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Jong-Hyock Park 1 Article
Improving the Performance of Risk-adjusted Mortality Modeling for Colorectal Cancer Surgery by Combining Claims Data and Clinical Data
Won Mo Jang, Jae-Hyun Park, Jong-Hyock Park, Jae Hwan Oh, Yoon Kim
J Prev Med Public Health. 2013;46(2):74-81.   Published online March 28, 2013
DOI: https://doi.org/10.3961/jpmph.2013.46.2.74
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  • 6 Crossref
AbstractAbstract PDF
Objectives

The objective of this study was to evaluate the performance of risk-adjusted mortality models for colorectal cancer surgery.

Methods

We investigated patients (n=652) who had undergone colorectal cancer surgery (colectomy, colectomy of the rectum and sigmoid colon, total colectomy, total proctectomy) at five teaching hospitals during 2008. Mortality was defined as 30-day or in-hospital surgical mortality. Risk-adjusted mortality models were constructed using claims data (basic model) with the addition of TNM staging (TNM model), physiological data (physiological model), surgical data (surgical model), or all clinical data (composite model). Multiple logistic regression analysis was performed to develop the risk-adjustment models. To compare the performance of the models, both c-statistics using Hanley-McNeil pair-wise testing and the ratio of the observed to the expected mortality within quartiles of mortality risk were evaluated to assess the abilities of discrimination and calibration.

Results

The physiological model (c=0.92), surgical model (c=0.92), and composite model (c=0.93) displayed a similar improvement in discrimination, whereas the TNM model (c=0.87) displayed little improvement over the basic model (c=0.86). The discriminatory power of the models did not differ by the Hanley-McNeil test (p>0.05). Within each quartile of mortality, the composite and surgical models displayed an expected mortality ratio close to 1.

Conclusions

The addition of clinical data to claims data efficiently enhances the performance of the risk-adjusted postoperative mortality models in colorectal cancer surgery. We recommended that the performance of models should be evaluated through both discrimination and calibration.

Summary

Citations

Citations to this article as recorded by  
  • Estimating postoperative mortality in colorectal surgery- a systematic review of risk prediction models
    Alexios Dosis, Jack Helliwell, Aron Syversen, Jim Tiernan, Zhiqiang Zhang, David Jayne
    International Journal of Colorectal Disease.2023;[Epub]     CrossRef
  • Modified Tumor Budding as a Better Predictor of Lymph Node Metastasis in Early Gastric Cancer: Possible Real-World Applications
    Kwangil Yim, Won Mo Jang, Sung Hak Lee
    Cancers.2021; 13(14): 3405.     CrossRef
  • Investigación epidemiológica en cáncer colorrectal: perspectiva, prospectiva y retos bajo la óptica de explotación del Big-Data
    J.M. García Torrecillas, M. Ferrer Márquez, Á. Reina Duarte, F. Rubio-Gil
    SEMERGEN - Medicina de Familia.2016; 42(8): 509.     CrossRef
  • Variation between Hospitals with Regard to Diagnostic Practice, Coding Accuracy, and Case-Mix. A Retrospective Validation Study of Administrative Data versus Medical Records for Estimating 30-Day Mortality after Hip Fracture
    Jon Helgeland, Doris Tove Kristoffersen, Katrine Damgaard Skyrud, Anja Schou Lindman, Alanna M Chamberlain
    PLOS ONE.2016; 11(5): e0156075.     CrossRef
  • Model for risk adjustment of postoperative mortality in patients with colorectal cancer
    K Walker, P J Finan, J H van der Meulen
    British Journal of Surgery.2015; 102(3): 269.     CrossRef
  • Problems With Public Reporting of Cancer Quality Outcomes Data
    Paul Goldberg, Rena M. Conti
    Journal of Oncology Practice.2014; 10(3): 215.     CrossRef

JPMPH : Journal of Preventive Medicine and Public Health