Prevalence of pseudonatremia in a clinical laboratory - role of the water content

Pseudohyponatremia: Mechanism, Diagnosis, Clinical Associations and Management

Pseudohyponatremia remains a problem for clinical laboratories. In this study, we analyzed the mechanisms, diagnosis, clinical consequences, and conditions associated with pseudohyponatremia, and future developments for its elimination. The two methods involved assess the serum sodium concentration ([Na]_S) using sodium ion-specific electrodes: (a) a direct ion-specific electrode (ISE), and (b) an indirect ISE. A direct ISE does not require dilution of a sample prior to its measurement, whereas an indirect ISE needs pre-measurement sample dilution. [Na]_S measurements using an indirect ISE are influenced by abnormal concentrations of serum proteins or lipids. Pseudohyponatremia occurs when the [Na]_S is measured with an indirect ISE and the serum solid content concentrations are elevated, resulting in reciprocal depressions in serum water and [Na]_S values. Pseudonormonatremia or pseudohypernatremia are encountered in hypoproteinemic patients who have a decreased plasma solids content. Three mechanisms are responsible for pseudohyponatremia: (a) a reduction in the [Na]_S due to lower serum water and sodium concentrations, the electrolyte exclusion effect; (b) an increase in the measured sample's water concentration post-dilution to a greater extent when compared to normal serum, lowering the [Na] in this sample; (c) when serum hyperviscosity reduces serum delivery to the device that apportions serum and diluent. Patients with pseudohyponatremia and a normal [Na]_S do not develop water movement across cell membranes and clinical manifestations of hypotonic hyponatremia. Pseudohyponatremia does not require treatment to address the [Na]_S, making any inadvertent correction treatment potentially detrimental.

Agreement of Potassium, Sodium, Glucose, and Hemoglobin Measured by Blood Gas Analyzer With Dry Chemistry Analyzer and Complete Blood Count Analyzer: A Two-Center Retrospective Analysis

Background

Blood gas analyzers (BGAs) and dry biochemistry analyzers for potassium and sodium are based on direct electrode methods, and both involve glucose oxidase for glucose detection. However, data are lacking regarding whether the results of the two assay systems can be used interchangeably. In addition, there remains controversy over the consistency between BGA-measured hemoglobin and complete blood count analyzer data. Here, we compared the consistency of sodium, potassium, glucose, and hemoglobin levels measured by BGA and dry chemistry and complete blood count analyzers.

Methods

Data from two teaching hospitals, the Zhejiang Provincial People's Hospital (ZRY) and the Qianfoshan Hospital (QY), were retrospectively analyzed based on dry biochemistry and complete blood count analyzer results as the reference system (X) and BGA as the experimental system (Y). Plasma was used for biochemical analysis at the ZRY Hospital, and serum at the QY Hospital. Paired data from the respective hospitals were evaluated for consistency, and biases between methods were assessed by simple correlation, Passing–Bablok regression, and Bland–Altman analyses.

Results

The correlations of potassium, sodium, glucose, and hemoglobin measured by BGA and dry biochemistry and complete blood count analyzers were high, at 0.9573, 0.8898, 0.9849, and 0.9883 for the ZRY Hospital and 0.9198, 0.8591, 0.9764, and 0.8666, respectively, for the QY Hospital. The results of Passing to Bablok regression analysis showed that the predicted biases at each medical decision level were within clinically acceptable levels for potassium, sodium, glucose, and hemoglobin at the ZRY Hospital. Only the predicted bias of glucose was below the clinically acceptable medical decision levels at the QY Hospital, while potassium, sodium, and hemoglobin were not. Compared with the reference system, the mean bias for BGA measurements at the ZRY Hospital was −0.08 mmol/L (95% confidence interval [CI] −0.091 to −0.069) for potassium, 1.2 mmol/L (95% CI 1.06 to 1.42) for sodium, 0.20 mmol/L (95% CI 0.167 to 0.228) for glucose, and −2.8 g/L for hemoglobin (95% CI −3.14 to −2.49). The mean bias for potassium, sodium, glucose, and hemoglobin at the QY Hospital were −0.46 mmol/L (95% CI −0.475 to −0.452), 3.7 mmol/L (95% CI 3.57 to 3.85), −0.36 mmol/L (95% CI −0.433 to −0.291), and −8.7 g/L (95% CI −9.40 to −8.05), respectively.

Conclusion

BGA can be used interchangeably with plasma electrolyte results from dry biochemistry analyzers but does not show sufficient consistency with serum electrolyte results from dry biochemistry analyzers to allow data interchangeability. Good consistency was observed between BGA and plasma or serum glucose results from dry biochemistry analyzers. However, BGA-measured hemoglobin and hematocrit assay results should be treated with caution.

Interference in Ion-Selective Electrodes Due to Proteins and Lipids

BACKGROUND

Ion-selective electrodes (ISE) have become the mainstay of electrolyte measurements in the clinical laboratory. In most automated analyzers used in large diagnostic laboratories, indirect ISE (iISE) -based electrolyte estimation is done; whereas direct ISE (dISE) -based equipment are mostly used in blood gas analyzers and in the point-of-care (PoC) setting.

CONTENT

Both the techniques, iISE as well as dISE, are scientifically robust; however, the results are often not interchangeable. Discrepancy happens between the two commonly due to interferences that affect the two measuring principles differently. Over the last decade, several studies have reported discrepancies between dISE and iISE arising due to abnormal protein and lipid contents in the sample.

SUMMARY

The present review endeavors to consolidate the knowledge accumulated in relation to interferences due to abnormal protein and lipid contents in sample with the principal focus resting on probable solutions thereof.

Evaluation of the analytical performance of a portable ion-selective electrode meter for measuring whole-blood, plasma, milk, abomasal-fluid, and urine sodium concentrations in cattle

A portable ion-selective electrode (ISE) meter (LAQUAtwin B-722; Horiba Instruments Inc., Irvine, CA) is available for measuring the sodium ion concentration ([Na]) in biological fluids. The objective of this study was to characterize the analytical performance of the ISE meter in measuring [Na] in whole-blood, plasma, milk, abomasal fluid, and urine samples from cattle. Method comparison studies were performed using whole-blood and plasma samples from 106 sick calves and 11 sick cows admitted to a veterinary teaching hospital, 80 milk and 206 urine samples from 16 lactating Holstein-Friesian cows with experimentally induced free water, electrolyte, and acid-base imbalances, and 67 abomasal fluid samples from 7 healthy male Holstein-Friesian calves fed fresh milk with or without an oral electrolyte solution. Deming regression and Bland-Altman plots were used to determine the accuracy of the meter against reference methods. The meter used in direct mode on undiluted samples measured whole-blood [Na] 9.7 mmol/L (7.3%) lower than a direct ISE reference method and plasma [Na] 16.7 mmol/L (12.7%) lower than an indirect ISE reference method. The meter run in direct mode measured milk [Na] 3.1 mmol/L lower and abomasal fluid [Na] 9.0% lower than indirect ISE reference methods. The meter run in indirect mode on diluted samples accurately measured urine [Na] compared with an indirect ISE reference method. We conclude that, after adjustment for the bias determined from Bland-Altman plots, the LAQUAtwin ISE meter provides a clinically useful and low-cost cow-side instrument for measuring [Na] in whole blood, plasma, milk, and abomasal fluid.

Point-of-care testing (POCT) and evidence-based laboratory medicine (EBLM) - does it leverage any advantage in clinical decision making?

Point-of-care testing (POCT) is the analysis of patient specimens outside the clinical laboratory, near or at the site of patient care, usually performed by clinical staff without laboratory training, although it also encompasses patient self-monitoring. It is able to provide a rapid result near the patient and which can be acted upon immediately. The key driver is the concept that clinical decision making may be delayed when samples are sent to the clinical laboratory. Balanced against this are considerations of increased costs for purchase and maintenance of equipment, staff training, connectivity to the laboratory information system (LIS), quality control (QC) and external quality assurance (EQA) procedures, all required for accreditation under ISO 22870. The justification for POCT depends upon being able to demonstrate that a more timely result (shorter turnaround times (TATs)) is able to leverage a clinically important advantage in decision making compared with the central laboratory (CL). In the four decades since POCT was adapted for the self-monitoring of blood glucose levels by subjects with diabetes, numerous new POCT methodologies have become available, enabling the clinician to receive results and initiate treatment more rapidly. However, these instruments are often operated by staff not trained in laboratory medicine and hence are prone to errors in the analytical phase (as opposed to laboratory testing where the analytical phase has the least errors). In some environments, particularly remote rural settings, the CL may be at a considerable distance and timely availability of cardiac troponins and other analytes can triage referrals to the main centers, thus avoiding expensive unnecessary patient transportation costs. However, in the Emergency Department, availability of more rapid results with POCT does not always translate into shorter stays due to other barriers to implementation of care. In this review, we apply the principles of evidence-based laboratory medicine (EBLM) looking for high quality systematic reviews and meta-analyses, ideally underpinned by randomized controlled trials (RCTs), looking for evidence of whether POCT confers any advantage in clinical decision making in different scenarios.