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Poster display session

4839 - Improving visualization and adherence by converting the Dutch colorectal cancer guidelines into decision trees: the Oncoguide project

Date

09 Sep 2017

Session

Poster display session

Presenters

Martijn van Oijen

Citation

Annals of Oncology (2017) 28 (suppl_5): v158-v208. 10.1093/annonc/mdx393

Authors

M.G. van Oijen1, X.A. Verbeek2, T. van Vegchel2, I.D. Nagtegaal3, M.J. Lahaye4, A. Méndez Romero5, H. Rütten6, S. de Bruijn7, H.M. Verheul8, P.J. Tanis9, C.J..A. Punt10, L. Keikes10

Author affiliations

  • 1 Medical Oncology, Academic Medical Center (AMC), 1105AZ - Amsterdam/NL
  • 2 Research, Comprehensive Cancer Center Netherlands, Utrecht/NL
  • 3 Pathology, Radboud University Medical Centre Nijmegen, 6500 HB - Nijmegen/NL
  • 4 Radiology, Netherlands Cancer Institute, Amsterdam/NL
  • 5 Radiation Oncology, Erasmus MC, Rotterdam/NL
  • 6 Radiation Oncology, Radboud University Medical Centre Nijmegen, 6500 HB - Nijmegen/NL
  • 7 Gastroenterology, Reinier de Graaf Hospital, Delft/NL
  • 8 Medical Oncology, Vrije University Medical Centre (VUMC), 1081 HV - Amsterdam/NL
  • 9 Surgery, Academic Medical Center (AMC), 1105AZ - Amsterdam/NL
  • 10 Medical Oncology, Academic Medical Center, 1105AZ - Amsterdam/NL
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Resources

Abstract 4839

Background

Clinical guidelines are designed to prevent undesired practice variation where high quality evidence or expert consensus is available. However, reading and interpretation of text-based guidelines is time-consuming and might be difficult to apply in routine daily practice. Therefore, the aim of our study is to examine the feasibility of converting the Dutch multidisciplinary colorectal cancer guideline recommendations into data driven algorithms (decision trees) to facilitate guideline usage.

Methods

We converted the most recent Dutch colorectal cancer guideline (published in 2014) into decision trees modelled by decision nodes representing patient or disease characteristics ultimately branching into guideline recommendations. Where not evidence-based, decision trees were discussed with an expert panel until agreement was reached. Thereafter, the developed decision trees were published in open access decision support software.

Results

In total, we developed 34 decision trees driven by 101 decision nodes. Decision trees focused on recommendations for diagnostics (n = 1) staging (n = 10), treatment (colon: n = 1, rectum: n = 5, both: n = 9), pathology (n = 4), follow-up (n = 3) and 1 overview decision tree. We identified guideline recommendation information gaps, for example specific surgical policy related to (the number of) lung metastases, a recommendation about follow-up schemes after resection or local treatment (e.g. RFA) of metastases and the period between neo-adjuvant treatment and re-staging. It was difficult to convert some of the guideline recommendations into decision trees (i.e. ‘consider PET-CT scan to exclude extrahepatic metastases’), related to non-conclusive evidence on specific topics.

Conclusions

Converting the Dutch colorectal cancer guideline into decision trees is feasible, but presents several challenges. Using decision trees may (I) improve guideline adherence or more conscious guideline deviation; (II) improve guideline (adherence) evaluation from cancer registries; and (III) ultimately learn from clinical cases with documented motivation for guideline deviation.

Clinical trial identification

Legal entity responsible for the study

Comprehensive Cancer Center Netherlands

Funding

Academic Medical Center - University of Amsterdam

Disclosure

All authors have declared no conflicts of interest.

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