A simple non-physiological artifact filter for invasive arterial blood pressure monitoring: a study of 1852 trauma ICU patients.
Invasive arterial blood pressure (BP) is a vital sign in hemodynamic monitoring of trauma intensive care unit (ICU) patients. Continuous BP analysis can potentially provide additional information about patient status, predict morbidity and mortality, and automatically populate electronic nurse charting systems than intermittent monitoring. Challenges to routine application in the ICU include integration of complex physiological data collection systems, artifacts, missing data, and the various clinical interventions that may temporarily corrupt the BP signal. We have developed and previously described SIMON (signal interpretation and monitoring), a physiological data collection system in the Trauma ICU at Vanderbilt University. In order to extract useful information from continuous arterial line BP monitoring, it is necessary to remove non-physiological artifacts. In this setting, potential artifacts appear to be caused by resonance, over-damping, and data transmission. We designed a simple filter to identify various sources of non-physiological artifacts using statistical signal processing techniques. We implemented the filter to arterial invasive BP signals of 1852 trauma patients throughout their length of ICU stay. After filtering, the power of BP measures to predict hospital death was enhanced. Therefore, we concluded that our strategy of removing non-physiological artifact was simple, fast and useful for an accurate assessment of BP measures in trauma patients.
Cao
Hanqing
H
Medical Centre, Vanderbilt University, Nashville, TN, USA.
Norris
Patrick
P
Ozdas
Asli
A
Jenkins
Judy
J
Morris
John A
JA
D000465:Algorithms; D016477:Artifacts; D001795:Blood Pressure Determination / Q000379:methods*; D003422:Critical Care / Q000379:methods*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted