title: Hospital characteristics, clinical severity, and outcomes for surgical oncology patients.
abstract: BACKGROUND
  Patients and payers wish to identify hospitals with good surgical oncology outcomes. Our objective was to determine whether differences in outcomes explained by hospital structural characteristics are mitigated by differences in patient severity.
  
  
  METHODS
  Using hospital administrative and cancer registry records in Pennsylvania, we identified 24,618 adults hospitalized for cancer-related operations. Colorectal, prostate, endometrial, ovarian, head and neck, lung, esophageal, and pancreatic cancers were studied. Outcome measures were 30-day mortality and failure to rescue (FTR) (30-day mortality preceded by a complication). After severity of illness adjustment, we estimated logistic regression models to predict the likelihood of both outcomes. In addition to American Hospital Association survey data, we externally verified hospitals with National Cancer Institute (NCI) cancer center or Commission on Cancer (COC) cancer program status.
  
  
  RESULTS
  Patients in hospitals with NCI cancer centers were significantly younger and less acutely ill on admission (P < .001). Patients in high volume hospitals were younger, had lower admission acuity, yet had more advanced cancer (P < .001). Unadjusted 30-day mortality rates were lower in NCI-designated hospitals (3.76% vs 2.17%;P = .01). Risk-adjusted FTR rates were significantly lower in NCI-designated hospitals (4.86% vs 3.51%;P = .03). NCI center designation was a significant predictor of 30-day mortality when considering patient and hospital characteristics (OR, 0.68; 95% CI, 0.47-0.97;P = .04). We did not find significant outcomes effects based on COC cancer program approval.
  
  
  CONCLUSIONS
  Patient severity of illness varies significantly across hospitals, which may explain the outcome differences observed. Severity adjustment is crucial to understanding outcome differences. Outcomes were better than predicted for NCI-designated hospitals.
authors.affiliation: Division of Nursing Business and Health Systems, School of Nursing, University of Michigan, Ann Arbor, MI 48109-5482, USA. cfriese@umich.edu
authors.forename: Christopher R
authors.initials: CR
authors.lastname: Friese
authors.forename: Craig C
authors.initials: CC
authors.lastname: Earle
authors.forename: Jeffrey H
authors.initials: JH
authors.lastname: Silber
authors.forename: Linda H
authors.initials: LH
authors.lastname: Aiken
mesh_terms: D000368:Aged; D002173:Cancer Care Facilities / Q000592:standards / Q000706:statistics & numerical data*; D015897:Comorbidity; D006742:Hospital Bed Capacity / Q000592:standards / Q000706:statistics & numerical data; D017052:Hospital Mortality; D006784:Hospitals, Teaching / Q000592:standards / Q000706:statistics & numerical data*; D006801:Humans; D008875:Middle Aged; D009369:Neoplasms / Q000401:mortality* / Q000601:surgery*; D017063:Outcome Assessment, Health Care; D010414:Pennsylvania / Q000453:epidemiology; D011237:Predictive Value of Tests; D012042:Registries; D012307:Risk Factors; D012720:Severity of Illness Index; D013503:Surgery Department, Hospital / Q000592:standards / Q000706:statistics & numerical data
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