Associations between race and survival in pediatric patients with diffuse large B-cell lymphoma

Karishma Khullar1, Jesse J Plascak2, Richard Drachtman3

  • 1Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA.

Cancer Medicine
|January 27, 2021
PubMed

Insights

Racial disparities in pediatric diffuse large B-cell lymphoma (DLBCL) survival may be linked to clinical factors and treatment. Further research is needed to address these survival differences in DLBCL patients.

Area of Science:

  • Pediatric Oncology
  • Hematology
  • Cancer Epidemiology

Background:

  • Pediatric diffuse large B-cell lymphoma (DLBCL) presents unique survival challenges.
  • Racial disparities in cancer outcomes are a significant public health concern.
  • Understanding factors influencing survival in pediatric DLBCL is crucial for equitable care.

Purpose of the Study:

  • To investigate the factors contributing to racial disparities in overall survival (OS) among pediatric diffuse large B-cell lymphoma (DLBCL) patients.
  • To analyze the impact of clinical features and treatment on survival differences by race in this population.

Main Methods:

  • Utilized data from the National Cancer Database (NCDB) for pediatric patients (≤21 years) diagnosed with DLBCL between 2004 and 2014.
  • Employed multivariable Cox proportional hazards models to evaluate clinical characteristics and survival.
  • Included 1023 eligible patients in the final analysis.

Main Results:

  • Unadjusted analysis indicated a higher death rate in Black patients compared to White patients (HR 1.51, p=0.041).
  • Adjusted analysis showed this disparity was not statistically significant (HR 1.46, p=0.103), suggesting mediation by other factors.
  • Key OS predictors included B symptoms, chemotherapy receipt, disease stage, and insurance type. Patients with B symptoms, "Other" insurance, or no chemotherapy had worse survival. Earlier stage disease correlated with better survival.

Conclusions:

  • Racial disparities in pediatric DLBCL survival may be influenced by clinical and treatment-related parameters.
  • These findings highlight the need to address disparities in care delivery and access to treatment.
  • Further investigation into specific mediating factors is warranted to improve outcomes for all pediatric DLBCL patients.
Abstract

Related Concept Videos

Cancer Survival Analysis01:21

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
525
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
403
Bone Marrow Sampling and Transplants01:22

Bone Marrow Sampling and Transplants

Bone marrow transplant is a potential cure for several diseases, including cancer and specific genetic disorders. Notably, this procedure is applicable for patients suffering from aplastic anemia, certain types of leukemia, severe combined immunodeficiency disease (SCID), Hodgkin's disease, non-Hodgkin's lymphoma, multiple myeloma, thalassemia, sickle-cell disease, and certain cancers.
The transplant begins with high doses of chemotherapy and radiation treatment, which aim to destroy...
638
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
253