Antiplatelet Therapy in PAD: Interpreting Data From TWILIGHT

Transcript: Deepak L. Bhatt, MD, MPH: How do you reconcile all this? Mike, let me put you on the spot because you’ve been involved with a number of these pivotal trials, including most recently the TWILIGHT trial, which looked at a strategy of dual-antiplatelet therapy with ticagrelor and aspirin for 3 months in stented patients who were largely high ischemic risk or meant to be. But with a strategy of dropping the aspirin and continuing ticagrelor monotherapy after those 3 months and, in that population, with a total of 15 months of observation, there was no benefit in terms of ischemic outcomes in being on 2 antiplatelets versus 1. There was about half as much major bleeding with 1 antiplatelet versus 2.

How do you reconcile all those different data sets? In a sense, that seems to be contradictory. It’s telling us that 1 antiplatelet, after an initial period of intense antiplatelet therapy with 2 agents, is the way to go. Granted, I understand it’s not aspirin monotherapy, it’s an ADP receptor antagonist—and a potent one—given twice a day, ticagrelor. But at least from the 30,000-foot view, it all does seem a bit contradictory. All these other trials say 2 agents are better than 1, but now we’re saying 1 agent is better than 2.

C. Michael Gibson, MS, MD: It is a very confusing time. One of the problems is we do trials in populations. We take care of populations in trials. When it comes to medicine, we take care of patients. The results of a trial may not apply to the patient in front of you. The other big problem is that in the trials, the results tend to be—in most of them, with a few exceptions—homogenous across multiple subgroups. That’s true for both effectiveness and bleeding. Yet as doctors, we want to say, “Here’s a high-risk patient regarding efficacy but at low risk of bleeding.” It turns out that the people who are at high risk of efficacy are also the people who are at a high risk of bleeding.

It’s hard to find that magic bullet, but it does emphasize that we have to look at individual patients and assess the balance of bleeding and efficacy. We need better tools. We have used machine learning to say for this person right in front of us, this 80-year-old patient with a creatinine of 2 mg/mL and this constellation of risk factors, here’s this person’s risk of bleeding and here’s their risk of effectiveness. We’ve got to develop better risk models to guide us in individual patient decisions.

Your other big question, Deepak, is that we’re being pushed and pulled in different directions about adding and taking away drugs. One of the things that we’ve got to be careful of, as we do these trials, is that they tend to be small. They tend to be powered for what we want to look at, such as bleeding, but they may not be powered for effectiveness. We’re seeing a lot of these early discontinuation trials that are very reassuring about bleeding but have not definitively answered the question about effectiveness. That then leads us to meta-analyses that you rightly referred to giving us some comfort about effectiveness. But my understanding is we don’t want to rely too much on any 1 underpowered study to give us reassurance about efficacy if it just reduced bleeding. We’ve got to do better than that.

Deepak L. Bhatt, MD, MPH: Absolutely. Those are really great points.

Transcript Edited for Clarity