gms | German Medical Science

62. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS)

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie

17.09. - 21.09.2017, Oldenburg

Bayesian survival analysis of colorectal cancer registry data

Meeting Abstract

  • Verena Jürgens - Carl von Ossietzky Universität Oldenburg, Oldenburg, Deutschland
  • Antje Timmer - Carl von Ossietzky Universität Oldenburg, Oldenburg, Deutschland
  • Verena Uslar - Universitätsklinik für Viszeralchirurgie, Pius-Hospital Oldenburg, Oldenburg, Deutschland
  • Kristin Eilermann - Pius-Hospital Oldenburg, Oldenburg, Deutschland
  • Andrea Tannapfel - Institut für Pathologie, Ruhr-Universität Bochum, Bochum, Deutschland
  • Dirk Weyhe - Universitätsklinik für Viszeralchirurgie, Pius-Hospital Oldenburg, Oldenburg, Deutschland

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie. 62. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (GMDS). Oldenburg, 17.-21.09.2017. Düsseldorf: German Medical Science GMS Publishing House; 2017. DocAbstr. 259

doi: 10.3205/17gmds128, urn:nbn:de:0183-17gmds1289

Veröffentlicht: 29. August 2017

© 2017 Jürgens et al.
Dieser Artikel ist ein Open-Access-Artikel und steht unter den Lizenzbedingungen der Creative Commons Attribution 4.0 License (Namensnennung). Lizenz-Angaben siehe http://creativecommons.org/licenses/by/4.0/.


Gliederung

Text

Introduction: Survival analysis provides several modeling approaches to analyze cancer registry data. Model choice depends on the underlying data.

In colorectal cancer, prognosis strongly depends on radicality of the surgical therapy. The extent of lymph node metastasis can be described by the pN-classification, whereas the quality of the regional radical surgery is substantially indicated by the number of detected lymph nodes.

Methods: Explorative data analysis was followed by a variable and model selection. Finally, we applied a bayesian accelerated failure time model assuming a weibull-distributed survival function. Survival time in months was modeled depending on seven main effects and one interaction. Data analysis was done in R and WinBUGS.

Results: During 01/2010–12/2014 a total of 470 patients were registered in a hospital colorectal cancer center providing a 100%-follow-up. The median age was 72 years (range 24 to 95). Overall, 148 (31.5%) patients died, 15.1% died due to cancer and 16.4% due to other/unknown reasons. A median number of 23 lymph nodes were detected by histology. Survival time of the colorectal cancer patients strongly depended on lymph node ratio (number of positive lymph nodes divided by the total number of removed lymph nodes), UICC-stage, age and chemotherapy (yes/no). An acceleration factor of 0.21 (95% CI: 0.08–0.47) was observed for age group >80 years (baseline age group <60 years). The estimated acceleration factor strongly decreased with increasing lymph node ratio. An acceleration factor of 6.23 (95% CI: 3.31–12.0) was observed for those receiving chemotherapy compared to those who did not.

Discussion: Our results show the relevance of the lymph node ratio as a predictor of colorectal cancer survival. This result highlights the importance of the request of removing and pathologically detecting a large number of lymph nodes.



Die Autoren geben an, dass kein Interessenkonflikt besteht.

Die Autoren geben an, dass kein Ethikvotum erforderlich ist.

Der Beitrag wurde bereits vorgestellt: DGVS 2016