Problems with measurements caused by high concentrations of serum solids

Distribution ratios of ethanol and water between whole blood, plasma, serum, and erythrocytes: Recommendations for interpreting clinical laboratory results in a legal context

This article reviews the scientific literature dealing with the distribution of ethanol and water between whole blood (WB), plasma, serum, and erythrocytes (red-blood cells). Knowledge of the ethanol distribution ratio is important when analytical results derived from hospital clinical laboratories are interpreted in a forensic context, such as during the prosecution of traffic offenders. Statutory blood-alcohol concentration (BAC) limits for driving are defined as the concentration of ethanol in WB and not in plasma, serum or red-blood cells. These bio-fluids differ in their water content and thereby the concentrations of ethanol. Plasma and serum contain ~90%-92% w/w water, WB ~78%-80% w/w and erythrocytes ~64%-66% w/w. The mean plasma/WB and serum/WB distribution ratios of ethanol are therefore expected to be ~1.15:1 (91/79 = 1.15), which is in good agreement with values determined empirically. However, in individual cases, the actual distribution ratio will depend on the person's age, gender, and biochemical and hematological properties of the blood specimen, such as its hematocrit. For legal purposes, we recommend that the concentration of ethanol in plasma or serum determined at hospital laboratories is divided by a factor of 1.2, which would provide a conservative estimate of the co-existing BAC and the chance of overestimating the true value is only 1 in 2000 (0.05%).

Spurious counts and spurious results on haematology analysers: a review. Part II: white blood cells, red blood cells, haemoglobin, red cell indices and reticulocytes

Haematology analysers provide quick and accurate results in most situations. However, spurious results, related either to platelets (part I of this report) or to other parameters from the cell blood count (CBC) may be observed in several instances. Spuriously low white blood cell (WBC) counts may be observed because of agglutination in the presence of ethylenediamine tetra-acetic acid (EDTA). Cryoglobulins, lipids, insufficiently lysed red blood cells (RBC), erythroblasts and platelet aggregates are common situations increasing WBC counts. In most of these instances flagging and/or an abnormal WBC differential scattergram will alert the operator. Several situations lead to abnormal haemoglobin measurement or to abnormal RBC count, including lipids, agglutinins, cryoglobulins and elevated WBC counts. Mean (red) cell volume (MCV) may be also subject to spurious determination, because of agglutinins, excess of glucose or salts and technological considerations. In turn, abnormality related to one measured parameter will lead to abnormal calculated RBC indices: mean cell haemoglobin content (MCHC) is certainly the most important RBC indices to consider, as it is as important as flags generated by the haematology analysers (HA) in alerting the user to a spurious result. In many circumstances, several of the measured parameters from CBC may be altered, and the discovery of a spurious change on one parameter frequently means that the validity of other parameters should be considered. Sensitive flags now allow the identification of several spurious counts, but only the most sophisticated HA have optimal flagging and more simple HA, especially those without a WBC differential scattergram, do not possess the same sensitivity for detecting anomalous results. Reticulocytes are integrated now into the CBC in many HA, and several situations may lead to abnormal counts.

Recommendations for detection and management of unsuitable samples in clinical laboratories

A large body of evidence attests that quality programs developed around the analytical phase of the total testing process would only produce limited improvements, since the large majority of errors encountered in clinical laboratories still prevails within extra-analytical areas of testing, especially in manually intensive preanalytical processes. Most preanalytical errors result from system flaws and insufficient audit of the operators involved in specimen collection and handling responsibilities, leading to an unacceptable number of unsuitable specimens due to misidentification, in vitro hemolysis, clotting, inappropriate volume, wrong container or contamination from infusive routes. Detection and management of unsuitable samples are necessary to overcome this variability. The present document, issued by the Italian Inter-society SIBioC-SIMeL-CISMEL (Society of Clinical Biochemistry and Clinical Molecular Biology-Italian Society of Laboratory Medicine-Italian Committee for Standardization of Hematological and Laboratory Methods) Study Group on Extra-analytical Variability, reviews the major causes of unsuitable specimens in clinical laboratories, providing consensus recommendations for detection and management.

Clin Chem Lab Med 2007;45:728–36.

Clinical review: the meaning of acid-base abnormalities in the intensive care unit part I - epidemiology

Acid-base abnormalities are common in critically ill patients. Our ability to describe acid-base disorders must be precise. Small differences in corrections for anion gap, different types of analytical processes, and the basic approach used to diagnose acid-base aberrations can lead to markedly different interpretations and treatment strategies for the same disorder. By applying a quantitive acid-base approach, clinicians are able to account for small changes in ion distribution that may have gone unrecognized with traditional techniques of acid-base analysis. Outcome prediction based on the quantitative approach remains controversial. This is in part due to use of various technologies to measure acid-base variables, administration of fluid or medication that can alter acid-base results, and lack of standardized nomenclature. Without controlling for these factors it is difficult to appreciate the full effect that acid-base disorders have on patient outcomes, ultimately making results of outcome studies hard to compare.