Defining measures of kidney function in observational studies using routine health care data: methodological and reporting considerations

Juan Jesus Carrero; Edouard L Fu; Søren V Vestergaard; Simon Kok Jensen; Alessandro Gasparini; Viyaasan Mahalingasivam; Samira Bell; Henrik Birn; Uffe Heide-Jørgensen; Catherine M Clase; Faye Cleary; Josef Coresh; Friedo W Dekker; Ron T Gansevoort; Brenda R Hemmelgarn; Kitty J Jager; Tazeen H Jafar; Csaba P Kovesdy; Manish M Sood; Bénédicte Stengel; Christian F Christiansen; Masao Iwagami; Dorothea Nitsch

doi:10.1016/j.kint.2022.09.020

Defining measures of kidney function in observational studies using routine health care data: methodological and reporting considerations

Kidney Int. 2023 Jan;103(1):53-69. doi: 10.1016/j.kint.2022.09.020. Epub 2022 Oct 22.

Authors

Juan Jesus Carrero¹, Edouard L Fu², Søren V Vestergaard³, Simon Kok Jensen³, Alessandro Gasparini⁴, Viyaasan Mahalingasivam⁵, Samira Bell⁶, Henrik Birn⁷, Uffe Heide-Jørgensen³, Catherine M Clase⁸, Faye Cleary⁵, Josef Coresh⁹, Friedo W Dekker¹⁰, Ron T Gansevoort¹¹, Brenda R Hemmelgarn¹², Kitty J Jager¹³, Tazeen H Jafar¹⁴, Csaba P Kovesdy¹⁵, Manish M Sood¹⁶, Bénédicte Stengel¹⁷, Christian F Christiansen³, Masao Iwagami¹⁸, Dorothea Nitsch¹⁹

Affiliations

¹ Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden. Electronic address: juan.jesus.carrero@ki.se.
² Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden; Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA; Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands.
³ Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark; Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.
⁴ Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden.
⁵ Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.
⁶ Division of Population Health and Genomics, University of Dundee, Dundee, UK.
⁷ Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Department of Biomedicine, Aarhus University, Aarhus, Denmark; Department of Renal Medicine, Aarhus University Hospital, Aarhus, Denmark.
⁸ Department of Medicine, McMaster University, Hamilton, Ontario, Canada; Department of Health Research and Methodology, McMaster University, Hamilton, Ontario, Canada.
⁹ Department of Epidemiology, Johns Hopkins University, Baltimore, Maryland, USA.
¹⁰ Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands.
¹¹ Department of Nephrology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.
¹² Department of Medicine, University of Alberta, Edmonton, Alberta, Canada.
¹³ ERA Registry, Amsterdam UMC location University of Amsterdam, Medical Informatics, Meibergdreef, Amsterdam, Netherlands; Amsterdam Public Health Research Institute, Quality of Care, Amsterdam, the Netherlands.
¹⁴ Program in Health Services and Systems Research, Duke-NUS Medical School, Singapore.
¹⁵ Division of Nephrology, Department of Medicine, University of Tennessee Health Science Center, Memphis, Tennessee, USA.
¹⁶ Department of Medicine, the Ottawa Hospital Research Institute, University of Ottawa, Ottawa, Ontario, Canada.
¹⁷ CESP (Center for Research in Epidemiology and Population Health), Clinical Epidemiology Team, University Paris-Saclay, University Versailles-Saint Quentin, Inserm U1018, Villejuif, France.
¹⁸ Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK; Department of Health Services Research, University of Tsukuba, Ibaraki, Japan.
¹⁹ Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK; Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand; UK Renal Registry, UK Kidney Association, Bristol, UK. Electronic address: Dorothea.Nitsch@lshtm.ac.uk.

PMID: 36280224
DOI: 10.1016/j.kint.2022.09.020

Abstract

The availability of electronic health records and access to a large number of routine measurements of serum creatinine and urinary albumin enhance the possibilities for epidemiologic research in kidney disease. However, the frequency of health care use and laboratory testing is determined by health status and indication, imposing certain challenges when identifying patients with kidney injury or disease, when using markers of kidney function as covariates, or when evaluating kidney outcomes. Depending on the specific research question, this may influence the interpretation, generalizability, and/or validity of study results. This review illustrates the heterogeneity of working definitions of kidney disease in the scientific literature and discusses advantages and limitations of the most commonly used approaches using 3 examples. We summarize ways to identify and overcome possible biases and conclude by proposing a framework for reporting definitions of exposures and outcomes in studies of kidney disease using routinely collected health care data.

Keywords: albuminuria; chronic kidney disease (CKD); creatinine; epidemiology; estimated glomerular filtration rate (eGFR); routinely collected health care data.

Publication types

Review
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Albuminuria / diagnosis
Creatinine
Glomerular Filtration Rate
Humans
Kidney
Kidney Diseases* / diagnosis
Kidney Diseases* / epidemiology
Kidney Diseases* / therapy
Kidney Function Tests
Renal Insufficiency, Chronic* / diagnosis
Renal Insufficiency, Chronic* / therapy

Substances

Creatinine

Abstract

Publication types

MeSH terms

Substances

Grants and funding