ArticleJournal of inherited metabolic disease2008
Qualitative urinary organic acid analysis: methodological approaches and performance.
Article in Journal of inherited metabolic disease, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.
- Laboratory analysis of organic acids, 2018 update: a technical standard of the American College of Medical Genetics and Genomics (ACMG).Genetics in medicine : official journal of the American College of Medical Genetics · 2018Guideline
- Chromatomass-Spectrometric Method for the Quantitative Determination of Amino- and Carboxylic Acids in Biological Samples.Metabolites · 2022Article
- The role of ERNDIM diagnostic proficiency schemes in improving the quality of diagnostic testing for inherited metabolic diseases.Journal of inherited metabolic disease · 2022Review
- Impacts of dietary exposure to pesticides on faecal microbiome metabolism in adult twins.Environmental health : a global access science source · 2022Article
- Qualitative urinary organic acid analysis: 10 years of quality assurance.Journal of inherited metabolic disease · 2016Article
- Quality of analytical performance in inherited metabolic disorders: the role of ERNDIM.Journal of inherited metabolic disease · 2008Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A programme for proficiency testing of biochemical genetics laboratories undertaking urinary qualitative organic acid analysis and its results for 50 samples examined for factors contributing to poor performance are described. Urine samples from patients in whom inherited metabolic disorders have been confirmed as well as control urines were circulated to participants and the results from 94 laboratories were evaluated. Laboratories showed variability both in terms of their individual performance and on a disease-specific basis. In general, conditions including methylmalonic aciduria, propionic aciduria, isovaleric aciduria, mevalonic aciduria, Canavan disease and 3-methylcrotonyl-CoA carboxylase were readily identified. Detection was poorer for other diseases such as glutaric aciduria type II, glyceric aciduria and, in one sample, 3-methylcrotonyl-CoA carboxylase deficiency. To identify the factors that allow some laboratories to perform well on a consistent basis while others perform badly, we devised a questionnaire and compared the responses with the results for performance in the scheme. A trend towards better performance could be demonstrated for those laboratories that regularly use internal quality control (QC) samples in their sample preparation (p = 0.079) and those that participate in further external quality assurance (EQA) schemes (p = 0,040). Clinicians who depend upon these diagnostic services to identify patients with these defects and the laboratories that provide them should be aware of the potential for missed diagnoses and the factors that may lead to improved performance.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.