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Abstract: PO0421

Clinical Characteristics and eGFR and Urine Albumin-to-Creatinine Ratio Distribution According to the 2012 KDIGO CKD Classification: A Report from the DISCOVER CKD International Retrospective Cohort

Session Information

Category: CKD (Non-Dialysis)

  • 2101 CKD (Non-Dialysis): Epidemiology, Risk Factors, and Prevention

Authors

  • Garcia Sanchez, Juan Jose, AstraZeneca, Cambridge, United Kingdom
  • Carrero, Juan Jesus, Karolinska Institutet, Stockholm, Stockholm, Sweden
  • Kumar, Supriya R., AstraZeneca, Gaithersburg, Maryland, United States
  • Pecoits-Filho, Roberto, Arbor Research Collaborative for Health, Ann Arbor, Michigan, United States
  • James, Glen, AstraZeneca, Cambridge, United Kingdom
  • L Heerspink, Hiddo Jan, Rijksuniversiteit Groningen, Groningen, Groningen, Netherlands
  • Nolan, Stephen, AstraZeneca, Cambridge, United Kingdom
  • Carolyn, Lam Su ping, National Heart Centre Singapore, Singapore, Singapore
  • Chen, Hungta (tony), AstraZeneca, Gaithersburg, Maryland, United States
  • Kanda, Eiichiro, Kawasaki Ika Daigaku, Kurashiki, Okayama, Japan
  • Abdul Sultan, Alyshah, AstraZeneca, Cambridge, United Kingdom
  • Kashihara, Naoki, Kawasaki Ika Daigaku, Kurashiki, Okayama, Japan
  • Kosiborod, Mikhail, Saint Luke's Mid America Heart Institute, Kansas City, Missouri, United States
  • Pollock, Carol A., Royal North Shore Hospital, St Leonards, New South Wales, Australia
  • Wheeler, David C., University College London, London, London, United Kingdom
Background

Contemporary studies describing the prevalence and characteristics of patients with CKD categorised according KDIGO 2012 are scarce. We describe patient characteristics and the prevalence of CKD according to the 2012 KDIGO categories in patients with CKD.

Methods

The DISCOVER-CKD retrospective cohort of patients was extracted using real-world data from the integrated Limited Claims and Electronic Health Record (LCED) data and TriNetX. Patients were aged ≥18 years, with ≥1 UACR measure and required first diagnostic coding of CKD (Stages3A to ESRD) or two estimated glomerular filtration rate (eGFR) measurements of <75 mL/min/1.73 m2 recorded at least 90 days apart (max 730) between January 2008 and March 2020. Index date was date of diagnostic coding or 2nd eGFR. UACR closest to index was used to categorise patients. Descriptive analyses were used to summarise prevalence and patient characteristics.

Results

Preliminarily, among 22229 included patients, 63.6% (n=10979, TriNetX) and 76.6% (n=3813, LCED) had normal to mildly increased albuminuria, 26.6% (n=4581, TriNetX) and 17.9% (n=889, LCED) had moderately increased albuminuria and 9.8% (n=1694, TriNetX) and 5.5% (n=273, LCED) had severely increased albuminuria (Figure 1). Hypertension and type 2 diabetes were the most common comorbidities (prevalence >60%) and their prevalence increased with albuminuria.

Conclusion

This study, utilising real-world data, fills an important knowledge gap regarding the characteristics of patients with CKD in different eGFR and UACR strata according to the KDIGO 2012 definitions.

Funding

  • Commercial Support –