Related Experiment Video
Updated: Feb 12, 2026
Cefoperazone-treated Mouse Model of Clinically-relevant Clostridium difficile Strain R20291
Published on: December 10, 2016
Poor yield of Clostridium difficile testing algorithms using glutamate dehydrogenase antigen and C. difficile toxin
Emily J Gomez1, Sandra Montgomery2, Kevin Alby3
1Department of Pathology and Laboratory Medicine, The Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA, USA, 19103; Author's location has changed to Clinical Laboratory, Abington-Jefferson Health, 1200 Old York Rd, Abington, PA, USA, 19001.
Insights
Algorithmic diagnosis of Clostridium difficile infection (CDI) showed lower sensitivity in children compared to molecular tests. This finding may be linked to the low prevalence of the NAP-1/ribotype 027 strain.
Area of Science:
- Medical Microbiology
- Pediatric Infectious Diseases
- Diagnostic Accuracy Studies
Background:
- Clostridium difficile infection (CDI) is a significant cause of healthcare-associated diarrhea in children.
- Accurate and timely diagnosis is crucial for effective patient management and infection control.
- Molecular diagnostic methods offer rapid detection but their performance relative to algorithmic approaches in pediatric populations requires evaluation.
Purpose of the Study:
- To compare the diagnostic performance of an algorithmic CDI testing strategy with four different molecular assays in a pediatric cohort.
- To assess the sensitivity, specificity, positive predictive value, and negative predictive value of each diagnostic method.
Main Methods:
- Stool samples from pediatric patients (1-18 years) were tested using a diagnostic algorithm (C. Diff Quik Chek Complete reflexed to illumigene C. difficile) and four molecular tests: AmpliVue C. difficile (ACD), Lyra Direct C. difficile (Lyra), BD MAX C diff (BDM), and Xpert C. difficile (XCD).
- The gold standard for diagnosis was defined as positivity by two distinct testing methods.
- Prevalence of the NAP-1/ribotype 027 strain was determined.
Main Results:
- The algorithmic approach demonstrated 85% sensitivity, 99% specificity, 93% PPV, and 97% NPV.
- Molecular tests showed high performance: ACD (94% sensitivity, 99% specificity), Lyra (88% sensitivity, 99% specificity), BDM (94% sensitivity, 100% specificity), and XCD (94% sensitivity, 99% specificity).
- The prevalence of ribotype 027 was 9.6%.
Conclusions:
- Algorithmic CDI detection in children may exhibit lower sensitivity compared to standalone molecular assays.
- The observed lower sensitivity of the algorithm might be associated with the limited prevalence of the NAP-1/ribotype 027 strain in the study population.
- Molecular tests generally provide high accuracy for CDI diagnosis in pediatric patients.
Abstract:
We compared the performance of algorithmic Clostridium difficile infection (CDI) diagnosis with four molecular tests in children. Stool samples in patients 1-18 years old were tested with an algorithm (C. Diff Quik Chek Complete (QCC) reflexed to illumigene C. difficile); AmpliVue C. difficile (ACD); Lyra Direct C. difficile (Lyra); BD MAX C diff (BDM); and Xpert C. difficile (XCD). The gold standard was positivity by two tests. Sensitivity, specificity, positive predictive value, and negative predictive value were 85%, 99%, 93%, 97% for the algorithm; 21%, 99%, 78%, 87% for QCC's toxin component; 94%, 99%, 94%, 99% for ACD; 88%, 99%, 94%, 98% for Lyra; 94%, 100%, 100%, 99% for BDM, and 94%, 99%, 94% and 99% for XCD. 9.6% of samples were ribotype 027. Algorithms may detect CDI with lower sensitivity compared to molecular methods in children. This may be related to low prevalence of NAP-1/ribotype 027.
More Related Videos
Related Concept Videos
02:07Conservation of Declining Populations
01:11Testing a Claim about Mean: Known Population SD
Estimating a population mean requires the samples to be distributed normally. The data should be collected from the randomly selected samples having no sampling bias. The sample size needed to be higher than 30, and most importantly, the population standard deviation should be already known.
In most realistic situations, the population standard deviation is often unknown, but in rare circumstances, when it...
01:08Prevalence and Incidence
Prevalence indicates the proportion of individuals in a population who have a specific disease or health...
01:24Testing a Claim about Population Proportion
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
01:21Testing a Claim about Mean: Unknown Population SD
Estimating a population mean requires the samples to be approximately normally distributed. The data should be collected from the randomly selected samples having no sampling bias. There is no specific requirement for sample size. But if the sample size is less than 30, and we don't know the population standard deviation, a different approach is used;...
01:19Magnetic Declination