Modeling Chronic Kidney Disease in Type 2 Diabetes Mellitus: A Systematic Literature Review of Models, Data Sources, and Derivation Cohorts.

IF 2.8 3区 医学 Q3 ENDOCRINOLOGY & METABOLISM Diabetes Therapy Pub Date : 2022-04-01 Epub Date: 2022-03-15 DOI:10.1007/s13300-022-01208-0
Johannes Pöhlmann, Klas Bergenheim, Juan-Jose Garcia Sanchez, Naveen Rao, Andrew Briggs, Richard F Pollock
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Abstract

Introduction: 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.

Methods: 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.

Results: 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.

Conclusion: 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.

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2型糖尿病慢性肾脏疾病建模:对模型、数据来源和衍生队列的系统文献综述
简介:随着针对 2 型糖尿病(T2DM)慢性肾脏病(CKD)的新型疗法的出现,应该使用 CKD 进展模型来评估这些疗法的长期益处。现有模型提供了可重复使用的不同建模方法,但对于建模者来说,评估众多可用模型之间的共性和差异可能具有挑战性。此外,为模型参数提供信息的数据和基本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:本系统性文献综述包括 T2DM 群体中 CKD 的计算机模拟模型。截至 2021 年 10 月,在 PubMed(包括 MEDLINE)、Embase 和 Cochrane 图书馆进行了检索。模型分为群组状态转换模型(cSTM)和个体患者模拟模型(IPS)。在主要数据源中提取了有关模型肾脏疾病状态、CKD 风险方程、数据来源和衍生队列基线特征的信息:研究发现了 49 个模型(21 个 IPS,28 个 cSTM)。五状态结构是状态转换模型的标准结构,包括一个无肾病状态、三个肾病状态(通常包括白蛋白尿和终末期肾病(ESKD))和一个死亡状态。五个模型捕捉到了 CKD 回归,三个模型包括心血管疾病 (CVD)。风险方程最常预测的是白蛋白尿和终末期肾病的发病率,而最常预测的 CKD 后遗症是死亡率和心血管疾病。大多数数据来源都是几十年前在高收入国家以白人为主的人群中开展的成熟的登记、队列研究和临床试验。最近的一些模型是根据特定国家(尤其是亚洲国家)的数据或临床结果试验开发的:T2DM中的CKD建模是一个活跃的研究领域,其趋势是利用非西方数据和单一数据源开发IPS模型,主要是近期新型肾保护治疗的结果试验。
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来源期刊
Diabetes Therapy
Diabetes Therapy Medicine-Endocrinology, Diabetes and Metabolism
自引率
7.90%
发文量
130
期刊介绍: Diabetes Therapy is an international, peer reviewed, rapid-publication (peer review in 2 weeks, published 3–4 weeks from acceptance) journal dedicated to the publication of high-quality clinical (all phases), observational, real-world, and health outcomes research around the discovery, development, and use of therapeutics and interventions (including devices) across all areas of diabetes. Studies relating to diagnostics and diagnosis, pharmacoeconomics, public health, epidemiology, quality of life, and patient care, management, and education are also encouraged. The journal is of interest to a broad audience of healthcare professionals and publishes original research, reviews, communications and letters. The journal is read by a global audience and receives submissions from all over the world. Diabetes Therapy will consider all scientifically sound research be it positive, confirmatory or negative data. Submissions are welcomed whether they relate to an international and/or a country-specific audience, something that is crucially important when researchers are trying to target more specific patient populations. This inclusive approach allows the journal to assist in the dissemination of all scientifically and ethically sound research.
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