In light of the evolving COVID-19 pandemic, the Association of American Medical Colleges (AAMC) and Liaison Committee on Medical Education (LCME) released a joint statement in March 2020 recommending an immediate suspension of medical student participation in direct patient contact. As graduating medical students who will soon begin residency, the authors fully support this recommendation. Though paid health care workers, like residents, nurses, and environmental services staff, are essential to the management of COVID-19 patients, medical students are not. Students’ continued involvement in direct patient care will contribute to SARS-CoV-2 exposures and transmissions and will waste already limited personal protective equipment. By decreasing nonessential personnel in health care settings, including medical students, medical schools will contribute to national and global efforts to “flatten the curve.” The authors also assert that medical schools are responsible for ensuring medical student safety. Without the protections provided to paid health care workers, students are uniquely disadvantaged within the medical hierarchy; these inequalities must be addressed before medical students are safely reintegrated into clinical roles. Although graduating medical students and institutional leadership may worry that suspending clinical rotations might prevent students from completing graduation requirements, the authors argue the ethical obligation to “flatten the curve” supersedes usual teaching responsibilities. Therefore, the authors request further guidance from the LCME and AAMC regarding curricular exemptions/alternatives and adjusted graduation timelines. The pool of graduating medical students affected by this pause in direct patient contact represents a powerful reserve, which may soon need to be used as the COVID-19 pandemic continues to challenge the U.S. health care infrastructure.

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Clostridioides difficile infection (CDI) can result in severe disease and death, with no accurate models that allow for early prediction of adverse outcomes. To address this need, we sought to develop serum-based biomarker models to predict CDI outcomes. We prospectively collected sera ≤48 h after diagnosis of CDI in two cohorts. Biomarkers were measured with a custom multiplex bead array assay. Patients were classified using IDSA severity criteria and the development of disease-related complications (DRCs), which were defined as ICU admission, colectomy, and/or death attributed to CDI. Unadjusted and adjusted models were built using logistic and elastic net modeling. The best model for severity included procalcitonin (PCT) and hepatocyte growth factor (HGF) with an area (AUC) under the receiver operating characteristic (ROC) curve of 0.74 (95% confidence interval, 0.67 to 0.81). The best model for 30-day mortality included interleukin-8 (IL-8), PCT, CXCL-5, IP-10, and IL-2Rα with an AUC of 0.89 (0.84 to 0.95). The best model for DRCs included IL-8, procalcitonin, HGF, and IL-2Rα with an AUC of 0.84 (0.73 to 0.94). To validate our models, we employed experimental infection of mice with C. difficile. Antibiotic-treated mice were challenged with C. difficile and a similar panel of serum biomarkers was measured. Applying each model to the mouse cohort of severe and nonsevere CDI revealed AUCs of 0.59 (0.44 to 0.74), 0.96 (0.90 to 1.0), and 0.89 (0.81 to 0.97). In both human and murine CDI, models based on serum biomarkers predicted adverse CDI outcomes. Our results support the use of serum-based biomarker panels to inform Clostridioides difficile infection treatment. IMPORTANCE Each year in the United States, Clostridioides difficile causes nearly 500,000 gastrointestinal infections that range from mild diarrhea to severe colitis and death. The ability to identify patients at increased risk for severe disease or mortality at the time of diagnosis of C. difficile infection (CDI) would allow clinicians to effectively allocate disease modifying therapies. In this study, we developed models consisting of only a small number of serum biomarkers that are capable of predicting both 30-day all-cause mortality and adverse outcomes of patients at time of CDI diagnosis. We were able to validate these models through experimental mouse infection. This provides evidence that the biomarkers reflect the underlying pathophysiology and that our mouse model of CDI reflects the pathogenesis of human infection. Predictive models can not only assist clinicians in identifying patients at risk for severe CDI but also be utilized for targeted enrollment in clinical trials aimed at reduction of adverse outcomes from severe CDI.