CamGFR v2: A New Model for Estimating the Glomerular Filtration Rate from Standardized or Non-standardized Creatinine in Patients with Cancer

Abstract

Purpose: Management of patients with cancer, specifically carboplatin dosing, requires accurate knowledge of glomerular filtration rate (GFR). Direct measurement of GFR is resource limited. Available models for estimated GFR (eGFR) are optimized for patients without cancer and either isotope dilution mass spectrometry (IDMS)- or non-IDMS-standardized creatinine measurements. We present an eGFR model for patients with cancer compatible with both creatinine measurement methods.

Experimental design: GFR measurements, biometrics, and IDMS- or non-IDMS-standardized creatinine values were collected for adult patients from three cancer centers. Using statistical modeling, an IDMS and non-IDMS creatinine-compatible eGFR model (CamGFR v2) was developed. Its performance was compared with that of the existing models Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI), Modification of Diet in Renal Disease (MDRD), Full Age Spectrum (FAS), Lund-Malmö revised, and CamGFR v1, using statistics for bias, precision, accuracy, and clinical robustness.

Results: A total of 3,083 IDMS- and 4,612 non-IDMS-standardized creatinine measurements were obtained from 7,240 patients. IDMS-standardized creatinine values were lower than non-IDMS-standardized values in within-center comparisons (13.8% lower in Cambridge; P < 0.0001 and 19.3% lower in Manchester; P < 0.0001), and more consistent between centers. CamGFR v2 was the most accurate [root-mean-squared error for IDMS, 14.97 mL/minute (95% confidence interval, 13.84-16.13) and non-IDMS, 15.74 mL/minute (14.86-16.63)], most clinically robust [proportion with >20% error of calculated carboplatin dose for IDMS, 0.12 (0.09-0.14) and non-IDMS, 0.17 (0.15-0.2)], and least biased [median residual for IDMS, 0.73 mL/minute (-0.68 to 2.2) and non-IDMS, -0.43 mL/minute (-1.48 to 0.91)] eGFR model, particularly when eGFR was larger than 60 ml/minute.

Conclusions: CamGFR v2 can utilize IDMS- and non-IDMS-standardized creatinine measurements and outperforms previous models. CamGFR v2 should be examined prospectively as a practice-changing standard of care for eGFR-based carboplatin dosing.

Figure 1

Schematic representation of data acquisition, filtering, and study workflow. The partitioning into development and validation datasets was performed randomly. n = number of GFR measurements (samples), p = number of patients, n_IDMS = number of samples for which serum creatinine was measured with an IDMS-standardized method, n_Enzymatic = number of samples for which serum creatinine was measured with an enzymatic method. The 7,695 samples in the “Combined data filtered” box include 4,983 from Cambridge, 2,056 from Manchester, and 656 from Gothenburg. The filter of repeat measurements was the final filtering step prior to creation of the “Combined data filtered” dataset.

Figure 2

Comparison of creatinine measurement methods in patients with cancer. Serum creatinine (SCr) is shown on a logarithmic scale. (A,B) Violin plots for IDMS (A) and non-IDMS (B) creatinine measurements by center. The values above brackets across the violins correspond to the t-test p-values for the respective comparisons. The numbers in the violin plots correspond to the respective number of samples. The horizontal lines correspond to the median SCr in that group. (C,D) Timeline of SCr measurements from Cambridge (C) and Manchester (D). The SCr is log-transformed and the vertical line corresponds to the date when the creatinine measurement methodology changed from non-IDMS-standardized to IDMS-standardized. The density plots on the right side of the panels show the log(SCr) distributions by color-coded by methodology. The p-values were computed by t-test. Smoothed lines were calculated using a generalized additive model with a cubic spline basis. The gap in SCr data around the start of 2018 in (C) was due to changes in the hospital database at that time.

Figure 3

Summary statistics comparing the CamGFR v1, CamGFR v2, CKD-EPI, LM, and FAS models, as well as the MDRD study equation. Statistics were calculated separately for patients with IDMS-standardized creatinine and patients with non-IDMS-standardized creatinine. The residual (measured GFR - estimated GFR) median (first row), residual interquartile range (second row), root-mean-squared error (third row), and the clinical robustness, approximated as the proportion of patients who have an percentage error of more than 20% of calculated carboplatin dose (Dose P20) (fourth row), are displayed. All error bars are 95% confidence intervals calculated using bootstrap resampling with 2,000 repetitions and a normal distribution approximation. The CG model performed less well than any other model and has not been included because it was developed for non-IDMS-standardized creatinine. Source data for this figure and results of testing for statistical significance by permutation tests[26] are presented in Tables S9, 16.

Figure 4

Residual (measured GFR - estimated GFR) median, dose p20 and RMSE for the CamGFR v1, CamGFR v2, CKD-EPI, LM and FAS models, as well as the MDRD study equation, as stratified by eGFR. Source data is presented in Table S14.