
Quality of Treatment Selection
11/14/24 • 25 min
Host Dr. Davide Soldato and Dr. Aaron Mitchell discuss the JCO article "Quality of Treatment Selection for Medicare Beneficiaries With Cancer"
TRANSCRIPT
Dr. Davide Soldato: Hello and welcome to JCO After Hours, the podcast where we sit down with authors from some of the latest articles published in the Journal of Clinical Oncology. I am your host, Dr. Davide Soldato, medical oncologist at Hospital San Martino in Genoa, Italy. Today, we are joined by JCO author Dr. Aaron Mitchell. Dr. Mitchell is a medical oncologist working at Memorial Sloan Kettering Cancer Center where he is also part of the Department of Epidemiology and Biostatistics. Dr. Mitchell specializes in treating genitourinary malignancy and has a research focus on improving how the healthcare system helps people with these and other cancers. So today, Dr. Mitchell will be discussing the article titled, “Quality of Treatment Selection for Medicare Beneficiaries with Cancer.”
Thank you for speaking with us, Dr. Mitchell.
Dr. Aaron Mitchell: Well, thank you for inviting me. I'm very glad to be here.
Dr. Davide Soldato: So I just wanted to introduce the topic by asking a couple of questions, very general, about the background of the article. So basically you reported the data using the SEER-Medicare to assist to assess the determinants of optimal systemic therapies delivery and selection. So, in particular, you focused on individuals that were diagnosed with cancer who were Medicare beneficiaries and in particular were part of the low income subsidy, which is also known as LIS. So I just wanted to ask you if you could briefly explain to our listeners how this program works, and what was the rationale of the study, and if there is any element of novelty in your study compared to what was done before the study was published.
Dr. Aaron Mitchell: Yeah. So that's a lot to cover, but yeah, a lot of opportunity to introduce the low income subsidy program which is a very important part of the Medicare program for prescription drugs, but often one that flies under the radar a little bit in the policy discussion. So this subsidy was created synchronously back with the Medicare Part D Program, which was created in 2006. There was some anticipation that for some high cost drugs, not all patients would be able to afford them even with the Part D program insurance as it was being created. And so they created a pathway to give an additional subsidy to some patients who had low income, who were anticipated to being at need and needing that assistance to afford high cost drugs. As the number of high cost drugs has really risen since 2006, this program has played an important role in helping patients afford drugs, especially those who need very expensive cancer drugs.
And what this program does is, once you meet the eligibility requirements, which require patients to have both quite a low income. So if you're single, that is at 135% of the federal poverty limit or below, and it also places some restrictions on assets. You also have to have low assets, so low income and low assets in order to qualify for the subsidy. But then once you do, the subsidy is really quite large. Patients who qualify for the LIS at the full subsidy level will pay about $10 per month per drug, even for specialty cancer drugs. So if you think about drugs such as those that we use to treat prostate cancer, my specialty, drugs like enzalutamide or XTANDI that run $15,000 to $20,000 per month, the out of pocket cost for a low income subsidy beneficiary is $10. So that is a huge discount. $10 isn't nothing, but even for someone with a low income, if they've got one or two cancer drugs that are at this rate, it's something that they can often afford.
This program applies to Part D cancer drugs that are prescription drugs basically. By and large, these are oral pills that patients are taking on a daily basis at home. These are the drugs that the low income subsidy program applies to. So if a patient needs a drug like that to treat their cancer, then they are able to receive it at very low cost. And what you'll see is a patient- in the studies that have been done, when a patient has low income, low enough for them to be able to qualify for this program, they then have better access to these drugs. You see increased adherence rates, you see increased prescription fill rates. And then when someone, when their income is just high enough to no longer qualify for this program, and they go back to regular Medicare Part D coverage, that's when the problems arise. So it's like as your income moves up the scale, you actually get more problems affording your cancer drugs. So that's the state of...
Host Dr. Davide Soldato and Dr. Aaron Mitchell discuss the JCO article "Quality of Treatment Selection for Medicare Beneficiaries With Cancer"
TRANSCRIPT
Dr. Davide Soldato: Hello and welcome to JCO After Hours, the podcast where we sit down with authors from some of the latest articles published in the Journal of Clinical Oncology. I am your host, Dr. Davide Soldato, medical oncologist at Hospital San Martino in Genoa, Italy. Today, we are joined by JCO author Dr. Aaron Mitchell. Dr. Mitchell is a medical oncologist working at Memorial Sloan Kettering Cancer Center where he is also part of the Department of Epidemiology and Biostatistics. Dr. Mitchell specializes in treating genitourinary malignancy and has a research focus on improving how the healthcare system helps people with these and other cancers. So today, Dr. Mitchell will be discussing the article titled, “Quality of Treatment Selection for Medicare Beneficiaries with Cancer.”
Thank you for speaking with us, Dr. Mitchell.
Dr. Aaron Mitchell: Well, thank you for inviting me. I'm very glad to be here.
Dr. Davide Soldato: So I just wanted to introduce the topic by asking a couple of questions, very general, about the background of the article. So basically you reported the data using the SEER-Medicare to assist to assess the determinants of optimal systemic therapies delivery and selection. So, in particular, you focused on individuals that were diagnosed with cancer who were Medicare beneficiaries and in particular were part of the low income subsidy, which is also known as LIS. So I just wanted to ask you if you could briefly explain to our listeners how this program works, and what was the rationale of the study, and if there is any element of novelty in your study compared to what was done before the study was published.
Dr. Aaron Mitchell: Yeah. So that's a lot to cover, but yeah, a lot of opportunity to introduce the low income subsidy program which is a very important part of the Medicare program for prescription drugs, but often one that flies under the radar a little bit in the policy discussion. So this subsidy was created synchronously back with the Medicare Part D Program, which was created in 2006. There was some anticipation that for some high cost drugs, not all patients would be able to afford them even with the Part D program insurance as it was being created. And so they created a pathway to give an additional subsidy to some patients who had low income, who were anticipated to being at need and needing that assistance to afford high cost drugs. As the number of high cost drugs has really risen since 2006, this program has played an important role in helping patients afford drugs, especially those who need very expensive cancer drugs.
And what this program does is, once you meet the eligibility requirements, which require patients to have both quite a low income. So if you're single, that is at 135% of the federal poverty limit or below, and it also places some restrictions on assets. You also have to have low assets, so low income and low assets in order to qualify for the subsidy. But then once you do, the subsidy is really quite large. Patients who qualify for the LIS at the full subsidy level will pay about $10 per month per drug, even for specialty cancer drugs. So if you think about drugs such as those that we use to treat prostate cancer, my specialty, drugs like enzalutamide or XTANDI that run $15,000 to $20,000 per month, the out of pocket cost for a low income subsidy beneficiary is $10. So that is a huge discount. $10 isn't nothing, but even for someone with a low income, if they've got one or two cancer drugs that are at this rate, it's something that they can often afford.
This program applies to Part D cancer drugs that are prescription drugs basically. By and large, these are oral pills that patients are taking on a daily basis at home. These are the drugs that the low income subsidy program applies to. So if a patient needs a drug like that to treat their cancer, then they are able to receive it at very low cost. And what you'll see is a patient- in the studies that have been done, when a patient has low income, low enough for them to be able to qualify for this program, they then have better access to these drugs. You see increased adherence rates, you see increased prescription fill rates. And then when someone, when their income is just high enough to no longer qualify for this program, and they go back to regular Medicare Part D coverage, that's when the problems arise. So it's like as your income moves up the scale, you actually get more problems affording your cancer drugs. So that's the state of...
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JCO Article Insights: HLA-Mismatched Unrelated Donor HCT With PTCy
In this JCO Article Insights episode, Alexandra Rojek provides a summary on "Post-Transplant Cyclophosphamide–Based Graft-Versus-Host Disease Prophylaxis Attenuates Disparity in Outcomes Between Use of Matched or Mismatched Unrelated Donors" by Schaffer et al published in the Journal of Clinical Oncology July 17th, 2024.
TRANSCRIPT
Alexandra Rojek: Hello and welcome to JCO Article Insights. I'm your host, Alexandra Rojek, and today we will be discussing an original report published in the October 1st issue of JCO titled, “Post-Transplant Cyclophosphamide–Based Graft-Versus-Host Disease Prophylaxis Attenuates Disparity in Outcomes Between Use of Matched or Mismatched Unrelated Donors,” by Shaffer et al.
The CIBMTR registry study set out to compare outcomes of patients undergoing allogeneic stem cell transplantation hematologic malignancies by HLA antigen matching status as well as by the type of GVHD prophylaxis regimen received either calcineurin inhibitor-based prophylaxis or post-transplant cyclophosphamide or PTCy. This study included patients reported to CIBMTR from January 2017 to June 2021 with AML, ALL or MDS, and required that they have undergone allotransplant with either a calcineurin inhibitor based so tacro or cyclosporine, GVHD prophylaxis, or PTCy, which included a calcineurin inhibitor or sirolimus with or without MMF and ATG. Matched unrelated donors were defined as an 8 out of 8 HLA match. And mismatched unrelated donors were defined as HLA mismatched at any single locus or 7 out of 8. The primary objective of the study aimed to compare overall survival or OS and GVHD and relapse-free survival (GRFS) within and between matched unrelated donors versus mismatched unrelated donors separated by calcineurin inhibitor versus PTCy based GVHD prophylaxis.
GRFS was defined as survival without grade 3 to 4 acute GVHD, moderate to severe chronic GVHD requiring systemic therapy or relapse. 10,025 patients were included from 153 centers, with a median follow up of over 36 months. Mismatched unrelated donor recipients were made up of 22% minority ancestry patients as compared to just 8% of patients receiving a matched unrelated donor allo transplant, showing an enrichment for patients of minority ancestry in the mismatched unrelated donor group. Just under 10% of patients were of minority ancestry in the study overall, reflective of challenges in transplant care for these patients, which may include inferior access to care, fewer available and suitably matched donors, among other factors. 54% of all patients were transplanted for AML and 29% for MDS. 45% of patients received myeloablative conditioning, 25% received regimens containing ATG, and 23% overall received PTCy with either a calcineurin inhibitor or sirolimus as well as MMF.
Among patients receiving PTCy, the authors did not find differences in overall survival by degree of HLA matching, whereas among patients receiving calcineurin inhibitor-based prophylaxis, there remained survival differences by HLA matching status. When comparing matched unrelated donor calcineurin inhibitor patients with PTCy matched unrelated donor patients, the PTCy arm had better OS, and the mismatched unrelated donor group who received PTCy had similar OS as well. For GRFS, matched unrelated donor and mismatched unrelated donor PTCy patients had no difference in GRFS, similar to the trend the authors see with overall survival. But these patients also had better GRFS than matched unrelated donor patients receiving calcineurin inhibitor-based prophylaxis. Within each prophylaxis arm, there was no difference in GRFS by HLA matching status. HLA mismatched patients receiving PTCy were less likely to experience GRFS than HLA mismatched patients receiving calcineurin inhibitor-based prophylaxis.
The authors saw similar differences in comparative trends when subgrouping patients based on conditioning intensity and additionally did not find differences in GRFS and OS by ATG exposure. When looking at patients with minority ancestry, those patients who received a match unrelated donor or mismatched unrelated donor with PTCy had comparable outcomes to non-Hispanic white patients. Additionally, among minority ancestry patients, there was a significant benefit in both GRFS and OS in the PTCy groups as compared to calcineurin inhibitor-based prophylaxis. When examining other specific toxicities included in the composite GRFS endpoint, such as GVHD rates among PTCy patients, the authors note that patients receiving a matched unrelated donor had similar rates of grade 3 to 4 acute GVHD but lower rates of moderate to severe chronic GVHD requiring systemic therapy. There ...
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JCO Article Insights: Nivolumab + Relatlimab v Nivolumab + Ipilimumab in Melanoma
In this JCO Article Insights episode, Rohit Singh provides a summary on "First-Line Nivolumab Plus Relatlimab Versus Nivolumab Plus Ipilimumab in Advanced Melanoma: An Indirect Treatment Comparison Using RELATIVITY-047 and CheckMate 067 Trial Data", by Long et al, published in the November issue of the Journal of Clinical Oncology. The article provides insights into the use of the two dual immune checkpoint inhibitor regimens in patients with untreated advanced melanoma.
TRANSCRIPT
Rohit Singh: Hello and welcome to JCO Article Insights. I'm your host Rohit Singh, Assistant Professor at the University of Vermont Cancer Center and today we'll be discussing the article “First-Line Nivolumab Plus Relatlimab Versus Nivolumab Plus Ipilimumab in Advanced Melanoma: An Indirect Treatment Comparison Using RELATIVITY-047 and CheckMate 067 Trials,” authored by Dr. Georgina Long from the Melanoma Institute of Australia and her colleagues.
So as we know, nivolumab plus relatlimab and nivo plus ipi, I'm going to refer to as ipi-nivo moving forward, are dual immune checkpoint inhibitors regimens that are approved for treating patients with advanced melanoma based on the phase 2 and 3 RELATIVITY-047 and phase 3 CheckMate 067 trials respectively. Nivo plus relatlimab is the only dual PD-1 and LAG-3 inhibitor regimen approved for treating patients with advanced melanoma and relatlimab is the first in class human IgG4 LAG-3 blocking antibody. Ipi plus nivo is a dual PD-1 and CTLA-4 inhibitor regimen.
So this paper basically is an indirect treatment comparison using a patient level database from these trials and this pretty much was conducted because of the absence of head to head trials looking at different regimens in advanced melanoma in first line setting. In this trial, the authors tried to compare these two trials. However, it's always hard to compare two different trials and we usually don't do cross trial comparisons. The problem is that the groups might be different to begin with. For example, one group might have younger patients, healthier patients, while the other might have older or sicker. These differences can make it hard to tell if the treatment caused improvement or if the groups were different to begin with. In this trial, researchers use inverse probability of treatment weighting to adjust the baseline differences between the two patient groups or between these two trials. Inverse probability of treatment weighting is a method used in research to help make a fair comparison between two groups when studying how a treatment intervention works. Basically, IPTW helps level the playing field between the two groups or like two trials for this paper. So, it calculates the likelihood of receiving a treatment. For each person, for each patient, researchers estimate the chance they would have gotten the treatment based on their characteristics like age, health, condition, their baseline staging, and based on that they create weights. People who are less likely to get the treatment but did are given more weight, and those who are very likely to get the treatment are given less weight. The same is done for the group that didn't get the treatment, and then they rebalance the groups. By applying these weights the group becomes more similar in their characteristics as if everyone had an equal chance of getting the treatment. This way, IPTW helps researchers focus on the effect of treatment itself and other differences between the groups. It's like adjusting the scales to make sure you are comparing apples to apples.
The key outcomes the authors are looking at in this one was progression free survivals, overall survival, confirmed objective response rate, melanoma specific survival, and treatment related adverse events. Looking at the results of this cross comparison trial, first looking at the PFS or progression free survival, both regimens ipi plus nivo and nivo plus relatlimab, showed similar PFS. At 36 months, PFS was 36% in nivo-relatlimab versus 39% in the ipi-nivo regimen with a hazard ratio of 1.08 indicating no significant differences. Looking at the overall survival at 36 months, overall survival was 57% in both the treatment regimens with a hazard ratio of 0.14, again, indicating no significant differences. Now looking at another confirmed objective response rate, confirmed objective rates were similar between both treatment regimens after weighting, 48% versus 50% with an odds ratio of 0.91 suggesting comparable response rates between the two regimens. Looking at melanoma specific survival at 36 months ...
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