Background: Among patients with resected colon cancer, black patients have worse survival than whites. We investigated whether disparities in survival and related endpoints would persist when patients were treated with identical therapies in controlled clinical trials.
Methods: We assessed 14,611 patients (1218 black and 13,393 white) who received standardized adjuvant treatment in 12 randomized controlled clinical trials conducted in North America for resected stage II and stage III colon cancer between 1977 and 2002. Individual patient data on covariates and outcomes were extracted from the Adjuvant Colon Cancer ENdpoinTs (ACCENT) database. The endpoints examined in this meta-analysis were overall survival (time to death), recurrence-free survival (time to recurrence or death), and recurrence-free interval (time to recurrence). Cox models were stratified by study and controlled for sex, stage, age, and treatment to determine the effect of race. Kaplan-Meier estimates were adjusted for similar covariates to control for confounding. All statistical tests were two-sided.
Results: Black patients were younger than whites (median age, 58 vs 61 years, respectively; P < .001) and more likely to be female (55% vs 45%, respectively; P < .001). Overall survival was worse in black patients than whites (hazard ratio [HR] of death = 1.22, 95% confidence interval [CI] = 1.11 to 1.34, P < .001). Five-year overall survival rates for blacks and whites were 68.2% and 72.8%, respectively. When subsets defined by sex, stage, and age were analyzed, overall survival was consistently worse in black patients. Recurrence-free survival was worse in black patients than whites (HR of recurrence or death = 1.14, 95% CI = 1.04 to 1.24, P = .0045). Three-year recurrence-free survival rates in blacks and whites were 68.4% and 72.1%, respectively. In contrast, recurrence-free interval was similar in black and white patients (HR of recurrence = 1.08, 95% CI = 0.97 to 1.19, P = .15). Three-year recurrence-free interval rates in blacks and whites were 71.3% and 74.2%, respectively.
Conclusions: Black patients with resected stage II and stage III colon cancer who were treated with the same therapy as white patients experienced worse overall and recurrence-free survival, but similar recurrence-free interval, compared with white patients. The differences in survival may be mostly because of factors unrelated to the patients' adjuvant colon cancer treatment.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3196480 | PMC |
| http://dx.doi.org/10.1093/jnci/djr310 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Association of Ultrasound and Electrodiagnostic Studies in Patients Evaluated for Ulnar Neuropathy.
J Hand Surg Am
January 2025
Department of Orthopaedic Surgery, Duke University School of Medicine, Durham, NC. Electronic address:
Purpose: Our goal was to determine the association between the severity of electrodiagnostic (EDX) studies with the cross-sectional area (CSA) of the ulnar nerve at the cubital tunnel using diagnostic ultrasound. Based on our clinical experience, we hypothesized there would not be a positive correlation between the severity of EDX and ulnar nerve CSA.
Methods: This was a retrospective analysis of patients 18 years or older evaluated from May 1, 2020, to June 31, 2021, referred for an upper limb EDX and neuromuscular ultrasound to evaluate for an upper limb neuropathy.
Associations Between Peak Expiratory Flow and Community Mobility Loss Among Older Adults in the United States.
J Am Geriatr Soc
January 2025
Department of Physical Therapy and Rehabilitation Science, University of Maryland School of Medicine, Baltimore, Maryland, USA.
Background: Community mobility is a vital patient-centered outcome for older adults living in the community. These deficits in mobility are linked to social isolation, increased hospitalizations, and higher mortality rates. Impaired pulmonary function may be a modifiable risk factor for mobility decline, with existing inequities in lung health potentially contributing disproportionately to mobility loss among Black older adults.
View Article and Find Full Text PDFSegregation in hospital care for Medicare beneficiaries by race and ethnicity and dual-eligible status from 2013 to 2021.
Health Serv Res
January 2025
Brookdale Department of Geriatrics and Palliative Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Objective: To examine the extent of segregation between hospitals for Medicare beneficiaries by race, ethnicity, and dual-eligible status over time.
Data Sources And Study Setting: We used Medicare inpatient hospital provider data for fee-for-service (FFS) beneficiaries, and the Dartmouth Atlas of Health Care from 2013 to 2021 nationwide, for hospital referral regions (HRRs), and for and hospital service areas (HSAs).
Study Design: We conducted time trend analysis with dissimilarity indices (DIs) for Black (DI-Black), Hispanic (DI-Hispanic), non-White (including Black, Hispanic, and other non-White) (DI-non-White), and dual-eligible (DI-Dual) beneficiaries.
Racial and Ethnic Disparities in the Outcomes and Treatment of Patients Admitted with Heart Failure: A Nationwide Analysis.
J Clin Med
December 2024
Division of Cardiovascular Medicine, Tufts Medical Center, Boston, MA 02111, USA.
: Heart failure is the leading cause of hospital admission and mortality. Racial disparities have been demonstrated in various cardiovascular disorders; however, the data for in-hospital outcomes, complications, and procedural rates are limited. : Utilizing the National Inpatient Sample (NIS) database, this retrospective cohort study included adult patients admitted with a principal diagnosis of heart failure.
View Article and Find Full Text PDFAdvanced Brain Tumor Classification in MR Images Using Transfer Learning and Pre-Trained Deep CNN Models.
Cancers (Basel)
January 2025
Department of Computer Science, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology, 2815 Gjøvik, Norway.
Background/objectives: Brain tumor classification is a crucial task in medical diagnostics, as early and accurate detection can significantly improve patient outcomes. This study investigates the effectiveness of pre-trained deep learning models in classifying brain MRI images into four categories: Glioma, Meningioma, Pituitary, and No Tumor, aiming to enhance the diagnostic process through automation.
Methods: A publicly available Brain Tumor MRI dataset containing 7023 images was used in this research.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!