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

Abstract

Purpose of reviewTo 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.

Diagnostic prediction models to identify patients at risk for healthcare-facility–onset Clostridioides difficile: A systematic review of methodology and reporting

“…A narrative review published in 2021 discussed some models predicting the risk of incident or recurrent CDI. 26 The researchers emphasized the need for developing a validated incident CDI model and highlighted the key challenges to model portability. Our report builds on these findings by systematically evaluating and reporting the risk of bias in each study.…”