ArticleInternational journal for quality in health care : journal of the International Society for Quality in Health Care2021
Conversion of a colorectal cancer guideline into clinical decision trees with assessment of validity.
Article in International journal for quality in health care : journal of the International Society for Quality in Health Care, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.
- Leveraging artificial intelligence to support surgical oncology multidisciplinary team decision-making: a systematic review.Frontiers in artificial intelligence · 2026Pooled it
- The development and evaluation of a quality assessment framework for reuse of dietary intake data: an FNS-Cloud study.Frontiers in nutrition · 2025Article
- Construction of a Food Safety Evaluation System Based on the Factor Analysis of Mixed Data Method.Foods (Basel, Switzerland) · 2024Article
- Colorectal cancer prognosis based on dietary pattern using synthetic minority oversampling technique with K-nearest neighbors approach.Scientific reports · 2024Article
- Treatment outcomes of patients with BRAFContemporary oncology (Poznan, Poland) · 2024Article
- Guideline- Versus Non-Guideline-Based Neoadjuvant Management of Clinical T4 Rectal Cancer.Current oncology (Toronto, Ont.) · 2023Article
- Use and accuracy of decision support systems using artificial intelligence for tumor diseases: a systematic review and meta-analysis.Frontiers in oncology · 2023Review
- Using guideline-based clinical decision support in oncological multidisciplinary team meetings: A prospective, multicenter concordance study.International journal for quality in health care : journal of the International Society for Quality in Health Care · 2022Observational
- Construction of a Non-Mutually Exclusive Decision Tree for Medication Recommendation of Chronic Heart Failure.Frontiers in pharmacology · 2021Article
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Authors and funding
13 authors at 7 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe interpretation and clinical application of guidelines can be challenging and time-consuming, which may result in noncompliance to guidelines. The aim of this study was to convert the Dutch guideline for colorectal cancer (CRC) into decision trees and subsequently implement decision trees in an online decision support environment to facilitate guideline application.
methodsThe recommendations of the Dutch CRC guidelines (published in 2014) were translated into decision trees consisting of decision nodes, branches and leaves that represent data items, data item values and recommendations, respectively. Decision trees were discussed with experts in the field and published as interactive open access decision support software (available at www.oncoguide.nl). Decision tree validation and a concordance analysis were performed using consecutive reports (January 2016-January 2017) from CRC multidisciplinary tumour boards (MTBs) at Amsterdam University Medical Centers, location AMC.
resultsIn total, we developed 34 decision trees driven by 101 decision nodes based on the guideline recommendations. Decision trees represented recommendations for diagnostics (n = 1), staging (n = 10), primary treatment (colon: n = 1, rectum: n = 5, colorectal: n = 9), pathology (n = 4) and follow-up (n = 3) and included one overview decision tree for optimal navigation. We identified several guideline information gaps and areas of inconclusive evidence. A total of 158 patients' MTB reports were eligible for decision tree validation and resulted in treatment recommendations in 80% of cases. The concordance rate between decision tree treatment recommendations and MTB advices was 81%. Decision trees reported in 22 out of 24 non-concordant cases (92%) that no guideline recommendation was available.
conclusionsWe successfully converted the Dutch CRC guideline into decision trees and identified several information gaps and areas of inconclusive evidence, the latter being the main cause of the observed disagreement between decision tree recommendations and MTB advices. Decision trees may contribute to future strategies to optimize quality of care for CRC patients.
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