Selection of initial therapy for newly-diagnosed adult acute myeloid leukemia: Limitations of predictive models

Abstract

Acute myeloid leukemia (AML) remains difficult to treat: despite multiagent chemotherapy, allogeneic hematopoietic cell transplantation, and several newly approved agents, many patients will not be alive and in remission 3 years after diagnosis. However, with more agents available there are more options and a corresponding need to choose among them. Doing so is complicated by the molecular diversity of AML and the older age of many patients, predisposing them to both treatment-related mortality and, more commonly, resistance to treatment. There is no shortage of scoring systems to identify patients at high risk of early death or treatment resistance after conventional AML induction chemotherapy. As we point out here, their accuracy is limited. Furthermore, without periodic recalibration to account for new therapies and changes in supportive care, the accuracy of any prediction model will decrease over time. The limitations we describe here are important for clinicians to be aware of.

Keywords: Acute myeloid leukemia (AML); Mathematical modeling; Medical fitness; Outcome prediction; Therapeutic resistance; Treatment-related mortality.