对住院患者进行Clostridioides difficile筛查可以减少传播和医院内感染（HO-CDI）的机会。然而，直肠拭子检测是资源密集型的。相比之下，机器学习（ML）模型可以在不需要大量资源的情况下准确评估患者风险。在本研究中，我们比较了拭子监测和由ML模型生成的每日风险评估的效果，以识别在重症监护病房（ICU）中可能发展HO-CDI的患者。进行了一项前瞻性队列研究，通过厌氧培养和聚合酶链反应（PCR）分析直肠拭子识别了毒素原性C.difficile的患者携带情况。使用电子病历数据生成已经验证过的ML模型，为每个患者每天评估HO-CDI的风险。将拭子结果和风险预测与最终的HO-CDI状态进行比较。研究期间观察了发生在密歇根大学医院的医学和外科重症监护病房以及肿瘤科病房的成年住院患者共2,979名，代表2,044名患者，其中39个住院患者发展为HO-CDI。拭子监测确定了9个真正阳性和87个假阳性HO-CDI。ML模型确定了9个真正阳性和226个假阳性HO-CDI。由该模型确定的8个真阳性不同于由拭子监测确定的真阳性。在资源有限的情况下，ML模型确定了与基于拭子监测相同数量的HO-CDI住院患者，尽管它产生了更多的假阳性。被ML模型确定的患者尚未被C.difficle菌群感染。此外，该ML模型可以在疾病发生之前确定高危住院患者，提供了预防的机会。

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.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.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.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.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.