Katherine Mallin

Background The National Cancer Database (NCDB) is a hospital-based cancer registry that includes diagnostic, staging, treatment, and outcomes data for newly diagnosed cancer patients in the United...

300 Background: While a strong volume-outcome relationship exists for many cancer operations, patients continue to undergo these operations at low-volume centers. Methods: Patients were identified from the National Cancer Data Base (NCDB) from 2010-2014 who underwent resection for bladder, breast, esophagus, stomach, pancreas, lung and rectal malignancies from 1,406 hospitals. Low-volume hospitals were defined as those in the bottom quartile by surgical volume for each operation separately. Regression models were developed to assess patient-level factors associated with undergoing surgery at a low-volume hospital for their malignancy. Results: Of 633,853 patients identified, 49,926 (7.9%) underwent cancer surgery at a low-volume hospital. The low-volume threshold was 1 case/year for bladder, 34 for breast, 1 for esophagus, 1 for stomach, 1 for pancreas, 5 for lung, and 2 for rectal cancers. For all the cancer surgeries examined, patients were more likely to undergo surgery at a low-volume hospital if they lived in a rural area or if they already had to drive a long distance just to reach the low-volume hospital. Patient demographics, socioeconomic factors, insurance type, comorbidities, and stage of disease were not consistently associated with undergoing surgery at a low-volume hospital across all malignancies. Although breast cancer is a common cancer with a less established volume-outcome relationship, the factors associated with undergoing surgery at a low-volume hospital were similar to those of the more complex cancer operations. Conclusions: Patients continue to undergo surgery at low-volume hospitals due to where they live and how far they have to travel. Regionalization policy initiatives will remain challenging in this population. Efforts should therefore continue to emphasize quality improvement locally at each facility caring for patients with cancer.

173 Background: Many quality metrics of cancer care are process measures, based on whether a patient received recommended treatment for diagnosis and stage of their disease. Survival is dependent on patient characteristics, disease specifics, and care received (surgery, systemic therapies, radiation). Some argue survival is the ultimate measure of cancer care quality. Survival calculated from registry data may be a better indication of the real effects of new therapies as it contains a broad representation of patients who may not meet the eligibility criteria for clinical trials. Methods: The NCDB is composed of registry data from approximately 1,500 hospital based cancer programs, and includes stage, demographic data, co-morbidities, treatments, and vital status. Survival analyses were derived from NCDB data for all stages of breast cancer, non-small cell lung cancer (NSCLC), and pancreatic cancer. These diseases were selected because breast cancer has a high survival rate, whereas NSCLC and pancreatic cancer are diseases with a poor prognosis, but recent advances may have improved survival. Un-adjusted and risk adjusted survival were analyzed by socioeconomic, tumor, and hospital factors including stage, comorbidities, diagnosis year (to assess new treatment trends), and type of institution (academic, comprehensive community, and community cancer programs). Results: Results for these diseases and variables noted will be presented. Whereas better un-adjusted survival rates were often seen at academic cancer programs, differences disappeared after risk-adjustment. Improved survival was seen in more recent years, probably representing new treatment effects, though gains were modest and stage dependent. Conclusions: Measuring survival across hospitals and regions is critical to understanding the state of cancer treatment nationally and the effect of quality and therapy advances on patients across a variety of clinical settings. Methodologic challenges in analyzing and interpreting survival data must be realized, and continued innovation in the collection and analysis of data is needed.

Objective: To examine the association of hospital volume and 90‐day mortality after cystectomy, conditional on survival for 30 days. Patients and Methods: The N ational C ancer D ata B ase was used to evaluate 30‐ and 90‐day mortality for 35 055 patients who underwent cystectomy for bladder cancer at one of 1118 hospitals. Patient data were aggregated into hospital volume categories based on the mean annual number of procedures (low‐volume hospital: <10 procedures; intermediate‐volume hospital: 10–19 procedures; high‐volume hospital: ≥20 procedures). Associations between mortality and clinical, demographic and hospital characteristics were analysed using hierarchical logistic regression models. To assess the association between hospital volume and 90‐day mortality independently of shorter‐term mortality, 90‐day mortality conditional on 30‐day survival was assessed in the multivariate modelling. Results: Unadjusted 30‐ and 90‐day mortality rates were 2.7 and 7.2% overall, 1.9 and 5.7% among high‐volume hospitals, and 3.2 and 8.0% among low‐volume hospitals, respectively. Compared with high‐volume hospitals, the adjusted risks among low‐volume hospitals (odds ratio [95% CI ]) of 30‐ and 90‐day mortality, conditional on having survived for 30 days, from the hierarchical models were 1.5 (1.3–1.9), and 1.2 (1.0–1.4), respectively. Conclusions: A low hospital volume was associated with greater 30‐ and 90‐day mortality. These data support the need for further research to better understand the relatively high mortality rates seen between 30 and 90 days, which are high and less variable across hospital volume strata. The stronger association between volume and 30‐day mortality suggests that quality‐reporting efforts should focus on shorter‐term outcomes.