ReviewBiochemia medica2017
Preanalytical investigations of phlebotomy: methodological aspects, pitfalls and recommendations.
Review in Biochemia medica, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Blood alcohol concentration in the clinical laboratory: a narrative review of the preanalytical phase in diagnostic and forensic testing.Biochemia medica · 2024Review
- Coffee intake one hour prior to phlebotomy produces no clinically significant changes in routine biochemical test results.Biochemia medica · 2023Article
- Article
- Fasting conditions: Influence of water intake on clinical chemistry analytes.Biochemia medica · 2018Article
- Case report of unexplained hypocalcaemia in a slightly haemolysed sample.Biochemia medica · 2017Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Phlebotomy is often addressed as a crucial process in the pre-analytical phase, in which a large part of laboratory errors take place, but to date there is not yet a consolidated methodological paradigm. Seeking literature, we found 36 suitable investigations issued between 1996 and 2016 (April) dealing with the investigation of pre-analytical factors related to phlebotomy. We found that the largest part of studies had a cohort of healthy volunteers (22/36) or outpatients (11/36), with the former group showing a significantly smaller median sample size (N = 20, IQR: 17.5-30 and N = 88, IQR: 54.5-220.5 respectively, P < 0.001). Moreover, the largest part investigated one pre-analytical factor (26/36) and regarded more than one laboratory test (29/36), and authors preferably used paired Student's t-test (17/36) or Wilcoxon's test (11/36), but calibration (i.e. sample size calculation for a detectable effect) was addressed only in one manuscript. The Bland-Altman plot was often the preferred method used to estimate bias (12/36), as well as the Passing-Bablok regression for agreement (8/36). However, often papers did assess neither bias (12/36) nor agreement (24/36). Clinical significance of bias was preferably assessed comparing to a database value (16/36), and it resulted uncorrelated with the size of the effect produced by the factor (P = 0.142). However, the median effect size (ES) resulted significantly larger if the associated factor was clinically significant instead of non-significant (ES = 1.140, IQR: 0.815-1.700 and ES = 0.349, IQR: 0.228-0.531 respectively, P < 0.001). On these evidences, we discussed some recommendations for improving methodological consistency, delivering reliable results, as well as ensuring accessibility to practical evidences.
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