Abstract

Background: Various methods are employed to confirm Clostridioides difficile infection (CDI) diagnosis; however, current practices in Saudi Arabia remain undocumented which is a concern. This research assessment examines various critical elements of CDI testing in laboratories in Saudi Arabia, encompassing diagnostic techniques employed, testing protocols, specimen handling procedures, result reporting practices, and resource availability. This study aims to assess the current CDI testing practices in hospital microbiology laboratories across Saudi Arabia. Method: A cross-sectional study was conducted via an online survey to assess the CDI testing protocols in microbiology laboratories and the testing procedures employed by microbiologists throughout Saudi Arabia. The questionnaire examined various aspects of testing procedures, including laboratory protocols, testing schedules, and obstacles to conducting CDI tests. Cross-tabulation and descriptive analyses were carried out. Results: The survey elicited responses from 68 hospitals across 13 administrative regions of Saudi Arabia. 52.94% (n=36) came from small hospitals (≤200 beds), distributed across 8 regions, while the Western region contained the highest number of responding hospitals overall (44.12%, n=30). Positive CDI tests were reported more frequently by large hospitals (>200 beds) at 59.38% (n=19) versus 13.89% (n=5) of small hospitals (p<0.001). Among laboratories that test in-house, 22.73% (n=15) reported using nucleic acid amplification tests, whereas 25.76% (n=17) reported sending stool specimens to external laboratories and 11.76% (n=8) reported using multistep methods, but 25.76% (n=17) were unsure of the used tests. Limited institutional budget for CDI testing kits was the most commonly reported barrier by laboratory microbiologists. Conclusion: While CDI testing practices in Saudi Arabian microbiology laboratories generally align with international guidelines, this survey identifies opportunities for improvement through enhanced education, implementation of evidence-based testing algorithms, and addressing resource limitations.