measure function. Quality of life is also a very important thing to consider in a PRO. And when you think, you know, what is a PRO? How does this come about? Well, basically you take a series of what are called items. And items in the clinical setting really are questions and answers to the patient. And they group into what are called domains. So you'll hear me use the word domain. So that domain could be quality of life, it could be symptoms, it could be health perceptions, it could be different things depending on what you're trying to achieve with your tool. But this is a reiterative process. You know, FDA is saying for each company, we don't have a validated instrument. Go do your own. What that means is that they're delaying trials to develop PROs so that they can get their drug approved because we don't have anything that the FDA recommends. Generally, you start at some point. You create the framework. And you usually bring in experts. I've been fortunate enough to work on four or five of these studies. And then you solicit patient input at some point. And then you continue this process of testing and reevaluating until eventually you get to the process of a validated instrument. But this can take years to do this. So what about quality of life? Because that's one of the things that could be measured in a PRO. And as you know, in medicine right now, it's often used in research. So quality of life is a concept that aims to capture the well-being of a person. It could be a population as well. There's one that we're going to talk about, though, is called the QOLB. So this is a quality of life instrument specifically tailored to bronchiectasis. It's self-administered. And it reports on a number of different things in what are eight scales or domains. These are some. Two of the most important are respiratory symptoms. I mean, this is how we think as clinicians. If you have a cough, I treat you. I'd like to see your cough get better. Physical functioning, I'd like to see when I treat you that your energy is improved, you function better. And vitality, because vitality is energy. It's fatigue. So these are very common things that we see in our NTM patients. So it makes sense to focus on those. Now this instrument has been validated in bronchiectasis. And it's been partially validated in MAC pulmonary disease, but not to the point that we can use it to get a drug approved. Enter MAC 2 versus 3. This is a randomized trial that is occurring in the U.S. It's comparing 2 versus 3 drugs for the treatment of MAC. Patients are enrolled who meet ATS diagnostic criteria, so at least two positive cultures or one BAL. It has excluded cystic fibrosis patients and people with cavitary disease. So the patients are randomized to get two drugs, azithromycin and ethambutol, or three drugs, azithromycin, ethambutol, and rifampin. And during the course of this study, which again is enrolling now, and you can see the sites that are involved, they are administered the QOLB. So this is how we can think. If we administer one at baseline and then during the course of treatment, does it work? Does it differentiate them from another group, or can we show that they're improving in some statistically significant way? So now enter the article published by EMILY. Patient reported symptom and health-related quality of life validation and responsiveness during the first six months of treatment for MAC abium complex pulmonary disease. This is a first. So this study nested, right, the data or pulled the data that was occurring in this randomized trial. Two cohorts were defined. One was the cross-sectional cohort. That's everyone who got the baseline QOLB survey. And then there was a longitudinal and very important cohort. These are the ones who got repeated ones along the way as of June 8, 2022. The way this works in this QOLB is it's a rating scale, zero to 100. A hundred is perfect. Zero is the worst. And so patients, as they go through the different questions, will rate themselves from zero to 100. The data were pooled, and this will be a weakness, right, because this is an ongoing randomized trial. So we can't know which arm they're in right now. So this is pooling all the data. So the first thing to note is the size of the group. The cross-sectional group, you can see here, 228 patients. The longitudinal group was 144. Pretty much like most of our patients that we see in clinic, mostly women, older population. About a quarter of them were diagnosed with BAL alone. Almost all had bronchiectasis. Now, that's important because this is a bronchiectasis tool that we're using in MAC patients. And it probably wouldn't work as well in a different group. Small proportion has COPD or asthma, but it wasn't their primary issue, bronchiectasis was. So really no significant differences between the longitudinal and the cross-sectional group. So as we go through, to validate an instrument, there's certain things we have to satisfy. One is called the psychometric properties of these domains, of the QALB. And in addition, there's what's called the NTM module, which specifically looks at NTM symptoms. And what we see here is what's called the floor effect and the ceiling effect. What that means is, how many people are saying zero, because if you have zero, or how many people are saying 100%, that's not a good instrument. You're not going to have much responsiveness within that if everyone's on the ends. So you don't want to see floor effect if you can, and you don't want to see a ceiling effect. So you don't really see floor effect with these various domains that we'll function, that we'll look at, the respiratory symptoms, functioning, vitality, health perception. And then again, I mentioned the NTM symptoms. The only place where there was a little bit of a ceiling effect was with physical functioning. So 16.7% said 100%, they're perfect. So I can't show that they could get better. So that's what you don't want. You don't want everyone saying I'm fine, because then you can't measure improvement. So you want an instrument that doesn't have floor and ceiling effects to any great degree. The other thing you want is internal consistency and reliability, and that's measured by this Cronbach score. And this did very well, because above .65 is usually considered a reliable instrument. So, so far, looking at the way this test works from its properties, this is looking good. The next thing is what's called construct validity. You need to compare it to something that makes sense. So you would expect that with certain characteristics, in this case, AFB smear result, or FEV1, or FEC predicted, that there would be some correlation with how the patient feels now, how they improve, and what happens with these. And as it turns out, we looked at the AFB smear results, and there was no difference. Whether you were smear negative or positive, the scores that the patient rated themselves in these domains were the same. But it turns out with FEV1, in respiratory symptoms and physical functioning, you can see they're bolded. There were statistically significant difference between those who had over 70% predicted and less. And as you would predict, those less didn't feel as well. And then for the health perception, we saw also a change, the change in the score with FEC was statistically significant. But the two that stand out here, at least in terms of FEV1 and FEC, are respiratory symptoms and physical functioning. Then the next piece, really, is what happens over time. Because this is where the FDA, this is what they care about. Do you have something that can measure change over time? And in here, again, bolded are those that are significant. So if we look at respiratory symptoms, we look at physical functioning, we look at three months, there's a significant change in the score, and there's also significant change at six months. These are what are called minimal important differences. What you'd like to see is that that change is above those numbers. Those are statistically derived, and we can see that with one of these, respiratory symptoms, 7.5, 7.8, sorry, very touchy, is above the minimal important difference. So we've identified that we have something that we can measure, and we can see as soon as three months a difference. Well, that's what we want. We don't want to treat someone for a year to figure out if it's working. If we have something like this, believe it or not, if the respiratory symptom domain changes in three months statistically different between two arms, you have a drug approved. You don't have to wait forever. So this is a huge thing for the development of new drugs for NTM. Sorry. So there are some strengths and weaknesses to this. It's not a perfect study. I mean, we use this instrument in patients with bronchiectasis. That's a strength, because we had 90% had bronchiectasis. And we had modern psychometric techniques. Alexander Quintner, who developed QOLB, was part of this study and was instrumental in helping develop and do the analysis. And it was longitudinal, and that takes time, takes money. So this was a very strong component. It is not fully validated in MACPD. So this is what this process was to try to get to that point. And we were not able to compare to microbiologic or radiographic response, because as I said, the study is still ongoing. More on that in just a second. So I would conclude saying that this instrument, the respiratory symptom domain, is a valid and responsive measurement in patients with MAC pulmonary disease. And this is the kind of thing that may lead drug development forward. We did see significant differences in the respiratory symptom scales that exceeded that statistical minimal important difference. Again, that is a major hurdle to try to get to when developing a PRO. The physical functioning, vitality, health perception, and the MTM symptoms, they showed good psychometric properties, but they were not able to, we could not see a jump above that meaningful minimal important difference. So I want to just note that soon after this was published, something else was published. And this is the ARISE trial topline results. This is a randomized, double-blind, placebo-controlled, active-comparator, multi-center study that was aimed to validate a PRO for MAC. And this was specifically a trial that randomized patients to, who were treatment naive, to amikacin liposome inhalation solution plus background therapy to background therapy with a placebo instead of the amikacin. This was a six-month trial, and it was specifically aimed to figure out if the PRO could be validated. And there were multiple different instruments that were evaluated, but one of them was the QOLB. And in this, again, where people were randomized one-to-one for six months, the topline results, this was published in September 5th, the Allistree patients performed better, I'm sorry, better than those in the comparator arm, as measured by this instrument. And this was a very clean result. It is so clean that INSMED will be discussing with FDA to see if they can use this in what's called their Encore trial, which is a 12-month-long study to try to get ALICE registered for the treatment of treatment naive MAC. So between our study and this study, it looks like we might be on our way in that you might finally have an instrument that will allow drug development to proceed forward. Just a plug, we are still enrolling in MAC 2 versus 3. We have 350 patients enrolled, less than 150 to go. These are the sites throughout the U.S. You can find this at clinicaltrials.gov, or we have a webpage. So if you have a patient with treatment naive MAC, please refer them to us because we would love to figure out, do you even need that third drug, or is two enough? So thank you very much.

chest infections

pneumonia

COPD

immunocompromised patients

antimicrobial therapy

pseudomonas aeruginosa

patient-reported outcomes

MAC pulmonary disease

Chronic Obstructive Pulmonary Disease