How can blood pressure and depth of narcosis prediction benefit from a networked operating room?

e6 standardized Continuous Individualized Multiorgan Variability Analysis system. Results: Repetitive fetal UCO resulted in marked acidemia (pH 7.36 ± 0.01 decreasing to 6.99 ± 0.01, mean ± SEM). We found individual differences in the timing of increase in RMSSD and SampEn compared with EEG-FHR pattern onset. At the 1000-Hz fHRV sampling rate, in 2 animals RMSSD increased ~ 1 hour and 6 minutes before the onset of the EEG-FHR pattern at ~ 23 minutes before pH b 7.00 drop. With SampEn, this time was increased by another hour for all 7 fetuses at ~ 2 hour and 11 minutes (range, 1 hour and 17 minutes–2 hours and 40 minutes) before pH b 7.00. At the 4-Hz fHRV sampling rate, the same 2 of 7 fetuses were missed with SampEn as with RMSSD. Sample entropy decreased at ~ 44 minutes (range, 22 minutes–1 hour and 39 minutes) before pH b 7.00. In 6 of 7 fetuses, EEG-FHR pattern emerged ~ 34 minutes earlier than SampEn increase (range, 6 minutes–1 hour). In contrast, in 1 fetus, the increase of SampEn was found 20 minutes and that of RMSSD was 40 minutes earlier than EEG-FHR pattern onset. Moreover, RMSSD increased ~ 26 minutes earlier than EEGFHR pattern onset in 2 of 7 fetuses. Conclusions: At 1000 Hz, SampEn extends our ability to detect fetal acidemia. At 4 Hz, EEG-FHR monitoring is augmented by the addition of fHRV RMSSD monitoring, but not by SampEn. Our findings support the hypothesis that multivariate EEG-FHRfHRV EFM is more likely to detect fetal acidemia early than any of these modalities alone, even at the low fHRV sampling rate of 4 Hz used clinically. http://dx.doi.org/10.1016/j.jcrc.2012.10.026

Abstract 11 How can blood pressure and depth of narcosis prediction benefit from a networked operating room? Marcus Koeny a, Anna Kerekes a, Michael Czaplik b, Rolf Rossaint b, Steffen Leonhardt a a Chair for Medical Information Technology, RWTH Aachen, Aachen, Germany b Department of Anaesthesiology, University Hospital Aachen, Aachen, Germany

Objectives: During standard surgical interventions, the anesthesiologist controls the depth of anesthesia (DoA) using several drugs. Currently, decisions are made based on vital signs such as heart rate and blood pressure from monitors, anesthesia machines, and by observing visual events such as sweating or patient movement. The anesthesiologist must regard information displayed on each of these devices in the context of the simultaneous surgical intervention, to react to events such as pain stimuli or CO2 insufflation. For optimal anesthesia, not only the current situation and context are relevant but also anticipated procedures. Moreover, in some cases, heart rate and blood pressure follow a certain trend due to the pharmacokinetic (PK) and pharmacodynamic (PD) properties of applied drugs and therefore are potentially predictable. This fact is already in use for some commercially available projects like SmartPilot View (Draeger, Luebeck, Germany). The smart operating room research project aims to build a manufacturer independent standard that networks all medical devices used in the operating room. Consolidated information about the patient condition and relevant parameters from

Abstracts networked devices are collected and shown on central displays optimized for the surgical and anesthesiology workplaces, respectively. As an application example, we present an approach to estimate and predict blood pressure and DoA based on a PK/PD model together with information gathered from the networked operating room. In particular, we focus on intravenous anesthesia using propofol and remifentanil. Methods: The PK modeling uses standard models and parameters that are already in use in target-controlled infusion pumps. Blood pressure is estimated using a linear combination of the concentrations of propofol and remifentanil. In addition, the Bateman function models the influence of manually applied drugs. Finally, the influence of pain stimuli is modeled using a simple proportional time delay in combination with information about the event. Vital signs and information about applied drugs are automatically obtained from devices within the smart operating room network in real time. Information about further events such as pain stimuli are currently obtained post hoc. In future, these events will be provided by a combination of an expert system and a workflow engine. Results: Using the described PK/PD model, blood pressure can be estimated and predicted during neutral conditions, depending on parameters such as patient weight, age, size, and sex. The influence of pain stimuli can currently only be simulated post hoc because of high interpatient variation. Once the parameters have been estimated for a specific patient, the influence of pain stimuli can be considered during the prediction of blood pressure. Unfortunately, because such a calibration would require forced pain stimuli, it is not feasible due to practical and ethical reasons. Conclusions: Blood pressure can be predicted during normal conditions. As a matter of fact, DoA cannot be assessed and predicted reasonably using only the blood pressure. Therefore, further vital signs and other clinical observations must be considered. Nevertheless, the aim of the prediction is to assist and not to replace the anesthesiologist by means of a combination of a PK/PD model and a workflow-supported expert system, especially in critical situations. http://dx.doi.org/10.1016/j.jcrc.2012.10.027

Abstract 12 Thermoregulatory responses of coronary bypass patients to a removal of the heating blanket Tim Tambuyzer a, Jasmine Craps a, Geert Meyfroidt b, Greet Van Den Berghe b, Daniel Berckmans a, Jean-Marie Aerts a a Measure, Model & Manage Bioresponses (M3-BIORES), Department of Biosystems, KU Leuven, Leuven, Belgium b Surgical Intensive Care Unit, Department of Intensive Care Medicine , KU Leuven, Leuven, Belgium

Objectives: The control and regulation of body temperature is one of the key homeostatic functions of the body. In response to internal or external stimuli (such as thermal disturbances of the environment, invading pathogens, medications, surgical trauma, psychological stress, etc), these complex control mechanisms are continuously adjusted to maintain an optimal internal microenvironment. After a cardiac surgery, the thermoregulation is often highly disturbed. In this study, we hypothesized that insight in the dynamics of the thermoregulatory responses of coronary bypass patients can provide critical information about the health status of