A passive monitoring tool using hospital administrative data enables earlier specific detection of healthcare-acquired infections

Journal article

Background: Healthcare-associated infections impose a signi cant burden on the health care system. Current methods for detecting these infections are constrained by combinations of high cost, long processing times, and imperfect accuracy, reducing their effectiveness. Methods: We examine whether the quantity of time a patient spends in a ward with other patients clinically-suspected of infection, which we call co-presence, can be used as a tool to predict subsequent healthcare-associated infection. Compared to contact tracing, this leverages passively-collected electronic data rather than manually-collected data, allowing for improved monitoring. We abstracted all 133,304 inpatient records between 2011 and 2015 from a healthcare system in the UK. We calculate the AUROC for each of ve pathogens based on co-presence time, the sensitivity and speci city for the test, and how much earlier co-presence would have predicted infection for the true positives. Findings: Across the ve pathogens, AUROC ranged from 0.92 to 0.99, and was 0.52 for the negative control. Optimal cut-points of co-presence ranged from 25 to 59 hours, and would have led to detection of true positives up to an average of one day earlier. Interpretation: These findings show that co-presence time would help predict healthcare-acquired infection, and would do so earlier than the current standard of care. Using this measure prospectively in hospitals based on real-time data could limit the consequences of infection, both by being able to treat individual infected patients earlier, and by preventing potential secondary infections stemming from the original infected patient. Funding: This research was funded via NHGRI, NIH (ZIA HG200335) and the Oxford Martin School, University of Oxford (LC1213-006).

Authors: Rewley, J; Koehly, L; Marcum, CS; Reed-Tsochas, F

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Elsevier
• Journal: Journal of Hospital Infection
• Volume: 106
• Issue: 3
• Pages: 562-569
• Publication date: 2020-08-01
• Acceptance date: 2020-07-27
• DOI: 10.1016/j.jhin.2020.07.031
• ISSN: 0195-6701
• Language: English
• Keywords: big data; co-presence; FFR; electronic medical records; contact tracing; proximity