Pöhlmann Johannes, Bergenheim Klas, Garcia Sanchez Juan-Jose, Rao Naveen, Briggs Andrew, Pollock Richard F
Covalence Research Ltd, 51 Hayes Grove, London, SE22 8DF, UK.
Global Market Access and Pricing, BioPharmaceuticals, AstraZeneca, Gothenburg, Sweden.
Diabetes Ther. 2022 Apr;13(4):651-677. doi: 10.1007/s13300-022-01208-0. Epub 2022 Mar 15.
As novel therapies for chronic kidney disease (CKD) in type 2 diabetes mellitus (T2DM) become available, their long-term benefits should be evaluated using CKD progression models. Existing models offer different modeling approaches that could be reused, but it may be challenging for modelers to assess commonalities and differences between the many available models. Additionally, the data and underlying population characteristics informing model parameters may not always be evident. Therefore, this study reviewed and summarized existing modeling approaches and data sources for CKD in T2DM, as a reference for future model development.
This systematic literature review included computer simulation models of CKD in T2DM populations. Searches were implemented in PubMed (including MEDLINE), Embase, and the Cochrane Library, up to October 2021. Models were classified as cohort state-transition models (cSTM) or individual patient simulation (IPS) models. Information was extracted on modeled kidney disease states, risk equations for CKD, data sources, and baseline characteristics of derivation cohorts in primary data sources.
The review identified 49 models (21 IPS, 28 cSTM). A five-state structure was standard among state-transition models, comprising one kidney disease-free state, three kidney disease states [frequently including albuminuria and end-stage kidney disease (ESKD)], and one death state. Five models captured CKD regression and three included cardiovascular disease (CVD). Risk equations most commonly predicted albuminuria and ESKD incidence, while the most predicted CKD sequelae were mortality and CVD. Most data sources were well-established registries, cohort studies, and clinical trials often initiated decades ago in predominantly White populations in high-income countries. Some recent models were developed from country-specific data, particularly for Asian countries, or from clinical outcomes trials.
Modeling CKD in T2DM is an active research area, with a trend towards IPS models developed from non-Western data and single data sources, primarily recent outcomes trials of novel renoprotective treatments.
随着2型糖尿病（T2DM）慢性肾脏病（CKD）的新型治疗方法问世，应使用CKD进展模型评估其长期益处。现有模型提供了不同的可重复使用的建模方法，但建模者评估众多可用模型之间的异同可能具有挑战性。此外，为模型参数提供信息的数据和基础人群特征可能并不总是显而易见的。因此，本研究回顾并总结了T2DM中CKD的现有建模方法和数据来源，作为未来模型开发的参考。
本系统文献综述纳入了T2DM人群中CKD的计算机模拟模型。检索在截至2021年10月的PubMed（包括MEDLINE）、Embase和Cochrane图书馆中进行。模型分为队列状态转换模型（cSTM）或个体患者模拟（IPS）模型。提取了关于模拟的肾脏疾病状态、CKD风险方程、数据来源以及主要数据源中推导队列的基线特征的信息。
该综述确定了49个模型（21个IPS，28个cSTM）。五状态结构在状态转换模型中是标准的，包括一个无肾脏疾病状态、三个肾脏疾病状态[通常包括蛋白尿和终末期肾病（ESKD）]以及一个死亡状态。五个模型捕捉了CKD的逆转，三个模型纳入了心血管疾病（CVD）。风险方程最常预测蛋白尿和ESKD的发生率，而最常预测的CKD后遗症是死亡率和CVD。大多数数据来源是成熟的登记处、队列研究和临床试验，这些研究通常在几十年前于高收入国家的主要白人人群中启动。一些近期模型是根据特定国家的数据开发的，特别是针对亚洲国家的数据，或者是根据临床结局试验开发的。
T2DM中CKD的建模是一个活跃的研究领域，呈现出从非西方数据和单一数据源（主要是新型肾脏保护治疗的近期结局试验）开发IPS模型的趋势。
