Practical solution for control of the pre-analytical phase in decentralized clinical laboratories for meeting the requirements of the medical laboratory accreditation standard DIN EN ISO 15189
Uberörtliche Gemeinschaftspraxis für Laboratoriumsmedizin Leverkusen-Köln-Wenrath, Standort Köln, Labor Laser und Kollegen, Cologne, Germany. Clinical laboratory
(Impact Factor: 1.13).
This report was written in response to the article by Wood published recently in this journal. It describes a practical solution to the problems of controlling the pre-analytical phase in the clinical diagnostic laboratory. As an indicator of quality in the pre-analytical phase of sample processing, a target analyte was chosen which is sensitive to delay in centrifugation and/or analysis. The results of analyses of the samples sent by satellite medical practitioners were compared with those from an on-site hospital laboratory with a controllable optimized pre-analytical phase. The aim of the comparison was: (a) to identify those medical practices whose mean/median sample values significantly deviate from those of the control situation in the hospital laboratory due to the possible problems in the pre-analytical phase; (b) to aid these laboratories in the process of rectifying these problems. A Microsoft Excel-based Pre-Analytical Survey tool (PAS tool) has been developed which addresses the above mentioned problems. It has been tested on serum potassium which is known to be sensitive to delay and/or irregularities in sample treatment. The PAS tool has been shown to be one possibility for improving the quality of the analyses by identifying the sources of problems within the pre-analytical phase, thus allowing them to be rectified. Additionally, the PAS tool has an educational value and can also be adopted for use in other decentralized laboratories.
Available from: Giuseppe Lippi
- "It is important to mention, however, that an inverse trend was observed for the relative frequency of preanalytical errors, where the error rate has increased from to 68 to 87% in 10 y (p b 0.001, by χ 2 test) , meaning that major efforts should still be placed on those procedures, especially the manually intensive ones, which are performed before the sample reaches the laboratory. 3. Governance of the preanalytical phase: the road to improvement As for any other type of medical errors, implementation of a total quality management system is the most effective road to improvement   , by encompassing a multifaceted strategy which ultimate target is to decrease the uncertainty inherent to this process. The preanalytical phase begins with the formulation of a reliable clinical suspicion, the request of the most appropriate investigations to make (or exclude) a potential diagnosis, and continues with collection, transportation, treatment, handling and storage of the biological samples. "
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ABSTRACT: Medical errors can be traditionally clustered into 4 categories, which include errors of diagnosis, errors of treatment, errors of prevention, and an 'other miscellaneous' category. Owing to the volume and complexity of testing, and considering that laboratory error is defined as any defect from ordering tests to reporting results and appropriately interpreting and reacting on these, it is not surprising that mistakes in the total testing process occur with frequency, have connections to all four types of medical errors and represent a serious hazard for patient health. Throughout the laboratory diagnostics, preanalytical problems prevail. Moreover, the positive trends towards reduction of laboratory errors over the past decade, particularly those in the analytical phase, has little involved the preanalytical phase, which actually represents the most critical area to target. In particular, the high frequency of errors still attributable to processes external to the laboratory requires additional efforts for the governance of this mistreated phase of the total testing process, so that we can finally find the right path to progress from the dark to the bright side of the moon. As for any other type of medical errors, the most effective path to improvement is the implementation of a total quality management system, encompassing a multifaceted strategy for process and risk analysis, based on error prevention, detection, and management.
Clinica chimica acta; international journal of clinical chemistry 04/2009; 404(1):32-6. DOI:10.1016/j.cca.2009.03.026 · 2.82 Impact Factor
Available from: Gianfranco Cervellin
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ABSTRACT: We planned a study to establish whether spurious hemolysis may be present in low volume tubes or partially filled tubes.
Four serum tubes were collected in sequence from 20 healthy volunteers, i.e., 4.0 mL, 13 x 75 mm (discard tube), 6.0 mL, 13 x 100 mm half-filled, 4.0 mL, 13 x 75 mm full-draw and 6.0 mL, 13 x 100 mm full-draw. Serum was separated and immediately tested for hemolysis index (HI), potassium, aspartate aminotransferase (AST), and lactate dehydrogenase (LDH).
The HI always remained below the limit of detection of the method (< 0.5 g/L) in all tubes. No statistically significant differences were recorded in any parameter except potassium, which increased by 0.10 mmol/L in 4 mL full-draw tubes. No clinically significant variation was however recorded in any tube.
The results suggest that all types of tubes tested might be used interchangeably in term of risk of spurious hemolysis.
Clinical laboratory 01/2012; 58(11-12):1187-91. DOI:10.7754/Clin.Lab.2012.111225 · 1.13 Impact Factor
Available from: Maximilian Bielohuby
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ABSTRACT: Researchers analyse hormones to draw conclusions from changes in hormone concentrations observed under specific physiological conditions and to elucidate mechanisms underlying their biological variability. It is, however, frequently overlooked that also circumstances occurring after collection of biological samples can significantly affect the hormone concentrations measured, owing to analytical and pre-analytical variability. Whereas the awareness for such potential confounders is increasing in human laboratory medicine, there is sometimes limited consensus about the control of these factors in rodent studies. In this guide, we demonstrate how such factors can affect reliability and consequent interpretation of the data from immunoassay measurements of circulating metabolic hormones in rodent studies. We also compare the knowledge about such factors in rodent studies to recent recommendations established for biomarker studies in humans and give specific practical recommendations for the control of pre-analytical conditions in metabolic studies in rodents.
Molecular Metabolism 12/2012; 1(1-2):47-60. DOI:10.1016/j.molmet.2012.07.004
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