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• W4220654681 abstract "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.The clinical effects of new treatments and their costs are often evaluated over a longer time frame than is possible in clinical trials by using computer simulation models. As new treatments are becoming available to treat chronic kidney disease, including in patients with type 2 diabetes, chronic kidney disease models may be used to inform clinical and economic decisions regarding these new treatment options. In the present study, we identified 49 published simulation models of chronic kidney disease used in populations with type 2 diabetes, and reviewed their structures and the data sources they used. The models focused mostly on disease states and outcomes associated with albuminuria (a condition in which the protein albumin is found in the urine) and end-stage kidney disease. Model structures with five disease states, including a kidney disease-free state, three kidney disease states, and death, were the most common. Relatively few models used glomerular filtration rates (a common measure of kidney function) or captured the possibility of an improvement in chronic kidney disease. Important data sources for many models were patient registries, cohort studies, and clinical trials, most conducted several decades ago in high-income countries with a high proportion of White participants. Several models developed in the past 5 years, particularly for Asian countries, instead relied largely or exclusively on country-specific data. In parallel, several individual patient simulations were recently developed from large outcomes trials for new treatments, including from trial subgroups covering specific geographical settings or ethnicities, shortly after trial publication." @default.
• W4220654681 created "2022-04-03" @default.
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• W4220654681 date "2022-03-15" @default.
• W4220654681 modified "2023-10-09" @default.
• W4220654681 title "Modeling Chronic Kidney Disease in Type 2 Diabetes Mellitus: A Systematic Literature Review of Models, Data Sources, and Derivation Cohorts" @default.
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