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 2022 and some content may be unavailable. To unlock all content for 2022, please visit the archives.

Abstract: TH-PO867

New Surrogate Marker of CKD Progression and Mortality in Medical Word Virtual Space: Prospective Cohort Study

Session Information

Category: CKD (Non-Dialysis)

  • 2201 CKD (Non-Dialysis): Epidemiology‚ Risk Factors‚ and Prevention

Authors

  • Kanda, Eiichiro, Kawasaki Ika Daigaku, Kurashiki, Okayama, Japan
  • Epureanu, Bogdan I., University of Michigan, Ann Arbor, Michigan, United States
  • Adachi, Taiji, Kyoto Daigaku, Kyoto, Japan
  • Kashihara, Naoki, Kawasaki Ika Daigaku, Kurashiki, Okayama, Japan
Background

Chronic kidney disease (CKD) leads to end-stage renal disease (ESRD) or death. A new surrogate marker reflecting its pathophysiology has been needed for CKD therapy.

Methods

In this study, we developed a virtual space unifying data in the medical literature and that of actual CKD patients and created a surrogate marker of CKD progression and mortality using natural language processing.

Results

A virtual space of medical words was constructed from the CKD-related literature (n=165,271) using natural language processing, in which CKD-related words (n=106,612) composed a network (Figure 1). The data of CKD patients of a prospective cohort study for three years (n=26,433) were transformed into the space and linked with the network on the basis of information-geometry theory. We let the relationship between a patient and the outcome (ESRD or death) in the network be a surrogate marker of the outcome. The network satisfied the definitions of vector keeping their medical meanings. Riemannian metrics highly accurately predicted the primary outcomes; C-statistics, 0.911. Cox proportional hazards models with spline showed that the high Riemannian metrics were associated with high hazard ratio of the primary outcomes (p<0.0001). Moreover, the risk of the primary outcome in high-Riemannian-metric group was 21.92 (95% CI: 14.77, 32.51) times higher than that in the low-Riemannian-metric group.

Conclusion

The medical-word virtual space reflects the real-world patient data. And the Riemannian metrics between a patient and the outcome is a new surrogate marker for CKD therapy.