Context
Circumferential resection margin is the primary determinant of local recurrence and a major factor in survival in rectal cancer. Neither chemotherapy nor chemoradiation compensates for a positive margin.
Objective
To identify treatment-related factors associated with hospital margin-positive resection and to develop a tool that could be used by individual hospitals to assess their outcomes based on their unique mix of patient and tumor characteristics.
Design
Retrospective review of the National Cancer Data Base, 1998–2007.
Settings
Community and academic/research hospitals.
Patients
Histologically confirmed localized rectal/rectosigmoid adenocarcinoma.
Outcome measures
Rate of margin positivity determined and adjusted for patient- and tumor-related factors to calculate expected margin positivity per hospital. An observed/expected ratio was calculated based on patient and tumor factors to identify treatment associated variation.
Results
Overall margin-positive resection rate was 5.2%. Patients with positive margins were more likely to be older, male, and African American; not have private insurance; and have their cancer diagnosed later in the study period. Associated tumor factors include rectal location, higher American Joint Committee on Cancer stage, signet/mucinous histology, and poor/undifferentiated grade. Among hospitals that were significantly low outliers, 47% were comprehensive community hospitals and 43.9% were academic/research hospitals; of those that were significantly high outliers, 52.3% were comprehensive community hospitals and 17.8% were academic/research hospitals. High-volume centers made up 80% of significantly low-outlier hospitals and 17% of significantly high outliers. Rates of chemotherapy and radiation were similar, but low-outlier hospitals gave more neoadjuvant radiation (26.3% vs 17%).
Conclusions
After adjustment for patient and tumor factors we identified both low and high outliers for margin positivity at resection as well as potentially modifiable risk factors. The nomogram created in this model allows evaluation of observed and expected event rates for individual hospitals, providing a hospital self-assessment tool for identifying targets for improvement.