Multistate clinical prediction models in renal replacement therapy

临床预测模型(CPM)是一种预测患者未来风险的工具。现有的方案管理一般基于单一的结果或决策点。例如，在外科手术过程中心血管事件或死亡的未来风险。然而，在许多情况下，我们面临着一系列复杂的事件和决定，因此我们希望预测多种结果，以优化治疗。一个这样的例子是肾脏替代疗法(RRT)，这是对肾衰竭患者可用的治疗的通用术语。这些患者面临许多治疗选择(包括移植和透析)，并可能随着时间的推移在不同的治疗之间流动。从统计学上讲，TTI可以用一个多状态模型来描述。在这一领域，临床预测的统计方法还不够发达。预计这种对多态模型临床预测方法的改进有可能改善治疗，特别是在RRT中。我们的目标是：1.回顾CPM和多态建模的文献，以确定在多态框架中应用CPM的可能性。利用英国肾脏登记处的数据，开发一个多状态模型来描述患者在RRT中的治疗流程，该数据包含了英国所有RRT患者的数据。使用开发的多态模型定义肾脏替代疗法(RRT)患者预后的病因驱动因素4。使用开发的多态模型，结合CPM方法学，得出优化RRT患者治疗的“决策辅助”。在外部验证开发的国际肾脏注册决策辅助/CPM(例如澳大利亚/新西兰、瑞典)。我们预计该项目的结果/影响将是：通过多状态建模提供对RRT患者当前“旅程”的独特洞察。提供一个工具，可用于优化这些复杂患者的动态治疗方案，以改善预后。统计方法，特别是围绕预测建模和决策支持之间的联系，将在其他疾病和应用领域看到更广泛的影响。

A clinical prediction model (CPM) is a tool to predict future risk for patients. Existing CPMs are generally based on a single outcome or decision point. For example, the future risk of cardiovascular events or death during a surgical procedure. However, in many situations we are faced with a complex sequence of events and decisions, so we would like to predict multiple outcomes to optimise treatment. One such example is renal replacement therapy (RRT), which is a general term for treatments available to patients with kidney failure. These patients are faced with many treatment options (including transplant and dialysis) and may move between different treatments over time. Statistically,Ttis can be characterised by a multi-state model. Statistical methodology for clinical prediction to inform optimal treatment is underdeveloped in this area.It is expected that this improvement to methodology for clinical prediction in multi-state models has the potential to improve treatment, particularly in RRT.Our objectives are to:1. Review literature of CPMs and multi-state modelling to identify the potential to apply CPMs in a multi-state framework.2. Develop a multi-state model to describe the flows of patients through treatments in RRT, using data from the UK Renal Registry, which contains data on all patients in the UK on RRT.3. Use the developed multi-state model to define aetiological drivers of outcomes in patients on renal replacement therapy (RRT)4. Use the developed multi-state model, combined with CPM methodology, to derive a 'decision aid' for optimising treatment for RRT patients.5. Externally validate the developed decision aid/CPM on international renal registries (e.g. Australia/New Zealand, Sweden).We expect the outcomes/impact of the project to be:Provide a unique insight into the current 'journey' of RRT patients through multi-state modelling.Provide a tool which can be used to optimise dynamic treatment regimens for these complex patients to improve prognosis.Statistical methodology, particularly around the link between predictive modelling and decision support, that will see wider impact in other disease and application areas.