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From Unexpected CV Benefits to Potential in Heart Failure: Insights and Outlook for SGLT2 Inhibitors
Mary Caffrey
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From Unexpected CV Benefits to Potential in Heart Failure: Insights and Outlook for SGLT2 Inhibitors

Mary Caffrey
Coverage from the first of 3 Peer Exchange™ discussions from the Diabetes Stakeholders Summit.
As a result, the EMPEROR studies are now under way for empagliflozin, which will examine the effect of the SGLT2 inhibitor specifically on patients with HFpEF and HFrEF.9,10 Results will come in 3 to 4 years. “These are heart failure trials being driven by heart failure experts,” Inzucchi said. “The heart failure community is very interested in this class because of the EMPA-REG signal, »

but the question is, will the benefits be seen again in the diabetic population?”

“Or in prediabetes?” Bloomgarden asked.

Big Data and CVD Benefits

Of course, clues to what the EMPEROR results may reveal were contained in CVD-REAL. Scanlon asked Gabbay to discuss these findings, as well as the importance of using data to gain these insights.

“It’s the beginning of what we’ll see a lot more of—using big data to try to answer some of these questions,” Gabbay said. “There are a lot of hypotheses. Is it a class effect? What’s the effect on congestive heart failure? We have some data, but not all the questions are answered. There are a number of studies that are ongoing. Some, we’ll get results soon, but some will still take several years. What do we do in the meantime?”

Gabbay explained that while CVD-REAL seemed to suggest a signal for reducing congestive heart failure across all SGLT2 inhibitors, the “challenge with a retrospective analysis is you’re not randomizing people to therapy.” Gabbay and Inzucchi agreed there was some value in observational studies—as statisticians can use propensity matching to make up for the loss of randomization—but Inzucchi said these studies should be taken “with a grain of salt.”

Bloomgarden, too, noted the need to watch for “channeling bias,” in observational studies—when patients ask to be put on the “new drug.” But he said that even the largest clinical trials have event rates that are so low there are questions that can’t be answered. Inzucchi said the question is whether patients in trials are different from those in the real world. “I think it’s great when the randomized clinical trials and the observational data sets point in the same direction, but when they don’t, I think it’s really confusing.”

Designing Better CV Outcomes Trials

CV outcomes trials started with one idea, “First, do no harm.” But, Scanlon asked, is it time for a redesign? Are they powered sufficiently? As large as they are, are they large enough? How long should patients be followed? Can data be collected retrospectively, and if so, for how long?

Bloomgarden said as “hugely expensive” as it would be to follow large numbers of patients for a decade or more, this must be balanced against the 650 million individuals worldwide who will develop diabetes by 2040. And trials shouldn’t just examine the effect of therapies or strategies on the highest-risk patients. “Let’s try to figure out how all people developing diabetes should be treated going forward,” he said.

Long term, the progression of diabetes makes it impractical to study a single therapy for an extended period, Inzucchi said. “It’s not clean like that,” he said, using an example of a study conducted in 2004 based on the approaches that were common at the time that resulted in “cross contamination” as patients needed additional therapies.

Snow, the payer, said there’s no chance that an analysis of big data will ever replace the role of the randomized clinical trial, “no matter how good it is.” However, he said, “we do know that there are certain situations where a randomized clinical trial just doesn’t work because the population is relatively homogenous. So, you’re stuck with the question of, ‘Well, can’t I expand this into other populations? Do I need a full, other randomized controlled trial to answer that question or not?’ ”

Observational studies can help with questions that would take a long time to answer, that would require studies of great complexity, or in cases in which there is great risk of patients dropping out of the study, Snow said.

“I totally agree,” Gabbay said. There are many questions

that need answers, and not every question will get a randomized controlled trial that collects data for 5 to 10 years. Practically speaking, there are patients who need treatment today.

“As big data [analysis] becomes more sophisticated,…and studies are done more accurately, we’re going to have to rely on that kind of data to answer some of the questions that there are unlikely to be clinical trials on,” Gabbay said. The challenge is that some will be well done and others will be poorly done, and the average provider reading an abstract won’t know the difference. The danger is that kind of data sways clinical care. I think a better arbitration of study technique for big data analysis will really help move the field forward.”

Clinical Decision Making and Cost–Effectiveness

Scanlon turned the discussion toward the future—of using data beyond 1 institution or health plan to mine data sets for insights from larger populations, so that clinicians gain a more balanced view than might otherwise happen if they are influenced by an outlier case. Snow said this requires cooperation between plans and providers.

“One of the hopes for the future is that we’ll be able to integrate that type of data, more effectively, into the data that we have through various relationships we have with the providers,” Snow said, “where we’re able to share that information and able to bring the power of the information that’s collected on the individual patient level—lab data, physical findings, etc—but also bring it to a level where we’re talking about not necessarily hundreds or thousands, but now, talking millions of folks that we’re looking at into the analysis.”

Coverage decisions, he said, start with the scientific evidence. “That’s separate, or divorced, from the cost (either the cost of the therapy or even the savings). Once the scientific data are established, that it’s effective, then the question (that) comes is, ‘What is that cost? And how is it going to fit into a benefits plan?’”

“And so, obviously, it’s about something that, in addition to being scientifically valid, also saves money. Well, that’s about as easy as it gets.

“Those that are scientifically valid and cost some money; those are very likely to still be approved. And those that are scientifically valid but cost a lot of money may still well be approved, but they may get more scrutiny to make sure they’re being utilized for the appropriate patient in the appropriate way.”

Snow acknowledged that the time element does enter the cost-effectiveness discussion—will the payer of today realize the benefit for an expensive therapy that may prevent costly events years into the future?

Gabbay said that is where “class effect” become important. Once a class of drugs is shown to have a benefit, payers may choose among different drugs based on price. “But if it turns out that there’s ambiguity there, and right now, we’re still in an area of some ambiguity, it makes that much more problematic.

“That’s really where I think we’ll have a sense, over the coming months, of whether studies now confirm that there’s a class effect or not. For most other drugs, that has been the case.”

The panelists concluded by discussing how this is an exciting time in diabetes care.

“Diabetes has always been one of those situations in medicine where there was just a very negative association with it,” Snow said. “It’s increasing in frequency. The prevalence of diabetes is increasing. Folks will develop microvascular complications…the news is always bad.”

But now, “We have slowly chipped away at the microvascular complications and we’ve chipped away at the macrovascular complications, and now we have even further agents that look like we’ll be able to chip away at this big chip much more. And so, we can really give our patients an upscale message that, yes, it’s diabetes, but you can live a long and healthy life despite having diabetes.”

1.. Zinman B, Wanner C, Lachin JM, EMPA-REG OUTCOME Investigators. Empagliflozin, cardiovascular outcomes, and mortality in type 2 diabetes. N Engl J Med. 2015;373(22):2117-2128. doi: 10.1056/NEJMoa1504720.

2. Regan TL. FDA mea culpa part of cautionary tale. Am J Manag Care. 2013;19(SP7)SP242-SP243.

3. FDA approves Jardiance to reduce cardiovascular death in adults with type 2 diabetes [press release]. Silver Spring, MD: FDA; December 2, 2016. Accessed June 4, 2017.

4. Marso SP, Daniels GH, Brown-Frandsen K, et al; LEADER Steering Committee, LEADER Trial. Liraglutide and cardiovascular outcomes in type 2 diabetes. N Engl J Med. 2016;375(4):311-322. doi: 10.1056/NEJMoa1603827.

5. Pfeffer MA, Claggett B, Diaz R, et al; ELIXA investigators. Lixisenatide in patients with type 2 diabetes and acute coronary syndrome. N Engl J Med. 2015;373(23):2247-2257. doi: 10.1056/NEJMoa1509225.

6. Marso SP, Bain SC, Consoli A, et al; SUSTAIN-6 investigators. Semaglutide and cardiovascular outcomes in patients with type 2 diabetes. N Engl J Med. 2016;375(19):1834-1844. doi: 10.1056/NEJMoa1607141.

7. Caffrey M. Can SGLT2 inhibitors prevent heart failure on a broad population? The American Journal of Managed Care® website. Published March 19, 2017. Accessed June 20, 2017.

8. Kosiborod M, Cavender MA, Fu AZ, et al. Lower risk of heart failure and death in patients initiated on SGLT2 inhibitors versus other glucose lowering drugs: the CVD-REAL study [published online May 18, 2017]. Circulation. 2017. doi: 0.1161/CIRCULATIONAHA.117.029190.

9.  EMPagliflozin outcomE tRial in Patients With chrOnic HeaRt Failure With Reduced Ejection Fraction) EMPEROR-Reduced. website. Updated June 13, 2017. Accessed June 2017.

10. EMPagliflozin outcomE tRial in Patients With chrOnic HeaRt Failure With Preserved Ejection Fraction (EMPEROR-Preserved). website. Updated June 13, 2017. Accessed June 2017.
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