Predicting out of intensive care unit cardiopulmonary arrest or death using electronic medical record data
1 School of Pharmacy – Department of Pharmacy Practice, Texas Tech University Health Sciences Center, 5920 Forest Park Rd, Dallas, TX 75235, USA
2 Parkland Center for Clinical Innovation, 6300 Harry Hines Blvd, Suite 265, Mailstop 83020, Dallas, TX 75235, USA
3 Department of Clinical Sciences – Biostatistics Division, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, USA
4 Department of Internal Medicine – Division of General Internal Medicine, Department of Clinical Sciences – Division of Outcomes and Health Services Research, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, USA
5 Department of Internal Medicine – Division of Respiratory and Critical Care Medicine, Parkland Health and Hospital System – Division of Medical Affairs, University of Texas Southwestern Medical Center, 5201 Harry Hines Blvd, Dallas, TX 75235, USA
6 Department of Internal Medicine – Division of Respiratory and Critical Care Medicine, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, USA
Citation and License
BMC Medical Informatics and Decision Making 2013, 13:28 doi:10.1186/1472-6947-13-28Published: 27 February 2013
Accurate, timely and automated identification of patients at high risk for severe clinical deterioration using readily available clinical information in the electronic medical record (EMR) could inform health systems to target scarce resources and save lives.
We identified 7,466 patients admitted to a large, public, urban academic hospital between May 2009 and March 2010. An automated clinical prediction model for out of intensive care unit (ICU) cardiopulmonary arrest and unexpected death was created in the derivation sample (50% randomly selected from total cohort) using multivariable logistic regression. The automated model was then validated in the remaining 50% from the total cohort (validation sample). The primary outcome was a composite of resuscitation events, and death (RED). RED included cardiopulmonary arrest, acute respiratory compromise and unexpected death. Predictors were measured using data from the previous 24 hours. Candidate variables included vital signs, laboratory data, physician orders, medications, floor assignment, and the Modified Early Warning Score (MEWS), among other treatment variables.
RED rates were 1.2% of patient-days for the total cohort. Fourteen variables were independent predictors of RED and included age, oxygenation, diastolic blood pressure, arterial blood gas and laboratory values, emergent orders, and assignment to a high risk floor. The automated model had excellent discrimination (c-statistic=0.85) and calibration and was more sensitive (51.6% and 42.2%) and specific (94.3% and 91.3%) than the MEWS alone. The automated model predicted RED 15.9 hours before they occurred and earlier than Rapid Response Team (RRT) activation (5.7 hours prior to an event, p=0.003)
An automated model harnessing EMR data offers great potential for identifying RED and was superior to both a prior risk model and the human judgment-driven RRT.