Hemorrhage prediction models in surgical intensive care bedside monitoring data adds information to lab values
Hemorrhage is a frequent complication in surgery patients; its identification and management have received increasing attention as a target for quality improvement in patient care in the Intensive Care Unit (ICU). The purposes of this work were i) to find an early detection model for hemorrhage by exploring the range of data mining methods that are currently available, and ii) to compare prediction models utilizing continuously measured physiological data from bedside monitors to those using commonly obtained laboratory tests. We studied 3,766 patients admitted to the University of Virginia Health System Surgical Trauma Burn ICU. Hemorrhage was defined as three or more units of red blood cells transfused within 24 hours without red blood cell transfusion in the preceding 24 hours. 222 patients (5.9Percent) experienced a hemorrhage, and multivariate models based on vital signs and their trends showed good results (AUC=76.1Percent). The hematocrit, not surprisingly, had excellent performance (AUC=87.7Percent). Models that included both continuous monitoring and laboratory tests had the best performance (AUC=92.2Percent). The results point to a combined strategy of continuous monitoring and intermittent lab tests as a reasonable clinical approach to the early detection of hemorrhage in the surgical ICU.
bleeding, data mining, prediction model, intensive care unit, patient monitoring, blood transfusion