Identification of Patients with CKD in Medical Databases: A Comparison of Different Algorithms

Søren Viborg Vestergaard1, Christian Fynbo Christiansen1, Reimar Wernich Thomsen1

  • 1Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark.

Insights

The algorithm used to identify chronic kidney disease (CKD) significantly impacts prevalence estimates. However, laboratory-based methods yield similar patient prognoses for CKD.

Area of Science:

  • Nephrology
  • Epidemiology
  • Biostatistics

Background:

  • Epidemiologic studies utilize diverse algorithms for identifying chronic kidney disease (CKD), despite consensus definitions.
  • This variability may influence patient characteristics, CKD prevalence, and prognosis estimates.

Purpose of the Study:

  • To compare patient characteristics, CKD prevalence, and prognosis across six distinct algorithms for identifying CKD in population-based medical databases.

Main Methods:

  • Six algorithms were applied to identify CKD in Northern Denmark (2009-2016): five laboratory-based (single test, KDIGO, KDIGO persistent, KDIGO time-limited, KDIGO eGFR/albuminuria) and one hospital-diagnosed.
  • Estimated prevalence, baseline eGFR, and 1-year mortality were compared for each cohort using Kaplan-Meier methods.

Main Results:

  • Laboratory-based algorithms yielded CKD prevalence estimates ranging from 4637-8327 per 100,000 population, with comparable 1-year mortality (7%-9%).
  • The hospital-diagnosed algorithm showed lower prevalence (775 per 100,000), lower baseline eGFR (47 ml/min/1.73 m²), and higher 1-year mortality (22%).
  • Baseline eGFRs were similar across lab-based cohorts (53-56 ml/min/1.73 m²), but time since diagnosis varied significantly.

Conclusions:

  • The choice of algorithm for defining CKD in medical databases substantially affects prevalence estimates.
  • Despite variations in prevalence, laboratory-based algorithms identify CKD cohorts with similar prognostic outcomes.
Abstract

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