Treatment in Clinical Trials Could Potentially Overcome the Disparity in Outcomes of Patients with Acute Myeloid Leukemia from Disadvantaged Neighborhoods

• Published in: Blood 2024-11, Vol.144 (Supplement 1), p.3817-3817
• Main Authors: Swaminathan, Mahesh, Abbas, Hussein A., Garcia-Manero, Guillermo, Daver, Naval, Kadia, Tapan M., DiNardo, Courtney D., Borthakur, Gautam, Jabbour, Elias, Pemmaraju, Naveen, Alvarado Valero, Yesid, Ohanian, Maro, Jain, Nitin, Short, Nicholas J., Bose, Prithviraj, Takahashi, Koichi, Hwang, Hyunsoo, Huang, Xuelin, Edelkamp, Paul, Pierce, Sherry, Popat, Uday, Shpall, Elizabeth J., Champlin, Richard E., Kantarjian, Hagop M., Ravandi, Farhad
• Format: Article
• Language: English
• ISSN: 0006-4971; 1528-0020
• DOI: 10.1182/blood-2024-210779

Introduction Population-based studies from publicly available databases have shown a disparity in the outcomes of patients (pts) with acute myeloid leukemia (AML) based on their racial background. However, such studies are limited as several aspects of socioeconomic (SE) data, an independent prognostic factor, still need to be captured. Area Deprivation Index (ADI) is one of the most advanced SE tools, incorporating 17 SE factors to rank neighborhoods based on disadvantaged status (Powell et al. Forefront, 2023). This abstract, reports the largest cohort of pts with molecular, cytogenetic (CG) and ADI data treated in a single institution. Methods Adult pts with AML available self-reported race and treated at MD Anderson Cancer Center from 3/2013 to 3/2023 were included. ADI data was downloaded from https://www.neighborhoodatlas.medicine.wisc.edu/May 10,2024. The MatchIt package was used to perform nearest neighbor matching without replacement, using a generalized linear model to estimate propensity scores based on the following covariates (gender, age, ELN 2022 risk, treatment intensity, ADI State Rank, and ADI National Rank), and aiming to match each racial unit (Non-Hispanic Black, NHB or Hispanic, H) with up to 9 Non-Hispanic Whites (NHW). Fisher's exact test and Wilcoxon rank sum test were used to compare two groups. The probabilities of OS were estimated using Kaplan-Meier method. Backward multivariable analysis was used to predict factors influencing OS. Results 2442 pts were identified (NHW-2032, NHB-202, H-109, and Asians, A-99). Data on Asians will not be reported in this abstract. In the MVA on unmatched cohort (n=2343), age ≥60 (HR-1.4, 95%CI 1.2-1.6, p