So this is data that came out of the CIBMTR registry. So it is the largest data set across a number of CAR-T centers in the US and showing that there is some growth in the use of CAR-T in the outpatient setting, particularly with axi-cel, CD19-targeting CAR-T for lymphoma. Where because these CAR-Ts could have cytokine release syndrome that comes on within days of infusion in the initial phase there could be a lot of hesitancy in administering that outpatient...
So this is data that came out of the CIBMTR registry. So it is the largest data set across a number of CAR-T centers in the US and showing that there is some growth in the use of CAR-T in the outpatient setting, particularly with axi-cel, CD19-targeting CAR-T for lymphoma. Where because these CAR-Ts could have cytokine release syndrome that comes on within days of infusion in the initial phase there could be a lot of hesitancy in administering that outpatient. So it’s good to see how that is being used in the outpatient versus inpatient setting now in real-world practice to see that it is feasible and that you know patient safety is not impacted and there could be you know benefit to that from a health resource utilization standpoint.
So related to that, Mayo Clinic also has a poster here at EHA 2025 as well showing in our experience where we do apply outpatient management to all of our CAR-T patients and bispecific how we could really use AI and machine learning models to incorporate all the clinically available data to really predict the onset of cytokine release syndrome, sometimes within days before the patient has their very first symptom. So I think with the current technologies and tools like this, this may help enable a variety of practices, despite how their infrastructure setup is, to kind of use that information to see how they can do more risk-adaptive management and potentially be able to offer more of this management in the outpatient setting.
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