Suner A, Karakülah G, Dicle O, Sökmen S, Çelikoğlu C C
Ege University, School of Medicine , Department of Biostatistics and Medical Informatics, Bornova-Izmir, 35040, Turkey.
Neurobiology-Neurodegeneration and Repair Laboratory , National Eye Institute, National Institutes of Health , Bethesda, Maryland, 20892, USA ; Dokuz Eylül University, Health Sciences Institute , Department of Medical Informatics, Inciraltı-Izmir, 35340, Turkey.
Appl Clin Inform. 2015 Feb 4;6(1):56-74. doi: 10.4338/ACI-2014-10-RA-0087. eCollection 2015.
The selection of appropriate rectal cancer treatment is a complex multi-criteria decision making process, in which clinical decision support systems might be used to assist and enrich physicians' decision making.
The objective of the study was to develop a web-based clinical decision support tool for physicians in the selection of potentially beneficial treatment options for patients with rectal cancer.
The updated decision model contained 8 and 10 criteria in the first and second steps respectively. The decision support model, developed in our previous study by combining the Analytic Hierarchy Process (AHP) method which determines the priority of criteria and decision tree that formed using these priorities, was updated and applied to 388 patients data collected retrospectively. Later, a web-based decision support tool named corRECTreatment was developed. The compatibility of the treatment recommendations by the expert opinion and the decision support tool was examined for its consistency. Two surgeons were requested to recommend a treatment and an overall survival value for the treatment among 20 different cases that we selected and turned into a scenario among the most common and rare treatment options in the patient data set.
In the AHP analyses of the criteria, it was found that the matrices, generated for both decision steps, were consistent (consistency ratio<0.1). Depending on the decisions of experts, the consistency value for the most frequent cases was found to be 80% for the first decision step and 100% for the second decision step. Similarly, for rare cases consistency was 50% for the first decision step and 80% for the second decision step.
The decision model and corRECTreatment, developed by applying these on real patient data, are expected to provide potential users with decision support in rectal cancer treatment processes and facilitate them in making projections about treatment options.
选择合适的直肠癌治疗方法是一个复杂的多标准决策过程，临床决策支持系统可用于协助并丰富医生的决策。
本研究的目的是为医生开发一个基于网络的临床决策支持工具，用于为直肠癌患者选择潜在有益的治疗方案。
更新后的决策模型在第一步和第二步分别包含8个和10个标准。通过结合确定标准优先级的层次分析法（AHP）和使用这些优先级形成的决策树，在我们之前的研究中开发的决策支持模型被更新，并应用于回顾性收集的388例患者数据。随后，开发了一个名为corRECTreatment的基于网络的决策支持工具。检查专家意见和决策支持工具的治疗建议的兼容性，以确定其一致性。要求两名外科医生针对我们选择的20个不同病例推荐一种治疗方法，并给出该治疗方法的总生存价值，这些病例被转化为患者数据集中最常见和最罕见治疗方案中的一种情况。
在标准的AHP分析中，发现为两个决策步骤生成的矩阵都是一致的（一致性比率<0.1）。根据专家的决策，发现最常见病例在第一个决策步骤的一致性值为80%，在第二个决策步骤为100%。同样，对于罕见病例，第一个决策步骤的一致性为50%，第二个决策步骤为80%。
通过将这些应用于真实患者数据而开发的决策模型和corRECTreatment，有望在直肠癌治疗过程中为潜在用户提供决策支持，并帮助他们对治疗方案进行预测。
