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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.
Background And Objectives:
Despite CKD consensus definitions, epidemiologic studies use multiple different algorithms to identify CKD. We aimed to elucidate if this affects the patient characteristics and the estimated prevalence and prognosis of CKD by applying six different algorithms to identify CKD in population-based medical databases and compare the cohorts.
Design, Setting, Participants, & Measurements:
Patients with CKD in Northern Denmark (2009-2016) were identified using six different algorithms: five were laboratory based defined by (1) one measured outpatient eGFR <60 ml/min per 1.73 m2 (single test, n=103,435), (2) two such findings ≥90 days apart (Kidney Disease Improving Global Outcomes, n=84,688), (3) two such findings ≥90 days apart with no eGFR >60 ml/min per 1.73 m2 observed in-between (Kidney Disease Improving Global Outcomes, persistent, n=68,994), (4) two such findings ≥90 and <365 days apart (Kidney Disease Improving Global Outcomes, time limited, n=75,031), and (5) two eGFRs <60 ml/min per 1.73 m2 or two urine albumin-creatinine ratios >30 mg/g ≥90 days apart (Kidney Disease Improving Global Outcomes, eGFR/albuminuria, n=100,957). The sixth included patients identified by reported in- and outpatient hospital International Classification of Diseases diagnoses of CKD (hospital-diagnosed, n=27,947). For each cohort, we estimated baseline eGFR, CKD prevalence, and 1-year mortality using the Kaplan-Meier method.
Results:
The five different laboratory-based algorithms resulted in large differences in the estimated prevalence of CKD from 4637-8327 per 100,000 population. In contrast, 1-year mortality varied only slightly (7%-9%). Baseline eGFR levels at diagnosis were comparable (53-56 ml/min per 1.73 m2), whereas median time since first recorded eGFR <60 ml/min per 1.73 m2 varied from 0 months (single-test) to 17 months (Kidney Disease Improving Global Outcomes, persistent). The hospital-diagnosed algorithm yielded markedly lower CKD prevalence (775 per 100,000 population), a lower baseline eGFR (47 ml/min per 1.73 m2), longer time since first eGFR <60 ml/min per 1.73 m2 (median 70 months), and much higher 1-year mortality (22%).
Conclusions:
Population prevalence of CKD identified in medical databases greatly depends on the applied algorithm to define CKD. Despite these differences, laboratory-based algorithms produce cohorts with similar prognosis.
Podcast:
This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2021_03_11_CJN15691020_final.mp3.
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