Concordance of Helicobacter pylori Detection Methods in Symptomatic Children and Adolescents

Camila Cabrera1, Yanira Campusano1, Joaquín Torres1

  • 1Microbiology and Mycology Program, Institute of Biomedical Sciences, Faculty of Medicine, University of Chile, Santiago 8380453, Chile.

Microorganisms
|March 27, 2025
PubMed

Insights

Histology with Giemsa staining and ureA RT-PCR offer the highest detection rate for Helicobacter pylori infection. This combination provides the strongest diagnostic agreement, improving accuracy in identifying this common bacterial infection.

Area of Science:

  • Microbiology
  • Infectious Diseases
  • Diagnostic Medicine

Background:

  • Helicobacter pylori is the most common chronic bacterial infection worldwide, linked to gastritis, ulcers, and gastric cancer.
  • Accurate diagnosis is challenging due to culture difficulties, often requiring multiple biopsy-based methods.
  • Histology with Giemsa staining is a common, cost-effective clinical standard for H. pylori detection.

Purpose of the Study:

  • To evaluate the diagnostic concordance of various methods against histology with Giemsa staining.
  • To identify the most accurate non-culture diagnostic method for H. pylori.

Main Methods:

  • Compared histology with Giemsa staining to rapid urease test (RUT), ureA RT-PCR, 16S sequencing, and serum IgG.
  • Calculated positive percent agreement (PPA), negative percent agreement (NPA), and concordance kappa index.

Main Results:

  • 120 patients were analyzed; 41 tested positive by Giemsa staining.
  • UreA RT-PCR demonstrated superior performance (PPA=94.7%, NPA=98.6%, kappa=0.939).
  • RUT (PPA=65.9%) and serology (PPA=53.7%) showed lower agreement.

Conclusions:

  • Combining histology with Giemsa staining and ureA RT-PCR yields the highest detection rate.
  • This combination offers the strongest diagnostic agreement for H. pylori.
Abstract

Related Concept Videos

Kendall's Coefficient of Concordance01:20

Kendall's Coefficient of Concordance

Kendall's Coefficient of Concordance (W), also known as Kendall's W, is a non-parametric statistical measure used to assess the agreement or concordance between multiple raters or judges when they rank a set of items. It is often used when you have ordinal data (ranks) and you want to see if there is consistency or consensus among the raters. It is widely applied in research areas such as psychology, medicine, and social sciences, where multiple judges are asked to rank or rate subjects...
204
Conformity01:20

Conformity

Conformity is the change in a person’s behavior to go along with the group, even if that person does not agree with the group.
44.8K
Convergent Evolution01:54

Convergent Evolution

Evolution shapes the features of organisms over time, ensuring that they are suited for the environments in which they live. Sometimes, selection pressure leads to the rise of similar but unrelated adaptations in organisms with no recent common ancestors, a process known as convergent evolution.
27.3K
Multiple Comparison Tests01:13

Multiple Comparison Tests

Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
3.8K
Correlations02:20

Correlations

Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
32.2K
Correlation01:09

Correlation

In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
11.5K