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Gorski, Mathias ; Wiegrebe, Simon ; Burkhardt, Ralph ; Behr, Merle ; Küchenhoff, Helmut ; Stark, Klaus J. ; Böger, Carsten A. ; Heid, Iris M.

Bias-corrected serum creatinine from UK Biobank electronic medical records generates an important data resource for kidney function trajectories

Gorski, Mathias , Wiegrebe, Simon, Burkhardt, Ralph , Behr, Merle , Küchenhoff, Helmut, Stark, Klaus J., Böger, Carsten A. und Heid, Iris M. (2025) Bias-corrected serum creatinine from UK Biobank electronic medical records generates an important data resource for kidney function trajectories. Scientific Reports 15 (1).

Veröffentlichungsdatum dieses Volltextes: 30 Jan 2025 09:13
Artikel
DOI zum Zitieren dieses Dokuments: 10.5283/epub.74802


Zusammenfassung

Loss of kidney function is a substantial personal and public health burden. Kidney function is typically assessed as estimated glomerular filtration rate (eGFR) based on serum creatinine. UK Biobank provides serum creatinine measurements from study center assessments (SC, n = 425,147 baseline, n = 15,314 with follow-up) and emerging electronic Medical Records (eMR, “GP-clinical”) present a ...

Loss of kidney function is a substantial personal and public health burden. Kidney function is typically assessed as estimated glomerular filtration rate (eGFR) based on serum creatinine. UK Biobank provides serum creatinine measurements from study center assessments (SC, n = 425,147 baseline, n = 15,314 with follow-up) and emerging electronic Medical Records (eMR, “GP-clinical”) present a promising resource to augment this data longitudinally. However, it is unclear whether eMR-based and SC-based creatinine values can be used jointly for research on eGFR decline. When comparing eMR-based with SC-based creatinine by calendar year (n = 70,231), we found a year-specific multiplicative bias for eMR-based creatinine that decreased over time (factor 0.84 for 2007, 0.97 for 2013). Deriving eGFR based on SC- and bias-corrected eMR-creatinine yielded 454,907 individuals with ≥ 1eGFR assessment (2,102,174 assessments). This included 206,063 individuals with ≥ 2 assessments over up to 60.2 years (median 6.00 assessments, median time = 8.7 years), where we also obtained eMR-based information on kidney disease or renal replacement therapy. We found an annual eGFR decline of 0.11 (95%-CI = 0.10–0.12) versus 1.04 mL/min/1.73m2/year (95%-CI = 1.03–1.05) without and with bias-correction, the latter being in line with literature. In summary, our bias-corrected eMR-based creatinine values enabled a 4-fold increased number of eGFR assessments in UK Biobank suitable for kidney function research.



Beteiligte Einrichtungen


Details

DokumentenartArtikel
Titel eines Journals oder einer ZeitschriftScientific Reports
Verlag:Springer
Band:15
Nummer des Zeitschriftenheftes oder des Kapitels:1
Datum28 Januar 2025
InstitutionenMedizin > Abteilung für Nephrologie
Medizin > Lehrstuhl für Klinische Chemie und Laboratoriumsmedizin
Medizin > Institut für Epidemiologie und Präventivmedizin
Medizin > Institut für Epidemiologie und Präventivmedizin > Lehrstuhl für Genetische Epidemiologie
Informatik und Data Science > Fachbereich Maschinelles Lernen und Data Science > Chair of Machine Learning (Prof. Dr. Merle Behr)
Projekte
Gefördert von: Deutsche Forschungsgemeinschaft (DFG) (509149993)
Identifikationsnummer
WertTyp
10.1038/s41598-025-85391-7DOI
Stichwörter / KeywordsNephrology, Kidney, Medical Research, Epidemiology
Dewey-Dezimal-Klassifikation600 Technik, Medizin, angewandte Wissenschaften > 600 Technik
600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin
StatusVeröffentlicht
BegutachtetJa, diese Version wurde begutachtet
An der Universität Regensburg entstandenZum Teil
URN der UB Regensburgurn:nbn:de:bvb:355-epub-748029
Dokumenten-ID74802

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