Dynamic data in the ED predict requirement for ICU transfer following acute care admission

Misidentification of illness severity may lead to patients being admitted to a ward bed then unexpectedly transferring to an ICU as their condition deteriorates. Our objective was to develop a predictive analytic tool to identify emergency department (ED) patients that required upgrade to an intensive or intermediate care unit (ICU or IMU) within 24 h after being admitted to an acute care floor. We conducted a single-center retrospective cohort study to identify ED patients that were admitted to an acute care unit and identified cases where the patient was upgraded to ICU or IMU within 24 h. We used data available at the time of admission to build a logistic regression model that predicts early ICU transfer. We found 42,332 patients admitted between January 2012 and December 2016. There were 496 cases (1.2%) of early ICU transfer. Case patients had 18.0-fold higher mortality (11.1% vs. 0.6%, p < 0.001) and 3.4 days longer hospital stays (5.9 vs. 2.5, p < 0.001) than those without an early transfer. Our predictive analytic model had a cross-validated area under the receiver operating characteristic of 0.70 (95% CI 0.67-0.72) and identified 10% of early ICU transfers with an alert rate of 1.6 per week (162.2 acute care admits per week, 1.9 early ICU transfers). Predictive analytic monitoring based on data available in the emergency department can identify patients that will require upgrade to ICU or IMU if admitted to acute care. Incorporating this tool into ED practice may draw attention to high-risk patients before acute care admit and allow early intervention.