Ann Lab Med.  2020 Jan;40(1):68-71. 10.3343/alm.2020.40.1.68.

Evaluating Diagnostic Tests for Helicobacter pylori Infection Without a Reference Standard: Use of Latent Class Analysis

• Jekarl DW
• Choi H
• Kim JY
• Lee S
• Gweon TG
• Lee HK
• Kim Y

• KMID: 2457496
• DOI: http://doi.org/10.3343/alm.2020.40.1.68

Abstract

Evaluation of diagnostic tests requires reference standards, which are often unavailable. Latent class analysis (LCA) can be used to evaluate diagnostic tests without reference standards, using a combination of observed and estimated results. Conditionally independent diagnostic tests for Helicobacter pylori infection are required. We used LCA to construct a reference standard and evaluate the capability of non-invasive tests (stool antigen test and serum antibody test) to diagnose H. pylori infection compared with the conventional method, where histology is the reference standard. A total of 96 healthy subjects with endoscopy histology results were enrolled from January to July 2016. Sensitivity and specificity were determined for the LCA approach (i.e., using a combination of three tests as the reference standard) and the conventional method. When LCA was used, sensitivity and specificity were 83.8% and 99.4% for histology, 80.0% and 81.9% for the stool antigen test, and 63.6% and 89.3% for the serum antibody test, respectively. When the conventional method was used, sensitivity and specificity were 75.8% and 71.1% for the stool antigen test and 77.7% and 60.7% for the serum antibody test, respectively. LCA can be applied to evaluate diagnostic tests that lack a reference standard.

Keyword

Helicobacter pylori; Latent class analysis; Stool antigen test; Reference standard; Serum antibody test; Diagnosis

MeSH Terms

Diagnosis
Diagnostic Tests, Routine*
Endoscopy
Healthy Volunteers
Helicobacter pylori*
Helicobacter*
Methods
Sensitivity and Specificity

Reference

1. Kabir S. The role of interleukin-17 in the Helicobacter pylori induced infection and immunity. Helicobacter. 2011; 16:1–8.

2. Bjorkman DJ, Steenblik M. Best practice recommendations for diagnosis and management of Helicobacter pylori - synthesizing the guidelines. Curr Treat Options Gastroenterol. 2017; 15:648–659. PMID: 28932965.

3. Hooi JKY, Lai WY, Ng WK, Suen MMY, Underwood FE, Tanyingoh D, et al. Global prevalence of Helicobacter pylori infection: systematic review and meta-analysis. Gastroenterology. 2017; 153:420–429. PMID: 28456631.

4. Lee JH, Choi KD, Jung HY, Baik GH, Park JK, Kim SS, et al. Seroprevalence of Helicobacter pylori in Korea: a multicenter, nationwide study conducted in 2015 and 2016. Helicobacter. 2018; 23:e12463. PMID: 29345022.

5. Wang YK, Kuo FC, Liu CJ, Wu MC, Shih HY, Wang SS, et al. Diagnosis of Helicobacter pylori infection: current options and developments. World J Gastroenterol. 2015; 21:11221–11235. PMID: 26523098.

6. Jekarl DW, An YJ, Lee S, Lee J, Kim Y, Park YJ, et al. Evaluation of a newly developed rapid stool antigen test using an immunochromatographic assay to detect Helicobacter pylori. Jpn J Infect Dis. 2013; 66:60–64. PMID: 23429088.

7. Hirschl AM, Makristathis A. Non-invasive Helicobacter pylori diagnosis: stool or breath tests? Dig Liver Dis. 2005; 37:732–734. PMID: 15925556.

8. Malfertheiner P, Megraud F, O'Morain CA, Gisbert JP, Kuipers EJ, Axon AT, et al. Management of Helicobacter pylori infection–the Maastricht V/Florence consensus report. Gut. 2017; 66:6–30. PMID: 27707777.

9. Rutjes AWS, Reitsma JB, Coomarasamy A, Khan KS, Bossyut PMM. Evaluation of diagnostic test when there is no gold standard. A review of methods. Health Technol Assess. 2007; 11:1–86.

10. Rindskopf D, Rindskopf W. The value of latent class analysis in medical diagnosis. Stat Med. 1986; 5:21–27. PMID: 3961312.

11. Wiegand RE, Cooley G, Goodhew B, Banniettis N, Kohlhoff S, Gwyn S, et al. Latent class modeling to compare testing platforms for detection of antibodies against the Chlamydia trachomatis antigen Pgp3. Sci Rep. 2018; 8:4232. PMID: 29523810.

12. R Core Team. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing;Updated on Jun 2017. http://www.r-project.org.

13. Linzer DA, Lewis JB. poLCA: an R package for polytomous variable latent class analysis. J Stat Softw. 2011; 42:1–29.

14. Beath KJ, Heller GZ. Latent trajectory modelling of multivariate binary data. Stat Model. 2009; 9:199–213.

15. Kalali B, Formichella L, Gerhard M. Diagnosis of Helicobacter pylori: changes towards the future. Diseases. 2015; 3:122–135. PMID: 28943614.

16. Thijs JC, van Zwet AA, Thijs WJ, Oey HB, Karrenbeld A, Stellaard F, et al. Diagnostic tests for Helicobacter pylori: a prospective evaluation of their accuracy, without selecting a single test as the gold standard. Am J Gastroenterol. 1996; 91:2125–2129. PMID: 8855734.