The Search for More Reliable Estimated GFR Biomarkers

Performance of Panel-Estimated GFR Among Hospitalized Older Adults

RATIONALE & OBJECTIVE

Older adults represent nearly half of all hospitalized patients and are vulnerable to inappropriate dosing of medications eliminated through the kidneys. However, few studies in this population have evaluated the performance of equations for estimating the glomerular filtration rate (GFR)-particularly those that incorporate multiple filtration markers.

STUDY DESIGN

Cross-sectional diagnostic test substudy of a randomized clinical trial.

SETTING & PARTICIPANTS

Adults≥65 years of age presenting to the emergency department of Copenhagen University Hospital Amager and Hvidovre in Hvidovre, Denmark, between October 2018 and April 2021.

TESTS COMPARED

Measured GFR (mGFR) determined using 99mTc-DTPA plasma clearance compared with estimated GFR (eGFR) calculated using 6 different equations based on creatinine; 3 based on creatinine and cystatin C combined; and 2 based on panels of markers including creatinine, cystatin C, β-trace protein (BTP) and/or β2-microglobulin (B2M).

OUTCOME

The performance of each eGFR equation compared with mGFR with respect to bias, relative bias, inaccuracy (1-P30), and root mean squared error (RMSE).

RESULTS

We assessed eGFR performance for 106 patients (58% female, median age 78.3 years, median mGFR 62.9mL/min/1.73m2). Among the creatinine-based equations, the 2009 CKD-EPIcr equation yielded the smallest relative bias (+4.2%). Among the creatinine-cystatin C combination equations, the 2021 CKD-EPIcomb equation yielded the smallest relative bias (-3.4%), inaccuracy (3.8%), and RMSE (0.139). Compared with the 2021 CKD-EPIcomb, the CKD-EPIpanel equation yielded a smaller RMSE (0.136) but larger relative bias (-4.0%) and inaccuracy (5.7%).

LIMITATIONS

Only White patients were included; only a subset of patients from the original clinical trial underwent GFR measurement; and filtration marker concentration can be affected by subclinical changes in volume status.

CONCLUSIONS

The 2009 CKD-EPIcr, 2021 CKD-EPIcomb, and CKD-EPIpanel equations performed best and notably outperformed their respective full-age spectrum equations. The addition of cystatin C to creatinine-based equations improved performance, while the addition of BTP and/or B2M yielded minimal improvement.

FUNDING

Grants from public sector industry (Amgros I/S) and government (Capital Region of Denmark).

TRIAL REGISTRATION

Registered at ClinicalTrials.gov with registration number NCT03741283.

PLAIN-LANGUAGE SUMMARY

Inaccurate kidney function assessment can lead to medication errors, a common cause of hospitalization and early readmission among older adults. Several novel methods have been developed to estimate kidney function based on a panel of kidney function markers that can be measured from a single blood sample. We evaluated the accuracy of these new methods (relative to a gold standard method) among 106 hospitalized older adults. We found that kidney function estimates combining 2 markers (creatinine and cystatin C) were highly accurate and noticeably more accurate than estimates based on creatinine alone. Estimates incorporating additional markers such as β-trace protein and β2-microglobulin did not further improve accuracy.

Improving glomerular filtration rate estimation by semi-supervised learning: a development and external validation study

BACKGROUND

Accurate estimating glomerular filtration rate (GFR) is crucial both in clinical practice and epidemiological survey. We incorporated semi-supervised learning technology to improve GFR estimation performance.

METHODS

AASK [African American Study of Kidney Disease and Hypertension], CRIC [Chronic Renal Insufficiency Cohort] and DCCT [Diabetes Control and Complications Trial] studies were pooled together for model development, whereas MDRD [Modification of Diet in Renal Disease] and CRISP [Consortium for Radiological Imaging Studies of Polycystic Kidney Disease] studies for model external validation. A total of seven variables (Serum creatinine, Age, Sex, Black race, Diabetes status, Hypertension and Body Mass Index) were included as independent variables, while the outcome variable GFR was measured as the urinary clearance of ¹²⁵I-iothalamate. The revised CKD-EPI [Chronic Kidney Disease Epidemiology Collaboration] creatinine equations was selected as benchmark for performance comparisons. Head-to-head performance comparisons from four-variable to seven-variable combination were conducted between revised CKD-EPI equations and semi-supervised models.

RESULTS

In each independent variables combination, the semi-supervised models consistently achieved superior results in all three performance indicators compared with corresponding revised CKD-EPI equations in the external validation data set. Furthermore, compared with revised four-variable CKD-EPI equation, the seven-variable semi-supervised model performed less biased (mean of difference: 0.03 [- 0.28, 0.34] vs 1.53 [1.28, 1.85], P < 0.001), more precise (interquartile range of difference: 7.94 [7.37, 8.50] vs 8.28 [7.76, 8.83], P = 0.1) and accurate (P30: 88.9% [87.4%, 90.2%] vs 86.0% [84.4%, 87.4%], P < 0.001.

CONCLUSIONS

The superior performance of the semi-supervised models during head-to-head comparisons supported the hypothesis that semi-supervised learning technology could improve GFR estimation performance.

Improving accuracy of estimating glomerular filtration rate using artificial neural network: model development and validation

BACKGROUND

The performance of previously published glomerular filtration rate (GFR) estimation equations degrades when directly used in Chinese population. We incorporated more independent variables and using complicated non-linear modeling technology (artificial neural network, ANN) to develop a more accurate GFR estimation model for Chinese population.

METHODS

The enrolled participants came from the Third Affiliated Hospital of Sun Yat-sen University, China from Jan 2012 to Jun 2016. Participants with age < 18, unstable kidney function, taking trimethoprim or cimetidine, or receiving dialysis were excluded. Among the finally enrolled 1952 participants, 1075 participants (55.07%) from Jan 2012 to Dec 2014 were assigned as the development data whereas 877 participants (44.93%) from Jan 2015 to Jun 2016 as the internal validation data. We in total developed 3 GFR estimation models: a 4-variable revised CKD-EPI (chronic kidney disease epidemiology collaboration) equation (standardized serum creatinine and cystatin C, age and gender), a 9-variable revised CKD-EPI equation (additional auxiliary variables: body mass index, blood urea nitrogen, albumin, uric acid and hemoglobin), and a 9-variable ANN model.

RESULTS

Compared with the 4-variable equation, the 9-variable equation could not achieve superior performance in the internal validation data (mean of difference: 5.00 [3.82, 6.54] vs 4.67 [3.55, 5.90], P = 0.5; interquartile range (IQR) of difference: 18.91 [17.43, 20.48] vs 20.11 [18.46, 21.80], P = 0.05; P30: 76.6% [73.7%, 79.5%] vs 75.8% [72.9%, 78.6%], P = 0.4), but the 9-variable ANN model significantly improve bias and P30 accuracy (mean of difference: 2.77 [1.82, 4.10], P = 0.007; IQR: 19.33 [17.77, 21.17], P = 0.3; P30: 80.0% [77.4%, 82.7%], P < 0.001).

CONCLUSIONS

It is suggested that using complicated non-linear models like ANN could fully utilize the predictive ability of the independent variables, and then finally achieve a superior GFR estimation model.

Serum uromodulin-a marker of kidney function and renal parenchymal integrity

Background

An ELISA to analyse uromodulin in human serum (sUmod) was developed, validated and tested for clinical applications.

Methods

We assessed sUmod, a very stable antigen, in controls, patients with chronic kidney disease (CKD) stages 1-5, persons with autoimmune kidney diseases and recipients of a renal allograft by ELISA.

Results

Median sUmod in 190 blood donors was 207 ng/mL (women: men, median 230 versus 188 ng/mL, P = 0.006). sUmod levels in 443 children were 193 ng/mL (median). sUmod was correlated with cystatin C (rs = -0.862), creatinine (rs = -0.802), blood urea nitrogen (BUN) (rs = -0.645) and estimated glomerular filtration rate (eGFR)-cystatin C (rs  =  0.862). sUmod was lower in systemic lupus erythematosus-nephritis (median 101 ng/mL), phospholipase-A2 receptor- positive glomerulonephritis (median 83 ng/mL) and anti-glomerular basement membrane positive pulmorenal syndromes (median 37 ng/mL). Declining sUmod concentrations paralleled the loss of kidney function in 165 patients with CKD stages 1-5 with prominent changes in sUmod within the 'creatinine blind range' (71-106 µmol/L). Receiver-operating characteristic analysis between non-CKD and CKD-1 was superior for sUmod (AUC 0.90) compared with eGFR (AUC 0.39), cystatin C (AUC 0.39) and creatinine (AUC 0.27). sUmod rapidly recovered from 0 to 62 ng/mL (median) after renal transplantation in cases with immediate graft function and remained low in delayed graft function (21 ng/mL, median; day 5-9: relative risk 1.5-2.9, odds ratio 1.5-6.4). Immunogold labelling disclosed that Umod is transferred within cytoplasmic vesicles to both the apical and basolateral plasma membrane. Umod revealed a disturbed intracellular location in kidney injury.

Conclusions

We conclude that sUmod is a novel sensitive kidney-specific biomarker linked to the structural integrity of the distal nephron and to renal function.

De-indexed estimated glomerular filtration rates: A simple step towards improving accuracy of drug dosing of renally excreted medications in moderate to severe obesity

Kidney function is underestimated in obese individuals when standard equations are applied. Laboratory-reported estimated glomerular filtration rates (eGFR) report glomerular filtration rates corrected for body surface area in mL/min per 1.73 m² using modification of diet in renal disease or the chronic kidney disease-Epidemiology Collaboration equations. This may result in premature discontinuation or reduction in dosage of renally excreted medications. Currently, there are no clinical guidelines defining thresholds beyond which physicians should consider de-indexing patient eGFR values. We compared standard and de-indexed eGFR values for 281 consecutive patients seen in our chronic kidney disease clinic. In our study, half of the patients with a body mass index above 35 had clinically significant changes in their eGFR, with an improvement in chronic kidney disease stage, when eGFR was de-indexed. We propose that eGFR de-indexing should be considered in patients with moderate to severe obesity when calculating the dose, especially for medications that are excreted by the kidneys.

Establishing the presence or absence of chronic kidney disease: Uses and limitations of formulas estimating the glomerular filtration rate

The development of formulas estimating glomerular filtration rate (eGFR) from serum creatinine and cystatin C and accounting for certain variables affecting the production rate of these biomarkers, including ethnicity, gender and age, has led to the current scheme of diagnosing and staging chronic kidney disease (CKD), which is based on eGFR values and albuminuria. This scheme has been applied extensively in various populations and has led to the current estimates of prevalence of CKD. In addition, this scheme is applied in clinical studies evaluating the risks of CKD and the efficacy of various interventions directed towards improving its course. Disagreements between creatinine-based and cystatin-based eGFR values and between eGFR values and measured GFR have been reported in various cohorts. These disagreements are the consequence of variations in the rate of production and in factors, other than GFR, affecting the rate of removal of creatinine and cystatin C. The disagreements create limitations for all eGFR formulas developed so far. The main limitations are low sensitivity in detecting early CKD in several subjects, e.g., those with hyperfiltration, and poor prediction of the course of CKD. Research efforts in CKD are currently directed towards identification of biomarkers that are better indices of GFR than the current biomarkers and, particularly, biomarkers of early renal tissue injury.