Panel estimated Glomerular Filtration Rate (GFR): Statistical considerations for maximizing accuracy in diverse clinical populations. - Nora F Fino; Lesley A Inker; Tom Greene; Ogechi M Adingwupu; Josef Coresh; Jesse Seegmiller; Michael G Shlipak; Tazeen H Jafar; Roberto Kalil; Veronica T Costa E Silva; Vilmundur Gudnason; Andrew S Levey; et al. (PLoS ONE)
Assessing glomerular filtration rate (GFR) is critical for diagnosis, staging, and management of kidney disease. However, accuracy of estimated GFR (eGFR) is limited by large errors (>30% error present in >10-50% of patients), adversely impacting patient care. Errors often result from variation across populations of non-GFR determinants affecting the filtration markers used to estimate GFR. We hypothesized that combining multiple filtration markers with non-overlapping non-GFR determinants into a panel GFR could improve eGFR accuracy, extending current recognition that adding cystatin C to serum creatinine improves accuracy. Non-GFR determinants of markers can affect the accuracy of eGFR in two ways: first, increased variability in the non-GFR determinants of some filtration markers among application populations compared to the development population may result in outlying values for those markers. Second, systematic differences in the non-GFR determinants of some markers between application and development populations can lead to biased estimates in the application populations. Here, we propose and evaluate methods for estimating GFR based on multiple markers in applications with potentially higher rates of outlying predictors than in development data. We apply transfer learning to address systematic differences between application and development populations. We evaluated a panel of 8 markers (5 metabolites and 3 low molecular weight proteins) in 3,554 participants from 9 studies. Results show that contamination in two strongly predictive markers can increase imprecision by more than two-fold, but outlier identification with robust estimation can restore precision nearly fully to uncontaminated data. Furthermore, transfer learning can yield similar results with even modest training set sample size. Combining both approaches addresses both sources of error in GFR estimates. Once the laboratory challenge of developing a validated targeted assay for additional metabolites is overcome, these methods can inform the use of a panel eGFR across diverse clinical settings, ensuring accuracy despite differing non-GFR determinants.