Novel Risk-Assessment Model for Acute Myeloid Leukemia


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Mohamed Sorror, MD, MSc

Mohamed Sorror, MD, MSc

Elihu Estey, MD

Elihu Estey, MD

A RECENTLY PUBLISHED STUDY in JAMA Oncology, led by Mohamed Sorror, MD, MSc, and Elihu Estey, MD, of the Fred Hutchinson Cancer Research Center (Fred Hutch), involving five collaborating institutions, provides a novel model to predict 1-year survival rates of patients after beginning treatment for acute myeloid leukemia (AML).1 The researchers hope the new study, which included 1,100 patients treated for AML, will provide physicians and patients with novel, patient-centered risk-assessment means both to improve survival and reduce treatment-related health costs by more closely matching patients with therapies most likely to benefit them. Physicians can now use an online assessment tool to refine decision-making about the therapy used in treating patients with AML. 

Clinical Implications 

AMONG PHYSICIANS, Dr. Sorror noted there may be some reluctance in offering curative therapy to older patients with newly diagnosed AML, especially those with multiple medical problems. 

“The new model allows objective identification of those older patients who might benefit from referral to receive either conventional or investigational AML therapy,” Dr. Sorror said. “This is particularly important since the disease is more common in older adults, with a median age of 68 at diagnosis.” 

Conversely, the AML-composite model could identify patients who are unlikely to benefit from certain AML therapies and spare them related toxicities and costs of treatments. 

Dr. Sorror and colleagues are using this model in an ongoing prospective study to identify patients who might benefit from allogeneic hematopoietic cell transplantation, typically associated with high mortality risks, as a curative therapy for AML. ■

DISCLOSURE: For full disclosures of the study authors, visit www.jamanetwork.com. 

REFERENCE 

1. Sorror ML, et al: Development and validation of a novel acute myeloid leukemia-composite model to estimate risks of mortality. JAMA Oncol. September 7, 2017 (early release online).


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