Lena E. Winestone, MD, MSHP, on Health-Care Disparities in Hematologic Cancers: Real-World Data
2020 ASH Annual Meeting & Exposition
Lena E. Winestone, MD, MSHP, of the University of California, San Francisco and Benioff Children’s Hospital, reviews different aspects of bias in treatment delivery, including patient selection for clinical trials; racial and ethnic disparities in survival for indolent non-Hodgkin diffuse large B-cell lymphomas; and end-of-life hospitalization of patients with multiple myeloma, as well as outcome disparities (Abstracts 207-212).
The ASCO Post Staff
Sagar Lonial, MD, of the Emory University School of Medicine, summarizes key papers presented in a session he co-moderated on how second-generation CAR T cells can be used to treat patients with multiple myeloma (Session 653).
The ASCO Post Staff
Meletios A. Dimopoulos, MD, of the University of Athens, discusses data from the phase III APOLLO study, which evaluated the use of subcutaneous daratumumab plus pomalidomide and dexamethasone, vs pomalidomide and dexamethasone alone, in patients with relapsed or refractory multiple myeloma (Abstract 412).
The ASCO Post Staff
Corey Cutler, MD, MPH, of Dana-Farber Cancer Institute, discusses results from a multicenter trial that compared reduced-intensity allogeneic hematopoietic cell transplantation to hypomethylating therapy or best supportive care in patients aged 50 to 75 with advanced myelodysplastic syndromes (Abstract 75).
The ASCO Post Staff
Smita Bhatia, MD, MPH, and Radhika Gangaraju, MD, both of the Institute for Cancer Outcomes and Survivorship, University of Alabama at Birmingham, discuss findings that showed survivors of bone marrow transplants are at a 7- to 12-fold higher risk of coronary heart disease than a sibling comparison group. They recommend aggressive management of cardiovascular risk factors to prevent morbidity from heart disease in this patient population (Abstract 73).
The ASCO Post Staff
Hassan Awada, MD, of the Taussig Cancer Institute, Cleveland Clinic Foundation, discusses the use of newer machine-learning techniques to help decipher a set of prognostic subgroups that could predict survival, thus potentially improving on traditional methods and moving acute myeloid leukemia into the era of personalized medicine (Abstract 34).
