Summary
Progression of acute myeloid leukemia (AML) can primarily be characterized by two events: induction failure and relapse. These events are determined by measuring the percentage of blasts in bone marrow biopsies and peripheral blood.
To address the challenge of identifying induction failure and relapse events in RWD, researchers in this study developed a novel approach that combines structured and abstracted data sources to derive these events with accuracy, precision, and scalability.
Why this matters
By capturing derived induction failure and relapse (dIFR), researchers can gain an understanding of real-world outcomes for patients with AML.