ASN's Mission

To create a world without kidney diseases, the ASN Alliance for Kidney Health elevates care by educating and informing, driving breakthroughs and innovation, and advocating for policies that create transformative changes in kidney medicine throughout the world.

learn more

Contact ASN

1401 H St, NW, Ste 900, Washington, DC 20005

email@asn-online.org

202-640-4660

The Latest on X

Kidney Week

ASN / Education & Meetings / Kidney Week /

Please note that you are viewing an archived section from 2020 and some content may be unavailable. To unlock all content for 2020, please visit the archives.

Abstract: PO0965

A Simulation Model for CKD Progression Among Patients with Type 2 Diabetes in the United States

Session Information

Category: Diabetic Kidney Disease

  • 602 Diabetic Kidney Disease: Clinical

Authors

  • Ye, Wen, Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, United States
  • Kuo, Shihchen, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, United States
  • Farej, Ryan, Bayer U.S. LLC, Whippany, New Jersey, United States
  • Singh, Rakesh, Bayer U.S. LLC, Whippany, New Jersey, United States
  • Ding, Xuemei, Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, United States
  • Putnam, Nathaniel, Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, United States
  • Kong, Sheldon, Bayer U.S. LLC, Whippany, New Jersey, United States
  • Elliott, Jay, Bayer U.S. LLC, Whippany, New Jersey, United States
  • Lott, Jason, Bayer U.S. LLC, Whippany, New Jersey, United States
  • Herman, William H., Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, United States
Background

Patients with type 2 diabetes (T2D) and chronic kidney disease (CKD) are at a higher risk of end-stage renal disease (ESRD), cardiovascular diseases and mortality. Modeling CKD progression in patients with T2D can help guide disease management for reducing clinical and economic burdens of CKD.

Methods

We developed a discrete-state and discrete-time microsimulation model for predicting changes of underlying risk factors over time and the progression of kidney disease, coronary heart disease, and cerebrovascular disease among patients with T2D and CKD. Transition probabilities were modeled as patient-level characteristics and risk factors, current disease state, and treatment status, with model parameters derived from individual-level data and summary data in published literature. Changes in risk factors for ESRD (urine albumin to creatinine ratio [UACR], estimated glomerular filtration rate [eGFR]), and risk equations for ESRD, myocardial infarction (MI), congestive heart failure (CHF), stroke, and death without ESRD were developed using longitudinal data of a T2D subpopulation in the Chronic Renal Insufficiency Cohort (CRIC). This model underwent calibration and validation against the CRIC patients with T2D and CKD over a 7-year follow-up period.

Results

At baseline, 1,441 CRIC participants with T2D and CKD (mean age: 61.6 years) were available for model development and validation. Concordance between observed and predicted outcomes for the five risk equations ranged from 0.71 to 0.90. The simulated event rates of ESRD, CHF, MI and stroke using estimated changes in key risk factors, and the related 95% confidence intervals covered the observed event rates in CRIC.

Conclusion

The model provided reliable estimates of disease progression among T2D patients with CKD. Modeling disease progression in this population will allow assessment of the impact of early detection and interventions, which may alter the economic and quality of life burden of CKD.

Funding

  • Commercial Support –