Success! The link has been copied to your clipboard.

Personalized Nephrology

Sample image
Sample image

The proposed individual-centric Information Commons (right panel) is somewhat analogous to a layered Geographical Information System (left panel). In both cases, the bottom layer defines the organization of all the overlays; however, in a GIS, any vertical line through the layers connects related snippets of information since all the layers are organized by geographical position.  In contract, data in each of the higher layers of the Information Commons will overlay on the patient layer in complex ways (e.g. patients with similar microbiomes and symptoms may have very different genome sequences).
Source: FPA 2011 (left panel)

Having an integrated technology solution is not enough. Robust tools will be needed to assist clinicians in identifying specific and actionable steps to take based on the exact background of the patient, with his or her own genetic variation and outcomes predicted by data analysis.

Fresenius Medical Care is utilizing specific components of personalized nephrology. As an organization, we have 12 predictive models that provide personalized results to identify patients at a higher risk of hospital admissions, higher likelihood of missing a treatment, higher risk of mortality, or higher risk of progressing to ESRD. We also have multiple personalized algorithms that compute ESA doses per treatment or combine numerous variables into a functional status index.

Many of these models have proven to be successful in pilot and clinical settings. For example, the Dialysis Hospitalization Reduction (DHR) pilot was associated with a 10 percentage point reduction in hospital days over a 12-month period in participating clinics compared to non-DHR clinics in the organization.

Chronic disease management provides a good opportunity for implementing personalized holistic patient care. Typically, chronically ill patients will have multiple providers, and no one care provider has the panoramic view of the entire problem. We only "touch" some of the aspects of the disease, like the "blind men touching various parts of the elephant trying to identify what it really is."14

Personalized nephrology care and treatment provides an interesting example of transitioning from highly individualized treatment in 1962 to improvement in outcomes in recent years from standardization of care. In the near future, Fresenius Medical Care will have the opportunity to deliver personalized, precise care delivered to the right patient at the right time in the context of an organized, standardize system. This opportunity for precision medicine delivery in nephrology is made possible by general HIT advancements that promote continuous monitoring and patient engagement; sophisticated real-time data management that customizes care delivery; and advanced analytics on large data sets that support personalized treatment decisions.

Meet the Author

Vice President of Integrated Care Analytics, Fresenius Medical Care North America

Dr. Len Usvyat chairs Fresenius Medical Care North America's Predictive Analytics Steering Committee and works closely with the MONitoring Dialysis Outcomes (MONDO) initiative, an international consortium of dialysis providers. His team provides analytical support for the company's pharmacy, vascular care centers, urgent care facilities, hospitalist group, health plan and Medical Office research. He graduated with his PhD from the University of Maastricht in the Netherlands.


  1. Alexander S. They decide who lives, who dies: medical miracle and a moral burden of a small committee. LIFE, November 9, 1962.
  2. United States Renal Data System, USRDS annual data report 2016. adr.aspx.
  3. Corbett E. Standardized care vs. personalization: can they coexist? Health Catalyst. 2016. Accessed March 30, 2017.
  4. Peer Kidney Care Initiative. 2016 report: new advances in outcomes, death rates, infections, readmissions. Accessed March 30, 2017.
  5. Gulshan V, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316(22):2402-2410.
  6. All about the Human Genome Project. National Human Genome Research Institute.–human-genome-project-hgp/.
  7. NIH Genetics Home Reference. What is precision medicine? primer/precisionmedicine/definition. Updated March 21, 2017.
  8. Antman EM, Loscalzo J. Precision medicine in cardiology. Nat Rev Cardiol. 2016;13(10):591-602.
  9. Wyatt CM, Schlondorff D. Precision medicine comes of age in nephrology: identification of novel biomarkers and therapeutic targets for chronic kidney disease. Kidney Int. 2016;89(4):734-7.
  10. Ibid.
  11. Relling MV, Evans WE. Pharmacogenomics in the clinic. Nature. 2015;526(7573):343-50.
  12. Rasmussen-Torvik LJ, et al. Design and anticipated outcomes of the eMERGE-PGx project: a multicenter pilot for preemptive pharmacogenomics in electronic health record systems. Clin Pharmacol Ther. 2014: 96(4):482-9.
  13. Collins FS, Varmus H. A new initiative on precision medicine. N Engl J Med. 2015:372(9):793-5.
  14. Sagner M, et al. The P4 health spectrum—a predictive, preventive, personalized and participatory continuum for promoting healthspan. Prog Cardiovasc Dis. 2016:doi:10.1016/j.pcad.2016.08.002.