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Modeling of Disease Progression of Type 2 Diabetes Using Real-World Data: Quantifying Competing Risks of Morbidity and Mortality
Type 2 diabetes (T2D) is a progressive metabolic disorder that could be an underlying cause of long-term complications that increase mortality. The assessment of the probability of such events could be essential for mortality risk management. This work aimed to establish a framework for risk predictions of macrovascular complications (MVC) and diabetic kidney disease (DKD) in patients with T2D, using real-world data from the Swedish National Diabetes Registry (NDR), in the presence of mortality as a competing risk. The study consisted of 41,517 patients with T2D registered in NDR between 2005 and 2013. At inclusion, patients were newly diagnosed (T2D < 1 year) and had no prior evidence of DKD or MVC. Using three-quarters of the data, a five-state multistate model was established to describe competing events of MVC, DKD, a combination thereof, and the terminal state, death. Two hypotheses were investigated: (1) the risk of MVC and DKD are mutually independent, and (2) mortalit y is independent of morbidities. At the end of the study, the majority of individuals remained in uncomplicated T2D; however, the probability of transition to complications and death increased over time. The mortality hazard depended on the presence of morbidities and was quantified as a life expectancy decreased by 5.0, 9.7, and 12.2 years for MVC, DKD, and the combined morbidity, respectively, compared to uncomplicated T2D. An established framework with a five-state model incorporating competing events was shown to be a useful tool for comorbidities risk assessment in newly diagnosed patients with T2D. © 2025 The Author(s). CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.
Authors : Kunina H.; Franzén S.; Kjellsson M.C.
Source : American Society for Clinical Pharmacology and Therapeutics
Article Information
| Year | 2025 |
| Type | Article |
| DOI | 10.1002/psp4.13301 |
| ISSN | 21638306 |
| Volume |
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