Prospective assessment of two-stage testing for Clostridium difficile

Area of Science:

• Clinical Microbiology
• Infectious Diseases
• Diagnostic Laboratory Science

Background:

• Standard immunoassays for Clostridium difficile infection (CDI) exhibit limitations, particularly concerning specificity, leading to a high rate of false-positive results.
• Accurate and timely diagnosis of CDI is crucial for effective patient management and infection control.

Purpose of the Study:

• To evaluate a two-stage diagnostic algorithm for Clostridium difficile infection (CDI) to improve the accuracy and efficiency of laboratory testing.
• To assess the positive predictive value of a two-stage testing strategy involving enzyme immunoassay (EIA) followed by reference methods.

Main Methods:

• A two-stage diagnostic approach was implemented: initial testing of unformed stool samples with Meridian Premier enzyme immunoassay (EIA).
• Positive EIA results were confirmed using reference methods, including toxigenic culture and cell cytotoxicity assay.
• A retrospective review of 3643 stool specimens was conducted to analyze the performance of the two-stage algorithm.

Main Results:

• Of 3643 specimens, 158 (4.3%) were provisionally positive by EIA. Of these, 119 were confirmed positive by reference methods, yielding a positive predictive value of 75%.
• Analysis of optical density values suggested that implementing a second cut-off value could further enhance diagnostic precision.
• The proposed two-cut-off algorithm would categorize results as 'positive', 'negative', or 'indeterminate, requiring confirmatory testing'.

Conclusions:

• The implemented two-stage testing algorithm offers a simple, cost-effective method to immediately improve the diagnostic accuracy for Clostridium difficile infection (CDI).
• Further research is urgently needed to develop and validate improved laboratory diagnostic methods for CDI.
• The findings support the potential of a tiered diagnostic approach with optimized cut-off values to enhance CDI detection and reduce false positives.