Predicting ICU hemodynamic instability using continuous multiparameter trends.
BACKGROUND
Identifying hemodynamically unstable patients in a timely fashion in intensive care units (ICUs) is crucial because it can lead to earlier interventions and thus to potentially better patient outcomes. Current alert algorithms are typically limited to detecting dangerous conditions only after they have occurred and suffer from high false alert rates. Our objective was to predict hemodynamic instability at least two hours before a major clinical intervention (e.g., vasopressor administration), while maintaining a low false alert rate.


METHODS
From the MIMIC II database, containing ICU minute-by-minute heart rate (HR) and invasive arterial blood pressure (BP) monitoring trend data collected between 2001 and 2005, we identified 132 stable and 104 unstable patients that met our stability-instability criteria and had sufficient data points.


METHODS
We first derived additional physiological parameters of shock index, rate pressure product, heart rate variability, and two measures of trending based on HR and BP. Then we developed 220 statistical features and systematically selected a small set to use for classification. We applied multi-variable logistic regression modeling to do classification and implemented validation via bootstrapping.


RESULTS
Area under receiver-operating curve (ROC) 0.83+/-0.03, sensitivity 0.75+/-0.06, and specificity 0.80+/-0.07; if the specificity is targeted at 0.90, then the sensitivity is 0.57+/-0.07. Based on our preliminary results, we conclude that the algorithms we developed using HR and BP trend data may provide a promising perspective toward reliable predictive alerts for hemodynamically unstable patients.
Cao
Hanqing
H
Philips Research North America, Briarcliff Manor, NY 10510, USA. hanqing.cao@philips.com
Eshelman
Larry
L
Chbat
Nicolas
N
Nielsen
Larry
L
Gross
Brian
B
Saeed
Mohammed
M
D000465:Algorithms; D001794:Blood Pressure; D006339:Heart Rate / Q000502:physiology; D006439:Hemodynamics / Q000502:physiology*; D006801:Humans; D007362:Intensive Care Units / Q000706:statistics & numerical data*; D008991:Monitoring, Physiologic; D009102:Multiple Organ Failure / Q000175:diagnosis* / Q000503:physiopathology*; D016571:Neural Networks, Computer; D012372:ROC Curve; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software; D013997:Time Factors