Volume 10, Issue 3 (12-2018)
2018, 10(3): 67-72 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Mokhtariye A, Varasteh A, Marzban S, Keyfi F. The Effects of Pre-analytical Variables on the Diagnosis of Inborn Errors of Metabolism; A Review Study. North Khorasan University of Medical Sciences 2018; 10 (3) :67-72
URL: http://journal.nkums.ac.ir/article-1-1593-en.html
URL: http://journal.nkums.ac.ir/article-1-1593-en.html
1- Department of Clinical Biochemistry, Medical School, Kermanshah University of Medical Sciences, Kermanshah, Iran
2- Department of Medical Laboratory Sciences, Varastegan Institute for Medical Sciences, Mashhad, Iran
3- Division of Metabolic Disorders, Pardis clinical and Genetic Laboratory, Mashhad, Iran
4- Department of Medical Laboratory Sciences, Varastegan Institute for Medical Sciences, Mashhad, Iran ,keifyf@varastegan.ac.ir
2- Department of Medical Laboratory Sciences, Varastegan Institute for Medical Sciences, Mashhad, Iran
3- Division of Metabolic Disorders, Pardis clinical and Genetic Laboratory, Mashhad, Iran
4- Department of Medical Laboratory Sciences, Varastegan Institute for Medical Sciences, Mashhad, Iran ,
Abstract: (3995 Views)
Introduction: Quantitative metabolite profiling in biological samples has the potential to reflect physiological status and to identify disease associated disorders in metabolic pathways. This approach is hindered by a wide range of pre-analytical variables. Pre-analytical variables account for 32-75% of laboratory errors, which includes the time from when the test is ordered by the physician until the sample is ready for analysis. The aims of this study was to evaluate the pre-analytical errors in diagnostic methods of inherited metabolic disorders.
Methods: In this study, 35 articles with the key words Pre-analytical Errors, Metabolic disorders, Samples, Mass Spectrometry, and Gas Chromatography were searched in PubMed, Google Scholar, and Scopus databases from the year 1987 to 2018.
Results: There are some less controllable sources of pre-analytical errors that can strongly influence the reliability of test results in inborn errors of metabolism. These primarily include specimen collection, handling samples and physiological variables such as the effect of lifestyle, diet, stress, age, gender, positional effects, and endogenous variables such as drugs and circulating antibodies.
Conclusions: As pre-analytical sources of variation can produce unpredictable and unfavorable impacts on the wellbeing of patients, a reduction in laboratory testing errors play a significant role in assessing and quality improvements. In this respect, good quality specimens, resulting from proper training and knowledge about effective factors on laboratory results, are essential for minimizing errors and optimizing resource utilization and whole patient management process to assure accurate diagnosis.
Methods: In this study, 35 articles with the key words Pre-analytical Errors, Metabolic disorders, Samples, Mass Spectrometry, and Gas Chromatography were searched in PubMed, Google Scholar, and Scopus databases from the year 1987 to 2018.
Results: There are some less controllable sources of pre-analytical errors that can strongly influence the reliability of test results in inborn errors of metabolism. These primarily include specimen collection, handling samples and physiological variables such as the effect of lifestyle, diet, stress, age, gender, positional effects, and endogenous variables such as drugs and circulating antibodies.
Conclusions: As pre-analytical sources of variation can produce unpredictable and unfavorable impacts on the wellbeing of patients, a reduction in laboratory testing errors play a significant role in assessing and quality improvements. In this respect, good quality specimens, resulting from proper training and knowledge about effective factors on laboratory results, are essential for minimizing errors and optimizing resource utilization and whole patient management process to assure accurate diagnosis.
Type of Study: Review Article |
Subject:
Clinical
Received: 2018/01/13 | Accepted: 2018/05/8 | Published: 2018/12/22
Received: 2018/01/13 | Accepted: 2018/05/8 | Published: 2018/12/22
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
