Economic modelling of diagnostic and treatment pathways in National Institute for Health and Care Excellence clinical guidelines: the Modelling Algorithm Pathways in Guidelines (MAPGuide) project
Economic modelling of diagnostic and treatment pathways in National Institute for Health and Care Excellence clinical guidelines: the Modelling Algorithm Pathways in Guidelines (MAPGuide) project
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DOI：
10.3310/hta17580
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发表时间：
2013-12-01
影响因子：
3.6
通讯作者：
Ruiz, F.
中科院分区：
文献类型：
--
作者：
Lord, J.;Willis, S.;Ruiz, F.
背景资料：国家健康与护理卓越研究所（NICE）临床指南（CG）为广泛的患者群体提供了大型复杂的护理途径建议。它们依赖于从文献和对选定的高度优先主题的新分析中获得的成本效益证据。另一种方法是建立一个全面护理路径的模型，并将其作为一个平台，以评估整个指南建议中多个主题的成本效益。目标：在本项目中，我们旨在测试为NICE指南建立全面指南模型的可行性，并评估此类模型是否以及如何用作成本效益分析（CEA）的基础。数据来源：采用“最佳证据”方法确定模型参数。数据来自指南文件、临床专家的建议和选定主题的快速文献综述。在可能的情况下，我们依靠高质量，最近英国的系统评价和荟萃analysis.Review方法：两个出版的NICE指南被用作案例研究：前列腺癌和心房颤动（AF）。离散事件模拟（DES）被用来模拟推荐的护理路径，并估计随之而来的成本和结果。对于每一项指南，没有参与模型开发的研究人员整理了一份建议更新的主题清单。建模小组随后试图评估与这些专题有关的备选办法。成本效益的结果进行了比较意见的重要性，在调查的利益相关者。结果：建模团队开发的模拟的指导方针的途径和疾病的过程。开发所需的时间和分析时间比预期的要长。成本效益的估计产生了六个考虑的九个前列腺癌的主题，和五个八个AF的主题。由于缺乏数据或时间限制，没有对其他专题进行评价。模拟结果建议更新的“经济优先事项”与利益相关者调查中表达的优先事项不同。局限性：我们没有进行系统的审查，以告知模型参数，因此结果可能无法反映所有当前的证据。数据限制和时间限制限制了我们可以进行的分析数量。我们也无法获得反馈意见，从指导方针的利益相关者的有用性的模型在项目timescales.Conclusions：离散事件模拟可以用来模拟完整的指导方针途径CEA，虽然这需要大量的临床和分析时间和专业知识的投资。对于某些专题，缺乏数据可能会限制建模的潜力。在完整指南模型的可访问性和适应性方面也存在不确定性。然而，完整的指南建模提供了加强和扩展NICE的CG的分析基础的潜力。需要进一步的工作来扩展我们的案例研究模型的分析，以估计人口水平的预算和健康影响。我们的模型指南开发人员和用户的实用性也应该进行调查，因为应该的可行性和实用性的整个指南建模，同时开发一个新的CG。
Background: National Institute for Health and Care Excellence (NICE) clinical guidelines (CGs) make recommendations across large, complex care pathways for broad groups of patients. They rely on cost-effectiveness evidence from the literature and from new analyses for selected high-priority topics. An alternative approach would be to build a model of the full care pathway and to use this as a platform to evaluate the cost-effectiveness of multiple topics across the guideline recommendations.Objectives: In this project we aimed to test the feasibility of building full guideline models for NICE guidelines and to assess if, and how, such models can be used as a basis for cost-effectiveness analysis (CEA).Data sources: A 'best evidence' approach was used to inform the model parameters. Data were drawn from the guideline documentation, advice from clinical experts and rapid literature reviews on selected topics. Where possible we relied on good-quality, recent UK systematic reviews and meta-analyses.Review methods: Two published NICE guidelines were used as case studies: prostate cancer and atrial fibrillation (AF). Discrete event simulation (DES) was used to model the recommended care pathways and to estimate consequent costs and outcomes. For each guideline, researchers not involved in model development collated a shortlist of topics suggested for updating. The modelling teams then attempted to evaluate options related to these topics. Cost-effectiveness results were compared with opinions about the importance of the topics elicited in a survey of stakeholders.Results: The modelling teams developed simulations of the guideline pathways and disease processes. Development took longer and required more analytical time than anticipated. Estimates of cost-effectiveness were produced for six of the nine prostate cancer topics considered, and for five of eight AF topics. The other topics were not evaluated owing to lack of data or time constraints. The modelled results suggested 'economic priorities' for an update that differed from priorities expressed in the stakeholder survey.Limitations: We did not conduct systematic reviews to inform the model parameters, and so the results might not reflect all current evidence. Data limitations and time constraints restricted the number of analyses that we could conduct. We were also unable to obtain feedback from guideline stakeholders about the usefulness of the models within project time scales.Conclusions: Discrete event simulation can be used to model full guideline pathways for CEA, although this requires a substantial investment of clinical and analytic time and expertise. For some topics lack of data may limit the potential for modelling. There are also uncertainties over the accessibility and adaptability of full guideline models. However, full guideline modelling offers the potential to strengthen and extend the analytical basis of NICE's CGs. Further work is needed to extend the analysis of our case study models to estimate population-level budget and health impacts. The practical usefulness of our models to guideline developers and users should also be investigated, as should the feasibility and usefulness of whole guideline modelling alongside development of a new CG.