TY - JOUR
T1 - Postoperative 30-day mortality in patients undergoing surgery for colorectal cancer
T2 - development of a prognostic model using administrative claims data
AU - de Vries, S.
AU - Jeffe, D. B.
AU - Davidson, N. O.
AU - Deshpande, A. D.
AU - Schootman, M.
N1 - Funding Information:
Acknowledgments This work was supported by grants from the National Cancer Institute at the National Institutes of Health (Grant Number CA112159); and the Health Behavior, Communication, and Outreach Core; the Core is supported in part by the National Cancer Institute Cancer Center Support Grant (Grant Number P30 CA91842) to the Alvin J. Siteman Cancer Center at Washington University School of Medicine and Barnes-Jewish Hospital in St. Louis, Missouri. Dr. Davidson was supported in part through Grants HL-38180, DK-56260, and Digestive Disease Research Core Center DK-52574. We gratefully acknowledge James Struthers for his data management and programming services. We thank the Alvin J. Siteman Cancer Center at Barnes-Jewish Hospital and Washington University School of Medicine in St. Louis, Missouri, for the use of the Health Behavior, Communication, and Outreach Core. This study used the linked SEER-Medicare database. The interpretation and reporting of these data are the sole responsibility of the authors. The authors acknowledge the efforts of the Applied Research Program, NCI; the Office of Research, Development and Information, CMS; Information Management Services (IMS), Inc.; and the Surveillance, Epidemiology, and End Results (SEER) Program tumor registries in the creation of the SEER-Medicare database.
Publisher Copyright:
© 2014, Springer International Publishing Switzerland.
PY - 2014/10/31
Y1 - 2014/10/31
N2 - Purpose: To develop a prognostic model to predict 30-day mortality following colorectal cancer (CRC) surgery using the Surveillance, Epidemiology, and End Results (SEER)-Medicare-linked data and to assess whether race/ethnicity, neighborhood, and hospital characteristics influence model performance.Methods: We included patients aged 66 years and older from the linked 2000–2005 SEER-Medicare database. Outcome included 30-day mortality, both in-hospital and following discharge. Potential prognostic factors included tumor, treatment, sociodemographic, hospital, and neighborhood characteristics (census-tract-poverty rate). We performed a multilevel logistic regression analysis to account for nesting of CRC patients within hospitals. Model performance was assessed using the area under the receiver operating characteristic curve (AUC) for discrimination and the Hosmer–Lemeshow goodness-of-fit test for calibration.Results: In a model that included all prognostic factors, important predictors of 30-day mortality included age at diagnosis, cancer stage, and mode of presentation. Race/ethnicity, census-tract-poverty rate, and hospital characteristics were independently associated with 30-day mortality, but they did not influence model performance. Our SEER-Medicare model achieved moderate discrimination (AUC = 0.76), despite suboptimal calibration.Conclusions: We developed a prognostic model that included tumor, treatment, sociodemographic, hospital, and neighborhood predictors. Race/ethnicity, neighborhood, and hospital characteristics did not improve model performance compared with previously developed models.
AB - Purpose: To develop a prognostic model to predict 30-day mortality following colorectal cancer (CRC) surgery using the Surveillance, Epidemiology, and End Results (SEER)-Medicare-linked data and to assess whether race/ethnicity, neighborhood, and hospital characteristics influence model performance.Methods: We included patients aged 66 years and older from the linked 2000–2005 SEER-Medicare database. Outcome included 30-day mortality, both in-hospital and following discharge. Potential prognostic factors included tumor, treatment, sociodemographic, hospital, and neighborhood characteristics (census-tract-poverty rate). We performed a multilevel logistic regression analysis to account for nesting of CRC patients within hospitals. Model performance was assessed using the area under the receiver operating characteristic curve (AUC) for discrimination and the Hosmer–Lemeshow goodness-of-fit test for calibration.Results: In a model that included all prognostic factors, important predictors of 30-day mortality included age at diagnosis, cancer stage, and mode of presentation. Race/ethnicity, census-tract-poverty rate, and hospital characteristics were independently associated with 30-day mortality, but they did not influence model performance. Our SEER-Medicare model achieved moderate discrimination (AUC = 0.76), despite suboptimal calibration.Conclusions: We developed a prognostic model that included tumor, treatment, sociodemographic, hospital, and neighborhood predictors. Race/ethnicity, neighborhood, and hospital characteristics did not improve model performance compared with previously developed models.
KW - Administrative claims
KW - Colorectal cancer
KW - Mortality
KW - Prognostic model
KW - SEER-Medicare
UR - http://www.scopus.com/inward/record.url?scp=84911962443&partnerID=8YFLogxK
U2 - 10.1007/s10552-014-0451-x
DO - 10.1007/s10552-014-0451-x
M3 - Article
C2 - 25104569
AN - SCOPUS:84911962443
VL - 25
SP - 1503
EP - 1512
JO - Cancer Causes and Control
JF - Cancer Causes and Control
SN - 0957-5243
IS - 11
ER -