Published in:
01-04-2017 | Original Article
Clostridium difficile Infection and Patient-Specific Antimicrobial Resistance Testing Reveals a High Metronidazole Resistance Rate
Published in: Digestive Diseases and Sciences | Issue 4/2017
Login to get accessAbstract
Background
Clostridium difficile (CD) infection (CDI) causes marked morbidity and mortality, accounting for large healthcare expenditures annually. Current CDI treatment guidelines focus on clinical markers of patient severity to determine the preferred antibiotic regimen of metronidazole versus vancomycin. The antimicrobial resistance patterns for patients with CD are currently unknown.
Aim
The aim of this study was to define the antimicrobial resistance patterns for CD.
Methods
This study included all patients with stools sent for CD testing to a private laboratory (DRG Laboratory, Alpharetta, Georgia) in a 6-month period from across the USA. Patient data was de-identified, with only age, gender, and zip-code available per laboratory protocol. All samples underwent PCR testing followed by hybridization for CD toxin regions A and B. Only patients with CD-positive PCR were analyzed. Antimicrobial resistance testing using stool genomic DNA evaluated presence of imidazole- and vancomycin-resistant genes using multiplex PCR gene detection.
Results
Of 2743, 288 (10.5%) stool samples were positive for CD. Six were excluded per protocol. Of 282, 193 (69.4%) were women, and average age was 49.4 ± 18.7 years. Of 282, 62 were PCR positive for toxins A and B, 160 for toxin A positive alone, and 60 for toxin B positive alone. Antimicrobial resistance testing revealed 134/282 (47.5%) patients resistant to imidazole, 17 (6.1%) resistant to vancomycin, and 9 (3.2%) resistant to imidazole and vancomycin.
Conclusions
CD-positive patients with presence of imidazole-resistant genes from stool DNA extract was a common phenomenon, while vancomycin resistance was uncommon. Similar to treatment of other infections, antimicrobial resistance testing should play a role in CDI clinical decision-making algorithms to enable more expedited and cost-effective delivery of patient care.