TY - JOUR
T1 - Predicting out of intensive care unit cardiopulmonary arrest or death using electronic medical record data
AU - Alvarez, Carlos A.
AU - Clark, Christopher A.
AU - Zhang, Song
AU - Halm, Ethan A.
AU - Shannon, John J.
AU - Girod, Carlos E.
AU - Cooper, Lauren
AU - Amarasingham, Ruben
N1 - Funding Information:
This work was funded by the Parkland Health & Hospital System. RA and CC were additionally supported by the Commonwealth Fund Grant Number 20100323, titled “Harnessing EMR Data to Reduce Readmissions: Developing and Validating a Real Time Predictive Model.” CAA was supported in part by UT-STAR, NIH/NCATS Grant Number KL2 RR024983. The content is solely the responsibility of the authors and does not necessarily represent the official views of UT-STAR, UT Southwestern Medical Center and its affiliated academic and health care centers, the National Center for Advancing Translational Sciences, or the National Institutes of Health. SZ was supported in part by National Institutes of Health Grant Number UL1 RR024982, titled, “North and Central Texas Clinical and Translational Science Initiative”. We would also like to thank Adeola Jaiyeola, MD, MHSc and Brett Moran, MD for their participation in protocol development and manuscript revision. We also want to thank Jan Ross, M.S. for copyediting this manuscript.
PY - 2013
Y1 - 2013
N2 - Background: 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. Methods. 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. Results: 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). Conclusion: 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.
AB - Background: 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. Methods. 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. Results: 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). Conclusion: 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.
KW - Cardiopulmonary arrest
KW - Forecasting
KW - Intensive care units
KW - Medical informatics
KW - Medicine
KW - Models
KW - Statistical
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U2 - 10.1186/1472-6947-13-28
DO - 10.1186/1472-6947-13-28
M3 - Article
C2 - 23442316
AN - SCOPUS:84874226740
SN - 1472-6947
VL - 13
JO - BMC Medical Informatics and Decision Making
JF - BMC Medical Informatics and Decision Making
IS - 1
M1 - 28
ER -