Problems with estimating reference change values (critical differences)

Biological variation in the estimated glomerular filtration rate of healthy individuals within 24 h calculated using 2021CKD-EPI equations

BACKGROUND AND AIMS

Use the MDRD (Modification of Diet in Renal Disease) and 2021 CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) equation void of race coefficients (CKD-EPICrea, CKD-EPICys-C, and CKD-EPICrea+Cys-C) to estimate the BV (Biological variation) of eGFR (estimated glomerular filtration rate) within 24 h in a healthy population to help explain future studies using eGFR in the context of a known BV.

METHODS

Blood samples were collected from 30 healthy subjects at six time points within 24 h. Serum creatinine (S-Crea) and serum cystatin C (S-Cys-C) were measured, and the BV of eGFR was calculated. Outlier and variance homogeneity analyses were performed, followed by CV-ANOVA on trend-corrected data.

RESULTS

The eGFR CVI for the four equations (MDRD, CKD-EPICrea, CKD-EPICys-C, and CKD-EPICrea+Cys-C) were 8.39% (7.50-9.51%), 3.90% (3.49-4.42%), 6.58% (5.88-7.46%), and 5.03% (4.50-5.71%), respectively. The corresponding II and RCVpos/neg values were 0.69, 0.48, 0.51, and 0.31, and (29.30%, - 22.66%), (12.69%, - 11.2 6%), (20.97%, - 17.33%), and (15.88%, - 13.70%), respectively; RCVpos /neg of eGFR was highest in the MDRD equation and lowest in the CKD-EPI Crea equation. Additionally, the RCVpos/neg values of the individual was highest in the MDRD equation and lowest in the CKD-EPICrea+Cys-C equation; they are (56.51%, - 36.11%) and (5.01%, - 4.77%), respectively.

CONCLUSIONS

We present data on the 24 h BV eGFR of the 2021 CKD-EPI equations. The presence of BV has impact on the interpretation of GFR results, affecting CKD disease grading. The RCVpos/neg differences were large among the individuals. When using eGFRs based on the MDRD and CKD-EPI equations, it is necessary to combine RCVpos/neg values before interpreting the results.

A New Concept for Reference Change Values-Regression to the Population Mean

BACKGROUND

Reference change values (RCV) are used to indicate a change in analyte concentration that is unlikely to be due to random variation in the patient or the measurement. Current theory describes RCV relative to a first measurement result (X1). We investigate an alternative view predicting the starting point for RCV calculations from X1 and its location in the reference interval.

METHODS

Data for serum sodium, calcium, and total protein from the European Biological Variation study and from routine clinical collections were analyzed for the effect of the position of X1 within the reference interval on the following result from the same patient. A model to describe the effect was determined, and an equation to predict the RCV for a sample in a population was developed.

RESULTS

For all data sets, the midpoints of the RCVs were dependent on the position of X1 in the population. Values for X1 below the population mean were more likely to be followed by a higher result, and X1 results above the mean were more likely to be followed by lower results. A model using population mean, reference interval dispersion, and result diagnostic variation provided a good fit with the data sets, and the derived equation predicted the changes seen.

CONCLUSIONS

We have demonstrated that the position of X1 within the reference interval creates an asymmetrical RCV. This can be described as a regression to the population mean. Adding this concept to the theory of RCVs will be an important consideration in many cases.

Interpreting two TSH results from the same patient

OBJECTIVES

When the patient's mean (setpoint) concentration of an analyte is unknown and the physician tries to judge the clinical condition from the analyte concentration in two separate specimens taken a time apart, we believe that the two values should be judged against a bivariate reference interval derived from clinically healthy and stable individuals, rather than using univariate reference limits and comparing the difference between the values against reference change values (RCVs). In this work we compared the two models, using s-TSH as an example.

METHODS

We simulated two s-TSH measurement values for 100,000 euthyreot subjects, and plotted the second value against the first, along with a markup of the central 50, 60, 70, 80, 90, and 95 % of the bivariate distribution, in addition to the 2.5 and 97.5 percentile univariate reference limits and the 2.5 and 97.5 percentile RCVs. We also estimated the diagnostic accuracy of the combination of the 2.5 and 97.5 univariate percentile reference limits and the 2.5 and 97.5 percentile RCVs against the central 95 % of the bivariate distribution.

RESULTS

Graphically, the combination of the 2.5 and 97.5 univariate reference limits and the 2.5 and 97.5 percentile RCVs did not accurately delineate the central 95 % of the bivariate distribution. Numerically, the sensitivity and specificity of the combination were 80.2 and 92.2 %, respectively.

CONCLUSIONS

The concentrations of s-TSH measured in two samples taken at separate times from a clinically healthy and stable individual cannot be accurately interpreted using the combination of univariate reference limits and RCVs.

Is minimising waste volume for drawing blood samples in critically ill patients feasible?

INTRODUCTION

Drawing blood samples through a central venous catheter (CVC) is a customary practice in Intensive Care Units (ICUs). It is indicated to discard a volume of waste blood to avoid interference in the results.

AIM

To determine whether a lower discard volume for obtaining blood samples from temporary CVCs placed into the internal jugular, femoral or subclavian vein offers valid results.

METHOD

A quasi-experimental prospective cross-sectional study for which sixty-five patients of over 18 years of age in ICUs, who had been fitted with a triple lumen central venous catheter, were recruited over a period of eight months. Two consecutive blood samples were extracted with tubes for biochemistry, coagulation and hemogram from each patient from the distal lumen. The first sample was obtained with a discarded waste of 1.5 ml from a total extracted volume of 10.2 ml, similar to the usual waste in our ambit (10 ml). Subsequently the second sample was obtained. The paired t-test was used to analyse the data. The Bland-Altman plot and intraclass correlation coefficient (ICC) were used to measure the agreement between methods. The reference change value (RCV) was established as the admissible limit of variation between the pairs of samples.

RESULTS

A total of 65 sample pairs were drawn (intervention-control). The paired t-test found statistically significant differences with a significance level of α = .05 for chlorine (-.536; .012); prothrombin time (-.092; .019) and prothrombin activity (.284; 1.375).The ICC was greater than .9 in all the variables and the limit determined for the RCV was not surpassed by any value.

CONCLUSIONS

The results show the reliability of the blood samples drawn with a discard volume of 1.5 ml.