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ASH 2025 | Reveal-MM: retrospective evaluation of healthcare patterns in the years preceding myeloma diagnosis

Faith Davies, MD, MRCP, MRCPath, FRCPath, NYU Langone, New York, NY, discusses the Reveal-MM study, a US claims-based case-control study that retrospectively evaluated variables in early assessment and landmark trends in multiple myeloma (MM). The investigation highlighted that patients exhibit distinct patterns of healthcare utilization in the year prior to their myeloma diagnosis. Dr Davies notes that this data could be used to model patient presentation and identify those at risk of developing myeloma using artificial intelligence (AI), enabling earlier intervention and potentially improving patient outcomes. This interview took place at the 67th ASH Annual Meeting and Exposition, held in Orlando, FL.

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Transcript

Yeah, so I’m presenting a poster called the Reveal-MM study, and it’s actually a kind of slightly interesting area. Rather than it being a therapeutic study, it’s looking at how we may be able to diagnose patients earlier. Because one of the things that’s really very evident now is that we have some incredible drugs for myeloma patients, but patients are still invariably having at least a year’s worth of symptoms before they hit base with their oncology team...

Yeah, so I’m presenting a poster called the Reveal-MM study, and it’s actually a kind of slightly interesting area. Rather than it being a therapeutic study, it’s looking at how we may be able to diagnose patients earlier. Because one of the things that’s really very evident now is that we have some incredible drugs for myeloma patients, but patients are still invariably having at least a year’s worth of symptoms before they hit base with their oncology team. We’ve been able to use a large US claims database to look at the history of patients for the two years prior to when they’ve been diagnosed with multiple myeloma. 

And so what we’ve done is that we’ve looked at over 4,500 patients with myeloma and compared them to the same number of patients without myeloma. And we’ve made really sure that we’ve matched them very, very closely. And then we’ve looked at their healthcare utilization for the period prior to their diagnosis. And I think what’s really important is that a year prior to their diagnosis, we can identify patients who are going to go on and get myeloma – they have a different pattern of utilization. Now, some of that are obvious things. So bone pain, muscle pain, anemia, but some of them are slightly more unusual. So there’s increased chance of having cardiovascular issues or being investigated for GERD and having an endoscopy and also an increased chance of seeing the ophthalmologist. 

So there’s distinct patterns that we can identify, and I think this is really important because if we could then take this data to the next step, which is to use it to model how patients present, we could then utilize AI and big data to actually have a process where we could look at patients’ electronic healthcare records and say, right, okay, here’s a patient who we think may go on and get myeloma and send a ping to the GP, the PCP, and say, hey, have you thought about myeloma? So I guess it’s an interesting way of approaching things. In the past, we’ve tried educating PCPs. We’ve spent a lot of time getting on top of the kind of orthopedic docs saying, hey, you know, this is what you need to think about. Whereas this may be a slightly different way of approaching getting patients early so that we can make sure they benefit from therapy.

 

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