The landscape of kidney disease is ever-changing. Globally, changes in lifestyle have led to a steady increase in obesity and diabetes, major drivers of chronic kidney disease (CKD). On the other hand, the last couple of decades have seen tremendous progress in the form of improved treatments for CKD such as new drugs to slow the progression and/or treat the underlying etiology of kidney disease such as SGLT2 inhibitors (SGLT2i)1,2,3 and GLP-1 receptor agonists (GLP1ra).4 In addition, innovative dialytic therapies, such as high-volume hemodiafiltration (HVHDF), have documented beneficial impact on clinical outcomes in people with end-stage kidney disease (ESKD) on dialysis.5 Advanced epidemiological-type models are an invaluable tool to assess the integrated impact that novel therapies and demographic changes in the population may have on the size and characteristics of future populations with kidney disease.
The size and demographic composition of the CKD population depend on the complex interplay of various factors. Every year, hundreds of thousands of people develop ESKD around the world, many of them receiving kidney replacement therapy, mostly hemodialysis. At the same time, both the prevention and treatment of kidney disease are steadily improving, facilitated by new drugs and technologies. Novel therapies such as HVHDF have proven survival benefits for people with ESKD on maintenance hemodialysis.5 GLP1ra—originally developed as a treatment for type 2 diabetes—gained much attention for their potential to reduce weight and delay kidney disease progression.4 These are but a few examples. While life expectancy shows a general trend towards longer life expectancy, sudden global events like the COVID-19 pandemic can have a significant impact on the population.6,7
In this complex situation, several questions arise:
Estimating the impact of these developments on populations with kidney disease is a challenging task, not least because different demographic groups, especially younger and older individuals, may be affected differently.
This is where transparent mathematical models capturing the epidemiology of kidney disease can provide quantitative insights and make a decisive difference. By capturing ongoing public health trends and combining them with the latest clinical insights on the effect and efficacy of novel therapeutics, such models can provide valuable insights into what populations with kidney disease will look like in the future.
Fresenius Medical Care’s Global Medical Office has developed a proprietary, science-based systematic modeling approach: the Population Impact Model. It provides a quantitative tool to test a spectrum of hypotheses about the future impact of novel therapeutic interventions and large-scale public health disruptions in the kidney space. The Population Impact Model is specifically designed to:
In a first step, the model specifically addresses developments in the United States, Fresenius Medical Care’s largest dialysis services market, predicting the development of the U.S. population with kidney disease over the next decade.
The Population Impact Model describes how the interplay of kidney disease incidence and progression, treatment, population aging, and mortality shape the size and age distribution of the CKD and ESKD populations over time (Figure 1). The mathematical principles underlying the model are the same as for widely established models of epidemiology, such as the ones used to predict COVID-19 incidence and prevalence during the pandemic.8,9
Any model prediction can only be as good as the data with which it is informed. Well-established publicly available databases like the United States Renal Data System (USRDS) and the National Health and Nutrition Examination Survey (NHANES) provide a wealth of data on the past and current state of populations with kidney disease in the United States.10,11 However, to inform a systematic modeling approach and provide a basis for future predictions, the trends encoded in these datasets must be quantified:
By applying advanced analytical methods, the data often reveal surprisingly robust temporal and aging patterns that encode systematic public health trends and shed light on the above questions for the past and present. These trends (and their disruptions) provide a robust foundation for predictions.
Once one understands current populations with kidney disease and the recent trends in kidney disease, then the crucial question is how these trends might be impacted by novel therapies. Clinical trials remain the primary source of knowledge about their safety and efficacy. They provide quantitative insights on how a therapeutic intervention changes the probability of kidney disease progression, death, and possibly other relevant clinical events for an individual. These insights can then be used to extrapolate an intervention’s impact on the population scale (Figure 2). This, of course, also depends on how many and which patients are anticipated to have access to such novel interventions. Here, a modeling approach allows us to test different hypotheses (e.g., different anticipated prescription rates for a new drug in the coming years) and quantify how they affect the population.
Advanced epidemiological-type models provide a systematic and transparent tool to assess the population impact of current and future therapeutic innovations and public health megatrends. In particular, they help to disentangle the impacts of several concomitant developments in the kidney space, including the market introduction of new antidiabetic drugs and new kidney replacement therapies, pandemics, and other disruptions. Fresenius Medical Care is at the forefront of population impact modeling to inform medical, clinical, and business decisions. Continuous monitoring of therapeutic developments allows for regular updates to model assumptions and access to the latest predictions.
Predicting Population Trends in Kidney Health Using Advanced Mathematical Modeling
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1. Vlado Perkovic et al., “Canagliflozin and Renal Outcomes in Type 2 Diabetes and Nephropathy,” New England Journal of Medicine 380, no. 24 (2019): 2295–2306.
2. Hiddo JL Heerspink et al., “Dapagliflozin in Patients with Chronic Kidney Disease,” New England Journal of Medicine 383, no. 15 (2020): 1436–46.
3. EMPA-Kidney Collaborative Group, “Empagliflozin in Patients with Chronic Kidney Disease,” New England Journal of Medicine 388, no. 2 (2023): 117–27.
4. Vlado Perkovic et al., “Effects of Semaglutide on Chronic Kidney Disease in Patients with Type 2 Diabetes,” New England Journal of Medicine 391, no. 2 (2024): 109–21.
5. Peter J. Blankestijn et al., “Effect of Hemodiafiltration or Hemodialysis on Mortality in Kidney Failure,” New England Journal of Medicine 389, no. 8 (2023): 700-709.
6. Duvuru Geetha et al., “Impact of the COVID-19 Pandemic on the Kidney Community: Lessons Learned and Future Directions,” Nature Reviews Nephrology 18 (2022): 724–37.
7. COVID-19 Excess Mortality Collaborators, “Estimating Excess Mortality Due to the COVID-19 Pandemic: A Systematic Analysis of COVID-19-Related Mortality, 2020-21,” The Lancet 399 (2022): 1513–36.
8. Benjamin F. Maier and Dirk Brockmann, “Effective Containment Explains Subexponential Growth in Recent Confirmed COVID-19 Cases in China,” Science 368, no. 6492 (2020): 742–46.
9. J. Chhatwal et al., “PIN68 COVID-19 Simulator: An Interactive Tool to Inform COVID-19 Intervention Policy Decisions in the United States,” Value in Health 23 (2020): S556.
10. US Renal Data System, 2023 USRDS Annual Data Report: Epidemiology of Kidney Disease in the United States (Bethesda, MD: National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, 2023), usrds-adr.niddk.nih.gov/2023.
11. Centers for Disease Control and Prevention (CDC), National Center for Health Statistics (NCHS), National Health and Nutrition Examination Survey Data (Hyattsville, MD: 1999-2020), wwwn.cdc.gov/nchs/nhanes/default.aspx.