Can prediction scores be used to identify patients at risk of clostridioides difficile infection?

Krishna Rao, Erik R. Dubberke

Research output: Contribution to journalReview articlepeer-review

1 Scopus citations


Purpose of review To describe the current state of literature on modeling risk of incident and recurrent Clostridioides difficile infection (iCDI and rCDI), to underscore limitations, and to propose a path forward for future research.Recent findingsThere are many published risk factors and models for both iCDI and rCDI. The approaches include scores with a limited list of variables designed to be used at the bedside, but more recently have also included automated tools that take advantage of the entire electronic health record. Recent attempts to externally validate scores have met with mixed success.SummaryFor iCDI, the performance largely hinges on the incidence, which even for hospitalized patients can be low (often <1%). Most scores fail to achieve high accuracy and/or are not externally validated. A challenge in predicting rCDI is the significant overlap with risk factors for iCDI, reducing the discriminatory ability of models. Automated electronic health record-based tools show promise but portability to other centers is challenging. Future studies should include external validation and consider biomarkers to augment performance.

Original languageEnglish
Pages (from-to)7-14
Number of pages8
JournalCurrent opinion in gastroenterology
Issue number1
StatePublished - Jan 1 2022


  • Clostridioides difficile infection
  • Healthcare-associated infections
  • Machine learning
  • Predictive modeling


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