Using their machine-learning-based model, the investigators analyzed de-identified data - including individual patient demographics and medical history, details of their admission and daily hospitalization, and the likelihood of exposure to C. difficile - from the EHRs of almost 257,000 patients admitted to either MGH or to Michigan Medicine - U-M's academic medical center - over periods of two years and six years, respectively. The model generated daily risk scores for each individual patient that, when a set threshold is exceeded, classify patients as at high risk.
Overall, the models were highly successful at predicting which patients would ultimately be diagnosed with C. difficile. In half of those who were infected, accurate predictions could have been made at least five days before diagnostic samples were collected, which would allow highest-risk patients to be the focus of targeted antimicrobial interventions. If validated in prospective studies, the risk prediction score could guide early screening for C. difficile. For patients diagnosed earlier in the course of disease, initiation of treatment could limit the severity of the illness, and patients with confirmed C. difficile could be isolated and contact precautions instituted to prevent transmission to other patients.
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The research team has made the algorithm code freely available here for others to review and adapt for their individual institutions. Shenoy notes that facilities that explore applying similar algorithms to their own institutions will need to assemble the appropriate local subject-matter experts and validate the performance of the models in their institutions.
Study co-author Vincent Young, MD, PhD, the William Henry Fitzbutler Professor in the Department of Internal Medicine at U-M, adds, "This represents a potentially significant advance in our ability to identify and ultimately act to prevent infection with C. difficile. The ability to identify patients at greatest risk could allow us to focus expensive and potentially limited prevention methods on those who would gain the greatest potential benefit. I think that this project is a great example of a 'team science' approach to addressing complex biomedical questions to improve healthcare, which I expect to see more of as we enter the era of precision health."
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