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

ArticleinClinical laboratory 53(3-4):211-5 · February 2007with20 Reads
Source: PubMed
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.
    • "The ISO 15189:2012 requirements cover all aspects of the laboratory activities, including the laboratory information system (LIS) (Pouliakis et al., 2014aPouliakis et al., , 2014b Vacata et al., 2007; Westbrook et al., 2008). ISO 15189:2012 suggests specific measures for the protection of laboratory electronic records (Pouliakis et al., 2014aPouliakis et al., , 2014b Vacata et al., 2007; Westbrook et al., 2008). However, in most cases, this LIS is basically a standalone client that only operates on laboratory computer units using the power of a central hosting server nearby. "
    Article · Jul 2016
    • "This example from Alzheimer's research illustrates that in studies involving humans, the situation is not ideal, but the awareness has tremendously increased over the past years. This is also documented by an increasing number of published studies, reviews and guidelines providing support how to minimize preanalytical variability505152535455 and giving practical recommendations concerning biobanking and long-term storage [28,565758. Although many issues regarding pre-analytical variability in humans are still a matter of debate or uncertainty, there is no doubt that the situation is much better than the situation in rodent studies. "
    [Show abstract] [Hide abstract] 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.
    Full-text · Article · Dec 2012
    Maximilian BielohubySarah PoppSarah PoppMartin BidlingmaierMartin Bidlingmaier
    • "As for any other type of medical errors, implementation of a total quality management system is the most effective road to improvement [8,16,17], 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. "
    [Show abstract] [Hide abstract] 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.
    Full-text · Article · Apr 2009
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