Estimating the effects of hemolysis on potassium and LDH laboratory results

Determination of the Optimal Wavelength of the Hemolysis Index Measurement

Many biochemical auto-analyzers have methods that measure the hemolysis index (HI) to quantitatively assess the degree of hemolysis. Past reports on HI are mostly in vitro studies. Therefore, we evaluated the optimal wavelength of HI measurement ex vivo using clinical samples. Four different wavelengths (410/451 nm: HI-1, 451/478 nm: HI-2, 545/596 nm: HI-3 and 571/596 nm: HI-4) were selected for HI measurement, and correlations were examined from the measurement results of 3890 clinical samples. Another set of 9446 clinical samples was used to examine the correlation of HI with lactate dehydrogenase (LDH), aspartate aminotransferase (AST) and potassium (K). Strong correlations were found between HI-4 and HI-1 and between HI-4 and HI-3. HI-1 and HI-2 cannot correctly assess hemolysis for high bilirubin samples, and HI-3 cannot correctly assess hemolysis for high triglyceride samples. LDH, AST and K correlated positively with HI-4 in clinical samples. For every 1-unit increase in HI-4, LDH increased by 19.51 U/L, AST by 1.03 U/L and K by 0.061 mmol/L, comparable to reports of other studies. In clinical samples, HI-4 was less susceptible to bilirubin and chyle and reflected well the changes in LDH, AST and K caused by hemolysis. This suggested that the optimal wavelength for HI measurement is 571 nm.

Characterizing ability of serum potassium (K) and the serum K reference interval to flag hypokalemia or hyperkalemia as observed in plasma: a simulation study

OBJECTIVES

Serum potassium (K) exhibits a positive shift relative to plasma K due to a variable amount of K release associated with clotting. Because of this variation, plasma K results outside of the reference interval (RI) for plasma (hypokalemia or hyperkalemia) in individual samples may not produce classification-concordant results in serum according to the serum RI. We examined this premise from a theoretical standpoint by simulation.

DESIGN & METHODS

We used textbook K reference intervals for plasma (PRI = 3.4-4.5 mmol/L) and serum (SRI = 3.5-5.1 mmol/L). The difference between PRI and SRI is characterized by a normal distribution: serum K = plasma K + 0.35 ± 0.308 mmol/L. This transformation was applied by simulation to an observed patient data distribution for plasma K to generate a corresponding theoretical serum K distribution. Individual samples were tracked for comparison with respect to classification (below, within, above RI) for plasma and serum.

RESULTS

Primary data were an all-comers plasma K patient distribution (n = 41,768; median = 4.1 mmol/L; 7.1% below PRI (hypokalemia); 15.5% above PRI (hyperkalemia)). Simulation to obtain the associated serum K yielded a right-shifted distribution (median = 4.4 mmol/L; 4.8% below SRI; 10.8% above SRI). Sensitivity for detection in serum (flagged below SRI) for samples originating as hypokalemic in plasma was 45.7% (specificity = 98.3%). Sensitivity for detection in serum (flagged above SRI) for samples originating as hyperkalemic in plasma was 56.6% (specificity = 97.6%).

CONCLUSIONS

Simulation results indicate that serum K should best be thought of as an inferior substitute marker for plasma K. These results follow simply from the variable component of serum K compared to plasma K. Plasma should be the preferred specimen type for K assessment.

Reducing Specimen Rejection Rates Using Concentration-Dependent Hemolysis Rejection Thresholds

BACKGROUND

Using middleware solutions, it is possible to implement concentration-dependent analyte-specific hemolysis rejection limits. This makes day-to-day reporting of clinical specimens more efficient and potentially lowers sample rejection rates compared to a "one-size-fits-all" approach (i.e., solely based on a single cutoff provided in the package insert).

METHODS

Hemolysis interference studies were performed at multiple analyte concentrations for three frequently ordered tests. For each assay, concentration-dependent hemolysis rejection limits were designed based on the total allowable error (TAE) for the analyte as well as the clinical significance of such incurred inaccuracy at the respective concentrations. In general, the following rationale was used: if the interference exceeds 10% (or package insert cutoffs), a comment is placed on the result. If the interference exceeds the TAE, the result will not be reported. Reduction in specimen rejection rates were estimated by comparing the incurred specimen rejection rates when package inserts' vs concentration-dependent hemolysis interference limits were applied to a data set in our institute during a three-month period.

RESULTS

Concentration-dependent analyte-specific hemolysis rejection thresholds were designed for three commonly ordered assays that are especially susceptible to hemolysis interference. It is estimated that these novel thresholds for aspartate aminotransferase (AST), lactate dehydrogenase (LDH), and direct bilirubin (DBIL) reduced specimen rejection rates from 9.3% to 1.3%, 31.4% to 4.8%, and 19.9% to 7.1%, respectively.

CONCLUSIONS

Concentration-dependent analyte-specific hemolysis rejection thresholds for three commonly ordered assays can reduce rejection rates without significantly compromising the quality of test results.

Optimization of the Hemolysis Assay for the Assessment of Cytotoxicity

In vitro determination of hemolytic properties is a common and important method for preliminary evaluation of cytotoxicity of chemicals, drugs, or any blood-contacting medical device or material. The method itself is relatively straightforward, however, protocols used in the literature vary substantially. This leads to significant difficulties both in interpreting and in comparing the obtained values. Here, we examine how the different variables used under different experimental setups may affect the outcome of this assay. We find that certain key parameters affect the hemolysis measurements in a critical manner. The hemolytic effect of compounds tested here varied up to fourfold depending on the species of the blood source. The use of different types of detergents used for generating positive control samples (i.e., 100% hemolysis) produced up to 2.7-fold differences in the calculated hemolysis ratios. Furthermore, we find an expected, but substantial, increase in the number of hemolyzed erythrocytes with increasing erythrocyte concentration and with prolonged incubation time, which in turn affects the calculated hemolysis ratios. Based on our findings we propose an optimized protocol in an attempt to standardize future hemolysis studies.

What is the best wavelength for the measurement of hemolysis index?

BACKGROUND

Hemolysis is a common problem in the handling of serum specimens. The hemolysis index (HI) provides a warning of hemolysis in auto-analyzers. However, HI has not been standardized, and each laboratory's original method is applied. Especially, the wavelength used for HI measurement is different in each laboratory. Thus, we investigated the warning ability of HI at various wavelengths.

METHODS

We selected 4 wavelength types, and each HI was measured and calculated (410 nm/HI-1, 451 nm/HI-2, 545 nm/HI-3, and 571 nm/HI-4). To compare the 4 HI types, we investigated the influence of 3 interference components using artificially hemolyzed specimens (AHSs). We also investigated both the relationship between HI and hemoglobin concentration (Hb) and that between HI and 31 biochemical test values in AHSs.

RESULTS

In the interference assessment, only HI-4 showed no influence on the 3 interference components. The correlation between Hb and HI-4 was very strong (r_S = 0.9987). A 1-unit increase in HI-4 corresponded to a 14.8-mg/dL increase in Hb.

CONCLUSION

We found the best wavelength for HI to be at or near 571 nm.

A statistical model for restoration of serum potassium level disturbed by hemolysis

BACKGROUND

Blood sample hemolysis affects pre-analytical quality and may cause pseudohyperkalemia. We established a statistical model to estimate the corrected potassium (K⁺) in serum.

METHODS

Serum K⁺ and H index were analyzed, and blood cell index was obtained from the examined Full Blood Examination (FBE) results. A linear-regression model was developed using hemolysis (H) index, K⁺ and covariates of blood cell index from 139 cell lysates of blood samples. The model was then validated against 26 in vitro physically hemolyzed serum samples.

RESULTS

The final model selected H index, hemoglobin concentration (HGB), and hematocrit (HCT) as important predictors in estimating the K⁺ content. The model was validated against artificially hemolyzed serum samples, which returned a correlation of 0.942 between observed and predicted net K⁺ increase by hemolysis. The predictors H index, HCT, and HB contributed 93.7%, 3.5% and 2.8% to the model R², respectively.

CONCLUSION

In vitro hemolysis induced pseudohyperkalemia could be accurately predicted and restored by our model for clinical application.

Correcting laboratory results for the effects of interferences: an approach incorporating uncertainty of measurement

BACKGROUND

Results of numerical pathology tests may be subject to interference and many laboratories identify such interferences and withhold results or issue warnings if clinically erroneous results may be issued. Some laboratories choose to correct for the effect of interferences, with the uncertainty of the correction noted as a limitation in this process. We investigate the effect of correcting for the effect of interferences on the ability to release results within defined error goals using the effect of in-vitro haemolysis on serum potassium measurement as an example.

METHODS

A model was developed to determine the uncertainty of a result corrected for the effect of an interferent with a linear relationship between concentration and effect. The model was used to assess the effect of correction on the results which could be released within specified accuracy criteria.

RESULTS

Using the effects of haemolysis on potassium results as an example, the maximum amount of haemolysis in a sample that would change the result by > 0.5 mmol/L, with a frequency of 5%, was increased from approximately 1100 mg/L (no correction) to 8000 mg/L (with correction).

CONCLUSIONS

With modelling of the factors related to the uncertainties of results in the presence of interferences, it is possible to release results in the presence of significantly higher concentrations of interferences after correction than without correction. Correction of a result for a known bias and allowance for the uncertainty of the correction can be considered consistent with the guide to the expression of uncertainty in measurement (GUM).