Clinical prediction rule for identifying older patients with toxigenic Clostridioides difficile at the time of hospital admission

Author:

Lee Ki-Byung1,Lee Mina2,Suh Jin Woong1,Yang Kyung Sook1,Chung Youseung1,Kim Jeong Yeon1,Kim Sun Bean1,Sohn Jang Wook1,Yoon Young Kyung1

Affiliation:

1. Korea University College of Medicine

2. Korea University Anam Hospital

Abstract

Abstract Background: This study aimed to develop and validate a clinical prediction rule to screen older patients at risk of being toxigenic Clostridioides difficile carriers at the time of hospital admission. Methods: This retrospective case-control study was performed at a university-affiliated hospital. Active surveillance using a real-time polymerase chain reaction (PCR) assay for the toxin genes of C. difficile was conducted upon admission to the Division of Infectious Diseases among older patients (≥65 years). This rule was drawn from a derivative cohort from October 2019 to April 2021 using a multivariable logistic regression model. Clinical predictability was evaluated in the validation cohort between May 2021 and October 2021. Results: Of 628 PCR screenings for toxigenic C. difficile infection, 101 (16.1%) yielded positive findings. In the derivation cohort, the formula was derived using significant predictors for toxigenic C. difficile infection at admissions, such as septic shock, connective tissue diseases, anemia, recent use of antibiotics, and recent use of proton-pump inhibitors, to establish clinical prediction rules. In the validation cohort, the sensitivity, specificity, and positive and negative predictive values of the prediction rule, based on a cut-off value of ≥0.45, were 78.3%, 70.8%, 29.5%, and 95.4%, respectively. Conclusions: This clinical prediction rule for identifying toxigenic C. difficile infection at admission would be helpful for the selective screening of high-risk groups. In order to be implemented in a clinical setting, more patients from other medical institutions need to be prospectively examined.

Publisher

Research Square Platform LLC

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