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Postoperative 30-day mortality in patients undergoing surgery for colorectal cancer: development of a prognostic model using administrative claims data

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Abstract

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.

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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.

Conflict of interest

The authors state that they have no conflict of interest.

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Authors and Affiliations

  1. Division of Health Behavior Research, Department of Medicine, Washington University School of Medicine, Saint Louis, MO, USA

    S. de Vries, D. B. Jeffe & A. D. Deshpande

  2. Alvin J. Siteman Cancer Center at Barnes-Jewish Hospital, Washington University School of Medicine, Saint Louis, MO, USA

    D. B. Jeffe, N. O. Davidson, A. D. Deshpande & M. Schootman

  3. Division of Gastroenterology, Department of Medicine, Washington University School of Medicine, Saint Louis, MO, USA

    N. O. Davidson

  4. Department of Epidemiology, College for Public Health and Social Justice, Saint Louis University, St. Louis, MO, USA

    M. Schootman

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  1. S. de Vries
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Correspondence to M. Schootman.

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de Vries, S., Jeffe, D.B., Davidson, N.O. et al. Postoperative 30-day mortality in patients undergoing surgery for colorectal cancer: development of a prognostic model using administrative claims data. Cancer Causes Control 25, 1503–1512 (2014). https://doi.org/10.1007/s10552-014-0451-x

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  • DOI: https://doi.org/10.1007/s10552-014-0451-x

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