Abstract [from journal]
Background: Healthcare-associated infections impose a significant 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 five pathogens based on co-presence time, the sensitivity and specificity for the test, and how much earlier co-presence would have predicted infection for the true positives.
Findings: Across the five 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.