| Title: | Bias-corrected serum creatinine from UK Biobank electronic medical records generates an important data resource for kidney function trajectories |
| Journal: | Scientific Reports |
| Published: | 28 Jan 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/39875408/ |
| DOI: | https://doi.org/10.1038/s41598-025-85391-7 |
Publication 14959
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Abstract
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.</p>
15 Keywords
- Adult
- Aged
- Bias
- Biological Specimen Banks
- Creatinine
- Electronic Health Records
- Female
- Glomerular Filtration Rate
- Humans
- Kidney
- Kidney Function Tests
- Male
- Middle Aged
- UK Biobank
- United Kingdom
8 Authors
- Mathias Gorski
- Simon Wiegrebe
- Ralph Burkhardt
- Merle Behr
- Helmut Küchenhoff
- Klaus J. Stark
- Carsten A. Böger
- Iris M. Heid
1 Application
| Application ID | Title |
|---|---|
| 20272 | GWAS of kidney function related traits and kidney disease |
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