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Original ArticlesChronic Kidney Disease
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Identification of Patients with CKD in Medical Databases

A Comparison of Different Algorithms

Søren Viborg Vestergaard, Christian Fynbo Christiansen, Reimar Wernich Thomsen, Henrik Birn and Uffe Heide-Jørgensen
CJASN April 2021, 16 (4) 543-551; DOI: https://doi.org/10.2215/CJN.15691020
Søren Viborg Vestergaard
1Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark
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Christian Fynbo Christiansen
1Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark
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Reimar Wernich Thomsen
1Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark
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Henrik Birn
2Department of Renal Medicine, Aarhus University Hospital, Aarhus, Denmark
3Department of Biomedicine, Aarhus University, Aarhus, Denmark
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Uffe Heide-Jørgensen
1Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark
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Abstract

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

  • chronic kidney disease
  • incidence
  • prevalence
  • prognosis
  • glomerular filtration rate
  • registries
  • mortality
  • epidemiology
  • hospital records
  • algorithms
  • Received October 1, 2020.
  • Accepted January 18, 2021.
  • Copyright © 2021 by the American Society of Nephrology
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Clinical Journal of the American Society of Nephrology: 16 (4)
Clinical Journal of the American Society of Nephrology
Vol. 16, Issue 4
April 07, 2021
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Identification of Patients with CKD in Medical Databases
Søren Viborg Vestergaard, Christian Fynbo Christiansen, Reimar Wernich Thomsen, Henrik Birn, Uffe Heide-Jørgensen
CJASN Apr 2021, 16 (4) 543-551; DOI: 10.2215/CJN.15691020

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Identification of Patients with CKD in Medical Databases
Søren Viborg Vestergaard, Christian Fynbo Christiansen, Reimar Wernich Thomsen, Henrik Birn, Uffe Heide-Jørgensen
CJASN Apr 2021, 16 (4) 543-551; DOI: 10.2215/CJN.15691020
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