Is There a Risk of Misinterpretation of Potassium Concentration from Undetectable Hemolysis Using a POCT Blood Gas Analyzer in the Emergency Department?

The influence of undetected hemolysis on POCT potassium results in the emergency department

OBJECTIVES

This study aimed to evaluate discrepancies in potassium measurements between point-of-care testing (POCT) and central laboratory (CL) methods, focusing on the impact of hemolysis on these measurements and its impact in the clinical practice in the emergency department (ED).

METHODS

A retrospective analysis was conducted using data from three European university hospitals: Technische Universitat Munchen (Germany), Hospital Universitario La Paz (Spain), and Erasmus University Medical Center (The Netherlands). The study compared POCT potassium measurements in EDs with CL measurements. Data normalization was performed in categories for potassium levels (kalemia) and hemolysis. The severity of discrepancies between POCT and CL potassium measurements was assessed using the reference change value (RCV).

RESULTS

The study identified significant discrepancies in potassium between POCT and CL methods. In comparing POCT normo- and mild hypokalemia against CL results, differences of -4.20 % and +4.88 % were noted respectively. The largest variance in the CL was a +4.14 % difference in the mild hyperkalemia category. Additionally, the RCV was calculated to quantify the severity of discrepancies between paired potassium measurements from POCT and CL methods. The overall hemolysis characteristics, as defined by the hemolysis gradient, showed considerable variation between the testing sites, significantly affecting the reliability of potassium measurements in POCT.

CONCLUSIONS

The study highlighted the challenges in achieving consistent potassium measurement results between POCT and CL methods, particularly in the presence of hemolysis. It emphasised the need for integrated hemolysis detection systems in future blood gas analysis devices to minimise discrepancies and ensure accurate POCT results.

Accurate correction model of blood potassium concentration in hemolytic specimens

BACKGROUND AND AIMS

The results of blood potassium can be seriously affected by specimen hemolysis which may interfere with clinicians' interpretation of test results. Redrawing blood and retesting may delay treatment time and it is not feasible for critically ill patients with difficulty in specimen collection. Therefore, it is significant to establish a mathematical model that can quickly correct the blood potassium concentration of hemolytic specimens.

MATERIALS AND METHODS

The residual blood samples from 107 patients at Peking University Third Hospital were collected to establish potassium correction model. Samples with different hemolysis indexes were obtained by ultrasonic crushing method. Blood potassium correction models of hemolysis specimens were established by linear regression and curve fitting using SPSS and MATLAB, respectively. In addition, blood samples from another 85 patients were used to verify the accuracy of the models and determine the optimal model.

RESULTS

Variation of potassium (ΔK) was 0.003HI-0.03 (R2 = 0.9749) in linear regression model which had high correlation in ΔK and HI, and the correction formula was Kcorrection = Khemolysis-0.003 × HI + 0.03. Average rate of potassium change (αaverage) was 0.003 ± 0.0002 mmol/L in curve fitting model, and correction formula was Kcorrection = Khemolysis-0.003 × HI, and both men and women can use the same correction model. The accuracy of linear regression model was 96.5 %, and there was statistical difference between the verification results and the measured values (p < 0.05), while the accuracy of curve fitting model was 100 %, and there was no statistical difference between the verification results and the measured values (p = 0.552). The model was validated in an independent set of samples and all were within the TEa of 6 % and the accuracy of 100 %.

CONCLUSIONS

Both linear regression and curve fitting models of potassium correction had high accuracy, and can effectively correct the potassium concentration of hemolytic specimens, while the curve fitting model have superior accuracy.

Free hemoglobin determination at patients' bedside to evaluate hemolysis

Background: The authors report the relevance of using a point of care test (Helge®) for free hemoglobin determination and concordance of the values the with Cobas® 8000 and spectrophotometer methods. Results: The within-run of the point of care test was <3%. Good correlations among the three methods were observed and an acceptable concordance for hemolysis index values from 50 mg/dl. An excellent agreement between the Cobas 8000 and the spectrophotometer was found. Conclusion: Automated methods represent methods of choice for free hemoglobin determination. An advantage of the Helge system is that it can be applied to samples experiencing a delay in evaluation due to the long distance between the collection site and the central laboratory. Another advantage is its use at the bedside, in the monitoring of extracorporeal membrane oxygenation patients.

Erroneous potassium results: preanalytical causes, detection, and corrective actions

Potassium is one of the most requested laboratory tests. Its level is carefully monitored and maintained in a narrow physiological range. Even slightly altered potassium values may severely impact the patient's health, which is why an accurate and reliable result is of such importance. Even if high-quality analytics are available, there are still numerous ways in which potassium measurements may be biased, all of which occur in the preanalytical phase of the total laboratory testing process. As these results do not reflect the patient's in-vivo status, such results are referred to as pseudo-hyper/hypokalemia or indeed pseudo-normokalemia, depending on the true potassium result. Our goal in this review is to present an in-depth analysis of preanalytical errors that may result in inaccurate potassium results. After reviewing existing evidence on this topic, we classified preanalytical errors impacting potassium results into 4 categories: 1) patient factors like high platelet, leukocytes, or erythrocyte counts; 2) the sample type 3) the blood collection procedure, including inappropriate equipment, patient preparation, sample contamination and others and 4) the tube processing. The latter two include sample transport and storage conditions of whole blood, plasma, or serum as well as sample separation and subsequent preanalytical processes. In particular, we discuss the contribution of hemolysis, as one of the most frequent preanalytical errors, to pseudo-hyperkalemia. We provide a practical flow chart and a tabular overview of all the discussed preanalytical errors including possible underlying mechanisms, indicators for detection, suggestions for corrective actions, and references to the according evidence. We thereby hope that this manuscript will serve as a resource in the prevention and investigation of potentially biased potassium results.