 It's been recap to Thanksgiving, one moment of classes, and things that have been a little bit slower. The excitement that I feel having Dr. Psycho-Satran are impacted to the UCI, overwhelms that kind of vaccine that we need to talk to, I don't know. But so I'm welcome back, and I'm delighted to introduce Dr. Psycho-Satran, who earned her doctorate and a PhD four years ago in the Environmental Health Science and Policy Program with an emphasis in epidemiology and public health. She studied genetic and environmental factors involved in ill breast cancer risk. And the publication from that work was hailed by many in the news media. Because it's such a rare condition, I'm very little with money, but I have to manage it. And many things have different ways of thinking about health and cancer than women. So there's a lot to do on that topic by itself. But I walk with Dr. Boyd-Anton Cooper and others in that group, led to make a disability. Also, she grinds it and then walks for what she's called now about technology operations, including planting. And I, a neighbor that makes botox, which is also famous. She currently serves as a consultant for Genentech. And her research areas include quality of life, patient-reported health outcomes, claims, and database analysis, and surveillance. Surveillance and epidemiology are a test result. We see our Medicare analysis from Kimi, and from her and Melanoma. So today, she's going to talk to us about cancer that affects the elderly. Meal makes to elderly, but the rest is slowly. But in that way, it really contributes to the nature of this botox function and its ability. So thank you again for accepting that invitation. And we'll stay for lunch. Thank you for that nice introduction. So as Deli was saying, I was an alumni here now. Deli was actually one of my very favorite professors. Oh, same. Same. So today I'm going to talk to you about a study that the title is Treatment Patterns and Outcomes in Elderly Patients Diagnosed with Project Lymphocytic Rheumatism. And the study was conducted using data from the Sierra Medicare. Well, it's a Sierra database of cancer registry linked to the Medicare claim to patients over age 60. So the results that I'll present to you has actually been previously presented as a poster at IWCL last year. And earlier this year was presented as asthma. And currently the manuscript for this work is a review network. So today I'll take you, I'll give you some brief overview of the CLO just to kind of get everyone to know about this. Although the objective of the study described to you this year Medicare data set, I will go over the methods of C-stable analyses used before some results and then we'll write down the limitations on the summary and conclusion. So epidemiology of CLL. So CLL is actually one of four subtypes of the group. And it affects the or to cancer of the white blood cells in the bone. It's the most common type of leukemia happening for about a third of all the human cases. Americans Cancer Society predicts that this year there will be roughly 15,000 new cases. And the prevalence is about 100,000 patients living with this disease. Who are actually in remission or are currently treated. So as I've already alluded, adults are primarily affected over adults. The mean age is around 70 to 78. Over 75% of the patients are over the age of it. And you see that risk is actually slightly higher in men. So with primal lymphocytic leukemia there's actually two different types. Delibensia block type of type that's very in the range of slow growing. And in fact a lot of patients are never even treated for it in their own lives. Without the procedure treatment they may actually die of some other on the way with the cold on their head. And then there's the other type that's really perhaps progressive and progressive. That would require patients to be treated by the way over the next three months. And unfortunately patients might circumfer that sort of progressive disease within the population. So disease stage is the main predictor of survival for probably the most of them. As well as several cancers. But a physician would usually consider other factors when deciding what sort of treatment they would want for these patients. And they would look at other things. And then overall all the co-ordination is their age and gender or different on the level of symptoms. These people are asymptomatic and diagnosed as an, and is actually an incidental on the way when someone goes in for an office visit having to do blood tests. It comes back with the pain towards them. And then there's a minor organization to have asymptomatic patients and go ahead and get out of there. So these are not the same white blood cells that will go out to someone who simply has an infection? So it is, so the thing is they go up but they don't work properly because they're cancerous. So you'll have more white blood cells you know that just before infection find themselves white. They're not working as involved with some of the patients. So current treatment guidelines recommend watchful waiting. So those are the patients who are never treated as doctors. Just pretty much monitor them with a lab test of regular blood tests and physical exams to see how the treatment is for all of their disorders. And that's typically for those who are diagnosed at a very early stage. And then once the patients are non-verbal, and you know there's some sort of indication that they're becoming symptomatic it will affect their quality of life. That's when they start considering treatment. And there's currently three different types of treatment that are considered. There's chemotherapy, the traditional cell, there you know they're the ideal one. Some of the common ones are the therapy, psycho-phosphide, and then we have the monoclonal additive body, which I talked a little bit about earlier on. And these are the proteins that are very abusive to your body and they target the cancer cells so that it is on the cancer cells. So it binds to it and it's somehow destroyed and they have to make medicals and then destroy the cells. And the current MPF group drug, the CR, is very toxic. And the studies come to an end. And then we also have combination of opioids, serum, and the other kind of combination. There's a small group of patients who might receive stem cell transplants, but because it's a disease of all patients, stem cell transplants are usually called in the younger patients and, you know, patients who have a body of better output. Question? Sure. So, those are not the same stem cells that are on the version. They're not on the brownie's stem cells. Where do they get from? So it would come from another group. So they're everybody's outcomes? Yeah. So the rationale for conducting stem cells. So far, a lot of the data that we have available on alkyne and alkyne population come from, or not really an alkyne population, but outcomes with CLL generally are from clinical trials. There's hardly a real low data out there. So given that CLL is a disease of all patients, you know, likely to be over the age of six to five, those types of patients are generally underrepresented in clinical trials. So, you know, the goal of this study was to see what are the real role outcomes in CLL patients, general practices of the disease. And that's one of the reasons why we selected the C or B here, I think, to make a basic process. So the objective of this study was to characterize all of the CLL patients of their demographic and clinical characteristics, and also to look at treatment patterns, their initiatives, the interviews therapies, to know the toxin, and then have confirmation numbers, and then finally to look at the outcomes, stride out by the difference. So we used the CR-medicine data. This, the CR part of the data is the CR registry that collects and publishes cancer records and survival data from various registries throughout the U.S., and it covers about 26% of what it represents, about 26% of what it does for others. And the CR-medicine combines this cancer-servingless data on the CR that they're including from Medicare for part A and part B services for patients that are over 65. So in the data, patients over 65, 97% of them are eligible for Medicare, and the actual rate is 93% of the patients who use it. So during the time of the study, you really looked at patients' guide and looked at the CR with CLL from 1999 to 2005, and looked at Medicare players from 1999 all the way to 2000. To identify treatment, that's where we would look at their Medicare claims. And the claims, claims coordination is very complicated, but briefly, we'll look at the part B of Medicare data. There are specific codes for general treatment for their administration. Specific codes for the type of course. So we'll do some coding and pick up these treatments, and we classify patients into three different treatment code words for this study. So we have the chemo of the room, then we have the retoxicab, monotherapy of the retoxicab of the room, and then we have the oral history. And we do, for the stimulation of the event, patients are required to survive 60 days after their initial patient to be classified into one of the treatment groups. And now it's the kind of introduction of something called mortal time bias or survival treatment selection bias. I don't know if you guys have been exposed to the different types of bias there, but so I'll try to explain this now. And one of the things to, I learned classical acting, going into pharmacological acting, you know, there's some new things that we pick up from all of them. So this is one of, this is an patient that's pretty typical in pharmacology studies of treatment where biases in three of those patients didn't survive long enough to see the treatment or to get the treatment benefit. So in a sense, the time between a patient initiates their treatment to the time of treatment that is sort of an immortal time where patients can die. And so what you might do on these days, is you want to make sure that your cause and effect are related to your time. Make sure that they didn't receive the treatment or to see how effective your survival is. And so one of the things that we do is you have to survive a certain period and make sure that you are really capturing that outcome that's associated with the patient. If we didn't do that, some studies that happened in the past, we've seen instances where the survival of the first was set very early on because patients were dropping out of the van. It would erroneously indicate that treatment on those two medical conditions. So this is one of the ways we did that. The reason for the 60 days, we consulted the clinical oncologist who was co-operating the study. They came up with this number based on prior research that's done as well as those six schedules in particular. One of the limitations of the day actually is Medicare doesn't reimburse for therapies. So a therapy like chlorhibisod, which used to be sort of a standard care or treatment procedure several years ago, is not available. However, more recently there's more kind of drugs that are more like a standard treatment, like food aerobics, like phosphoride. For an instance, I was often concerned most of the people like this would severely affect on the outcomes of this. But that's a really good question. Can I just make sure I understand the bias? Okay. So if the patient dies before the 60 days, you drop their phone number at the same time as this. Right. Giving the age group, I mean that means you're reducing the power of the state, if you lose it, you're out of it. So designing this study must somehow account for this, so that you don't lose enough power to find a good solution. Well, the good thing about the Sierra Medicare is that there were several thousand patients that we did with the drug. So more like an experiment criteria, you can get obviously to use power of the state. You know, you have forms that you can make without the solutions and everything. So, you know, it's a trade-off program. You can't, you know, we don't want to get into a situation, especially when we're looking at competitive drugs where you're going to just say something about it, someone else will show that it doesn't want to drop it. Okay, so this is a diagram of selection into the state of the world. So patients have to have a first primary CLL diagnosis between 1999 and 2005. They need to be at least 66 years of age and survive their month of diagnosis. So even though Medicare age is 65 or older, it's 66 because one of the other criteria is we need to be stimulating the study rules. Patients have to have 12 months of Medicare coverage for their diagnosis. The reason why people do that is to assess some baseline characteristics. So, more than weeks to make sure that they were treated for another cancer or a diagnosis or anything like that. So, essentially, that way they're going to be 66. They have to have part A to B coverage and no chemotherapy claims prior to their diagnosis. So the final summary was included 6,200. So we generated summary statistics to compare that graphic clinical characteristics between the treatment rules. Then we did some on a justice or vital analysis. We did some Captain Meyer curves to compare what the overall survival was. And we used the law-right test to test the differences between those curves. And then we did some of the all-day variants for a vital analysis using a Cox proportional hazardous regression model. And we used this to identify factors associated with risk of death and risk of death between the three treatment rules. Follow-up time, patients would follow them until they died. They enrolled in an HMO because HMO is the data that is not available in the data set. So they would have no claims if they had an HMO. So they were x, they were followed up until they were diagnosed to death. They enrolled in an HMO to develop a kind of a second primal tumor. The last days of their Medicare claims or the study average which in this case was December 31st, 2000. So some results to treatment patterns. So out of the 6430 patients, the majority of them are 68% for observation, so they weren't treated at all. These patients tended to be older, they had more diabetes, and they were likely to have earlier stage disease compared to the group of patients who didn't receive treatment. And that's intuitive. That's sort of what we expected. The patients who didn't receive treatment, that was 32% for more people. We had a lot of patients that were heavy in their tumor, 1,429 patients. And retosemite plus chemo had about 290 and 390 patients who treated with retosemite. So retosemite was actually part of the initial infuse therapy, 30% of the patients. So we also found that retosemite was increased over time throughout the course of the study. Now, it increased 10% to about 43% in 2005. And that's because, you know, as we do more clinical trials, I think that's the reason we also play an engine. Also retosemite became the gold standard for treatment. And we've been able to look very, very similar type of cancer. So it's been used more, it's shown to be very efficacious and effective. So looking at differences in demographic and different characteristics between people, the mean age of the entire cohort was second to the unit. Patients were more likely to be male, white, early stage and have fewer preferences. When we started looking at differences between the three different, which is about what chemo only and R plus chemo were pretty comparable when it came to age, clearly, at the stage. But the R-lock therapy seemed to be a different patient that was being treated. They were older, they had a broken woman, but they had earlier stages compared to female. One of the things that we don't hear for CLL is seer-medicare stages are available with stages typically available with seer, the seer data for many kinds of tumors. Probably more smaller too. But this cancer was not. So what we did, just for staining the analysis, we created this proxy variable based on a two-stated system, CLL. So there's the live staining system that's mostly used in the U.S. and the DNA, which is also used in different communities. But basically, to indicate that at the back stage, that's defined by the presence of an in-the-art prophesied in the U.S. blood vessel, prophesied in the U.S. blood platelets. And those two things indicate a patient has advanced diseases. And what we did is we looked up their claims and looked for a diagnosis of those two things. And we created this variable that we processed at the stage. And so we did essentially adjust practices and so forth. Really the big compounding variables come to some of our analysis. Looking at the unadjusted overall survival between the three regions, which we found at the back stage. So the top curve which Rituximab called here red is Rituximab, because chemo and blue is chemo. But essentially, we found that Rituximab had the highest overall survival cases unadjusted. So it was just very raw, including survival rates followed by Rituximab and chemo. So we hit survival as well. So we hit survival as well. We hit survival as well. 53 months of Rituximab. 52 months of Rituximab was chemo. And then 34 for Rituximab and the longer Rituximab was chemo. So when we did a long time very adjusted analysis, we found that it actually supported the unadjusted survival of Rituximab. We still saw that patients who were in Rituximab all along had a 40% overall risk of death than patients who were treated with Rituximab and patients who were treated with Rituximab plus chemo had a 25% overall risk of death compared to chemo as I just made for each gender-raised stage in Rituximab. So in general patients treated with Rituximab with Rituximab had a low risk of death compared to patients treated with chemo and we also identified our risk factors such as as age increased the risk of death increased patients with male gender black race before and by stage as the number of women increased their risk also the risk of death also increased. And now because in the previous slide that's adjusted for those who are not on any medication any treatment. This, yeah so this is unadjusted. This is an unadjusted survival practice. Is it a good survival? What are the side effects I mean I'm assuming the side effects are different for people they are and that's something we looked at it's in the manuscript I didn't mind getting to that in general you know there are different side effects you know we looked at race of hospitalization side effects you know we're still seeing in the real-world side effects it's already supported by this but in the real world we're still seeing the side effects with or without with or without with or without as I mentioned earlier we thought that the retoxemap only population was a very different patient population compared to the patients who were receiving to all of our questions and so as a sensitivity analysis we took out retoxemap a lot of retoxemap other patients from the process we thought that it was a really valid to compare the retoxemap to all of this good-rooting comparison of retoxemap was just a whole different type of patient in the first place so we took them out and the results were going to have changed it's still 25% full risk of death of care of people the risk factors are still the same so those are the results that I would go to like I said in the paper there was a lot of research but for the person with this presentation I thought it was available some of the limitations without results oral therapy for retoxemap mentioned earlier those are not included in the data and so we were unable to describe these concurrently retoxemap patients who received it before they actually felt retoxemap for example there might be a case where a patient relapsed and then they started their IV so their IV so that's something we're not able to account for but again just because the use of forerunner is much more about that and we don't see the reason why the use of it would be different between retoxemap you know it might just know that retoxemap is staged by using a diagnosis of retoxemap from the site of retoxemap so we were able to control for that but there are a lot of evaluations that are you know telling my comments about having treatment patients and chemotherapy so different side effects and advocacy you know but yeah it's just the best you do for what you have and for the other side so to summarize we found that the retoxemap of their population was over less likely to have AIDS disease and have better spinal which suggests that this is a group of patients who had less compared to the other two groups and we shouldn't really compare the patient with the disease and finally get unadjusted and adjusted to vital models we found that retoxemap with or without to or associated with improved outcomes we'll be able to just want to kind of disclose the study with co-authors from several different organizations to get this kind of perspective Questions? I'll start so it's conventional which says that like our technology factories of pharmaceutical companies don't work on don't invest a lot of money diseases that don't affect a lot of people don't have a force of segments like 15,000 people in approximate that disease in the U.S. I'm not and most maybe die after a few years of diagnosis so it just yeah I don't is this enough for companies to make money? Is this not good? Yes it's definitely not well a lot of these drugs are just several so the drug of patients were tested so you know there's several different indications like rheumatoid arthritis that it's the gold standard you know not there's a lot of different things being used for and these cancer patients are very sensitive because they were out of care system treat and manage these patients so you know you know they do get reimbursed and that's another part of you know just being able to see how all these different things come from the body so now people who study and they know how many how many you get reimbursed and coming up the comforts with the drug how many you get reimbursed how much more do they get there are all these economic models to say what the cost of patients what the quality of life that would be so you know it's not just this one little part that we do there's so many different things that are built that are built and developed and so very expensive and that's why you know a lot of these things maybe a fall-off one it's not that this is it's only rare in children it's not that it's possible rare is rare it's all over this type of stuff you can get there are others like me and others who are not this one is there's more value and your risk starts going back a little bit just a little bit some aluminum paper did you use for defining stages so we would so we would look for a diet and see a Medicare it doesn't it depends on the data that's in the data set that one so it doesn't give you the actual level it will tell you if there's a diagnosis of a you know what the site would do and if they were treated for it and it will give you a code for a treatment for it so we didn't look at the weapons but we didn't look for a diagnosis any other questions? okay thank you very much thank you