Clostridioides difficile infection surveillance in intensive care units and oncology wards using machine learning

Abstract

Objective: Screening individuals admitted to the hospital for Clostridioides difficile presents opportunities to limit transmission and hospital-onset C. difficile infection (HO-CDI). However, detection from rectal swabs is resource intensive. In contrast, machine learning (ML) models may accurately assess patient risk without significant resource usage. In this study, we compared the effectiveness of swab surveillance to daily risk estimates produced by an ML model to identify patients who will likely develop HO-CDI in the intensive care unit (ICU) setting. Design: A prospective cohort study was conducted with patient carriage of toxigenic C. difficile identified by rectal swabs analyzed by anaerobic culture and polymerase chain reaction (PCR). A previously validated ML model using electronic health record data generated daily risk of HO-CDI for every patient. Swab results and risk predictions were compared to the eventual HO-CDI status. Patients: Adult inpatient admissions taking place in University of Michigan Hospitals’ medical and surgical intensive care units and oncology wards between June 6th and October 8th, 2020. Results: In total, 2,979 admissions, representing 2,044 patients, were observed over the course of the study period, with 39 admissions developing HO-CDIs. Swab surveillance identified 9 true-positive and 87 false-positive HO-CDIs. The ML model identified 9 true-positive and 226 false-positive HO-CDIs; 8 of the true-positives identified by the model differed from those identified by the swab surveillance. Conclusion: With limited resources, an ML model identified the same number of HO-CDI admissions as swab-based surveillance, though it generated more false-positives. The patients identified by the ML model were not yet colonized with C. difficile. Additionally, the ML model identifies at-risk admissions before disease onset, providing opportunities for prevention.

Screening for Asymptomatic Clostridioides difficile Carriage Among Hospitalized Patients: A Narrative Review

“…The model identified different patients from those identified by screening who later developed CDI. This suggests a potential advantage of a machine-learning model because screening cannot identify at-risk patients who are not already colonized [ 41 ].…”