Prevention of medical errors is a major goal of healthcare, though healthcare workers themselves have not yet fully accepted or implemented reliable models of system error, and neither has the public. While there is widespread perception that most medical errors arise from an inappropriate or delayed clinical management, the issue of laboratory errors is receiving a great deal of attention due to their impact on the quality and efficiency of laboratory performances and patient safety. Haemolytic specimens are a frequent occurrence in clinical laboratories, and prevalence can be as high as 3.3% of all of the routine samples, accounting for up to 40%–70% of all unsuitable specimens identified, nearly five times higher than other causes, such as insufficient, incorrect and clotted samples. This article focuses on this challenging issue, providing an overview on prevalence and leading causes of in vivo and in vitro haemolysis, and tentative guidelines on identification and management of haemolytic samples in clinical laboratories. This strategy includes continuous education of healthcare personnel, systematic detection/quantification of haemolysis in any sample, immediate clinicians warning on the probability of in vivo haemolysis, registration of non-conformity, completing of tests unaffected by haemolysis and request of a second specimen for those potentially affected. Clin Chem Lab Med 2008;46:764–72.

Laboratory diagnostics (i.e., the total testing process) develops conventionally through a virtual loop, originally referred to as “the brain to brain cycle” by George Lundberg. Throughout this complex cycle, there is an inherent possibility that a mistake might occur. According to reliable data, preanalytical errors still account for nearly 60%–70% of all problems occurring in laboratory diagnostics, most of them attributable to mishandling procedures during collection, handling, preparing or storing the specimens. Although most of these would be “intercepted” before inappropriate reactions are taken, in nearly one fifth of the cases they can produce inappropriate investigations and unjustifiable increase in costs, while generating inappropriate clinical decisions and causing some unfortunate circumstances. Several steps have already been undertaken to increase awareness and establish a governance of this frequently overlooked aspect of the total testing process. Standardization and monitoring preanalytical variables is of foremost importance and is associated with the most efficient and well-organized laboratories, resulting in reduced operational costs and increased revenues. As such, this article is aimed at providing readers with significant updates on the total quality management of the preanalytical phase to endeavour further improvement for patient safety throughout this phase of the total testing process.

Total quality in laboratory medicine should be defined as the guarantee that each activity throughout the total testing process is correctly performed, providing valuable medical decision-making and effective patient care. In the past decades, a 10-fold reduction in the analytical error rate has been achieved thanks to improvements in both reliability and standardization of analytical techniques, reagents, and instrumentation. Notable advances in information technology, quality control and quality assurance methods have also assured a valuable contribution for reducing diagnostic errors. Nevertheless, several lines of evidence still suggest that most errors in laboratory diagnostics fall outside the analytical phase, and the pre- and postanalytical steps have been found to be much more vulnerable. This collective paper, which is the logical continuum of the former already published in this journal 2 years ago, provides additional contribution to risk management in the preanalytical phase and is a synopsis of the lectures of the 2nd European Federation of Clinical Chemistry and Laboratory Medicine (EFLM)-Becton Dickinson (BD) European Conference on Preanalytical Phase meeting entitled “Preanalytical quality improvement: in quality we trust” (Zagreb, Croatia, 1–2 March 2013). The leading topics that will be discussed include quality indicators for preanalytical phase, phlebotomy practices for collection of blood gas analysis and pediatric samples, lipemia and blood collection tube interferences, preanalytical requirements of urinalysis, molecular biology hemostasis and platelet testing, as well as indications on best practices for safe blood collection. Auditing of the preanalytical phase by ISO assessors and external quality assessment for preanalytical phase are also discussed.

Laboratory diagnostics, a pivotal part of clinical decision making, is no safer than other areas of healthcare, with most errors occurring in the manually intensive preanalytical process. Patient misidentification errors are potentially associated with the worst clinical outcome due to the potential for misdiagnosis and inappropriate therapy. While it is misleadingly assumed that identification errors occur at a low frequency in clinical laboratories, misidentification of general laboratory specimens is around 1% and can produce serious harm to patients, when not promptly detected. This article focuses on this challenging issue, providing an overview on the prevalence and leading causes of identification errors, analyzing the potential adverse consequences, and providing tentative guidelines for detection and prevention based on direct-positive identification, the use of information technology for data entry, automated systems for patient identification and specimen labeling, two or more identifiers during sample collection and delta check technology to identify significant variance of results from historical values. Once misidentification is detected, rejection and recollection is the most suitable approach to manage the specimen.Clin Chem Lab Med 2009;47:143–53.

Obesity and metabolic syndrome increase the risk of cardiovascular morbidity and mortality. Oxidative stress seems to be involved in the pathophysiology of diabetes and cardiovascular complications of metabolic syndrome. The aim of our study was to evaluate the level of oxidative stress and inflammation in obese adults with and without metabolic syndrome. Oxidative stress and inflammation markers (total amount of free radicals, malondialdehyde, allantoin, alpha1-antiproteinase, oxidized/reduced glutathione ratio, high-sensitive C-reactive protein, fibrinogen), total antioxidant capacity and lipid standardized alpha-tocopherol were determined in obese subjects fulfilling at least three criteria of metabolic syndrome according to the National Cholesterol Education Program-Adult Treatment Panel III guidelines (n=20 patients), in obese subjects without metabolic syndrome (n=20 patients) and in 48 healthy controls. Oxidative stress and inflammation markers were significantly elevated in the obese subjects, especially in those exhibiting metabolic syndrome. According to multidimensional statistical analysis, oxidative stress was independently related to triacylglyceride concentration, abdominal fat, low high-density lipoprotein cholesterol and low lipid standardized alpha-tocopherol in the patients with metabolic syndrome. High levels of free radicals together with low antioxidant capacity detected in obese adults indicate elevated oxidative stress, which is--together with systemic inflammation--further potentiated in the case of obese patients with metabolic syndrome. This imbalance in oxidative/antioxidative status and subclinical inflammatory state leads to higher risk of atherosclerotic and diabetic complications.

Background: In vitro hemolysis, the prevailing cause of preanalytical error in routine laboratory diagnostics, might influence the reliability of several tests, affect the quality of the total testing process and jeopardize patient safety. Although laboratory instrumentation is now routinely equipped with systems capable of automatically testing and eventually correcting for hemolysis interference, to our knowledge there are no reports that have compared the efficiency of different analytical platforms for identifying and classifying specimens with hemolysis. Methods: Serum from a healthy volunteer was spiked with varying amounts of hemolyzed blood from the same volunteer, providing a serum free hemoglobin concentration ranging from 0.0 g/L to 2.0 g/L as measured by the reference cyanmethemoglobin assay. The spiked serum samples were shipped to seven separate laboratories and the hemolysis index (HI) was tested in triplicate on the following analytical platforms: Roche Modular System P (n=4) and Integra 400 Plus (n=1), Siemens Dimension RxL (n=3), ADVIA 2400 (n=1) and ADVIA 1800 (n=1), Olympus AU 680 (n=1) and Coulter DXC 800 (n=1). Results: Satisfactory agreement of HI results was observed among the various analytical platforms, despite a trend toward overestimation by the ADVIA 2400 and 1800. After normalizing results according to the instrument-specific alert value, discrepancies were considerably reduced. All instruments except for the Dimension RxL gave values normalized to the instrument-specific alert value, 1.0 for the sample with 0.075 g/L free hemoglobin. The results of the four Modular System P tests were also highly reproducible among the different facilities. When evaluating instruments that provided quantitative HI results, the mean intra-assay coefficient of variation (CV) calculated for the triplicate determinations was always between 0.1% and 2.7%. Conclusions: The results of this multicenter evaluation confirm that efficiency of different analytical platforms to correctly identify and classify unsuitable samples is satisfactory. However, more effort should be placed on the standardization of reporting HI. All the instruments that we tested provide either quantitative or qualitative results that are essentially comparable, but which should always be compared with the instrument-specific alert values to harmonize their efficiency. Clin Chem Lab Med 2009;47:934–9.