Format

Send to

Choose Destination

See 1 citation in Scand J Clin Lab Invest 2017:

Scand J Clin Lab Invest. 2017 May;77(3):199-204. doi: 10.1080/00365513.2017.1292362. Epub 2017 Feb 28.

Accuracy diagrams: a novel way to illustrate uncertainty of estimated GFR.

Author information

1
a Department of Laboratory Medicine , Lund University , Lund , Sweden.
2
b Department of Clinical Sciences , Lund University , Malmö , Sweden.
3
c Department of Translational Medicine , Lund University , Malmö , Sweden.

Abstract

Most studies that validate GFR equations present accuracy results stratified by measured GFR (mGFR; diagnostic correctness) or by estimated GFR (eGFR; diagnostic predictiveness) only, without a clear distinction in interpretation. The accuracy of a GFR equation is normally reported in percent (e.g. P30), but is often misinterpreted when stratified by eGFR. The aim of the study was to develop new accuracy measures and diagrams that allow straightforward interpretations and illustrations of the uncertainty in eGFR in clinical practice. We applied quantile regression to the distribution of estimation errors for two creatinine-based GFR equations, LM-REV and CKD-EPI, in a clinical cohort (n = 3495) referred for GFR measurement (plasma clearance of iohexol). Measures of bias and precision and accuracy intervals (AIs) were expressed in mL/min/1.73 m2. Diagrams with AIs were chosen as a novel way to present the error margin in eGFR at a pre-specified certainty level. It was shown that creatinine-based equations are still quite inaccurate in that large estimation errors could not be ruled out with satisfactory certainty. As an example, the 75% AI for the most accurate equation, LM-REV, was approximately ±10 mL/min/1.73 m2 at eGFR = 45 mL/min/1.73 m2, whereas it ranged between -13 and +20 mL/min/1.73 m2 at eGFR = 90 mL/min/1.73 m2. Accuracy intervals presented in diagrams can be used to illustrate the uncertainty of eGFR. Future validation studies should assess the variability in the predictiveness of eGFR across populations and clinical settings using tools and performance measures that are easy to interpret.

KEYWORDS:

Renal insufficiency; bias (epidemiology); clinical decision-making; predictive value of tests; reproducibility of results; statistical regression

PMID:
28276725
DOI:
10.1080/00365513.2017.1292362
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Taylor & Francis
Loading ...
Support Center