ASH 2024 | A pooled analysis of baseline health-related QoL in patients with AML, APL, and MDS

Proactive Project is actually an international initiative involving mainly the GIMEMA and the former NCRI UK group and the University of Cardiff. So it was a massive joint effort in pulling together different data sets in patients with myelodysplastic syndromes, acute myeloid leukemia and acute promyelocytic leukemia. So we built up this database of nearly 4,000 patients across these three diseases. And this was only possible also thanks to a great job done by the lawyers because it was the very first step of this project was sorting out all the legal issues and administrative issues. But once we sorted out all this, we pulled together this data set for conducting a number of research questions. The first one that we’ll be presenting here is actually profiling the quality of life of these patients at the time of diagnosis to see what is the symptom burden of these patients, if there are differences in the symptom burden and in the quality of life of a patient with APL versus MDS or AML, because this might help physicians to be proactive in supporting these patients from the very beginning of the therapy. And what we are seeing is that these patients have a high symptom burden already at the time of diagnosis. And we also observe some differences between the populations. So for example, we noticed that patients with APL were doing worse already at the time of diagnosis as compared to AML, which was the other way around for patients with MDS. Patients with MDS, independent of the risk at the time of diagnosis, tended to have a better quality of life profile than AML patients. Of course, these were adjusted analyses. So we adjusted for age and gender because of course you expect that these diseases and diagnoses have a different age. And of course, this is something that you need to consider when you run these analyses. So we just started, I would say, to scratch the surface of the potential of this dataset. So in the coming weeks and months, we will start more prospective analyses to look at a number of other aspects that you can only look at when you have a large number of patients. And Proactive is 4,000 patients, it’s pretty unique. So we will also run some AI applications, artificial intelligence applications to run new prognostic models. So we expect more from this dataset.

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