Literature DB >> 32519216

Evaluation of RIDASCREEN® and RIDA®QUICK Helicobacter kits for Helicobacter pylori detection in stools.

Lucie Bénéjat1, Alice Buissonnière1, Astrid Ducournau1, Francis Mégraud1,2, Emilie Bessède1,2, Philippe Lehours3,4.   

Abstract

The diagnosis of Helicobacter pylori infection can be made by using noninvasive tests. The detection of bacterial antigens in stool samples is a technique proposed by some suppliers. The objective of this study was to evaluate retrospectively the performances of the commercially available RIDA®QUICK Helicobacter and RIDASCREEN® Helicobacter (R-Biopharm) kits in detecting H. pylori antigens in stool samples. A collection of 132 stools was used in this study: 94 stools obtained from H. pylori-negative patients and 38 stools from H. pylori-positive patients. The performances (sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV)) were evaluated for the RIDA®QUICK Helicobacter and RIDASCREEN® Helicobacter kits in comparison with real-time PCR results performed on gastric biopsies as well as culture. Discordant results, with respect to H. pylori status, were checked on the same day as the test by repeating the procedure. All of the readings concerning the RIDA®QUICK Helicobacter tests were concordant between 3 users, i.e., 94/94 negative tests and 34/38 positive tests. RIDASCREEN® Helicobacter tests were negative for all 94 H. pylori-negative samples and positive for 35/38 positive stools. Reading of the RIDA®QUICK Helicobacter tests was not a problem in routine practice. The RIDA®QUICK Helicobacter and RIDASCREEN® Helicobacter kits show good performances and can be included in the armamentarium of diagnostic tests for H. pylori infection.

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Keywords:  Antigen; Diagnosis; H. pylori; Noninvasive detection; Stools

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Year:  2020        PMID: 32519216     DOI: 10.1007/s10096-020-03943-3

Source DB:  PubMed          Journal:  Eur J Clin Microbiol Infect Dis        ISSN: 0934-9723            Impact factor:   3.267


  1 in total

1.  A Novel Computational Model for Detecting the Severity of Inflammation in Confirmed COVID-19 Patients Using Chest X-ray Images.

Authors:  Mohammed S Alqahtani; Mohamed Abbas; Ali Alqahtani; Mohammad Alshahrani; Abdulhadi Alkulib; Magbool Alelyani; Awad Almarhaby; Abdullah Alsabaani
Journal:  Diagnostics (Basel)       Date:  2021-05-10
  1 in total

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