e2 Abstracts

Methods: Our methodology involves taking advantage of anexisting body of knowledge in the form of ODEs and using this toreason more effectively with the real-time data available at thebedside. A standard first-order Euler approximation of the system ofODEs is mapped directly to a DBN. Delta nodes capture thechanges in the model variables in each time step. Model parametersare modeled as continuous gaussian nodes and allowed to vary overtime to allow them adjust to the individual patient. The frameworkallows a clear distinction to be made between true values andmeasured values, thus accounting for data uncertainty.Results: We applied our methodology to track and predictglycemia levels in critically ill patients in receipt of intravenousglucose and insulin. By using the DBN framework, we account foruncertainty in the reactions of unstable patients and uncertainty inmeasurement errors. We evaluated our approach on real data for12 critically ill patients. We compared our results to an existingapproach whereby model parameters were reestimated using anonlinear optimization algorithm. In 10 of 12 cases, the DBNapproach outperformed the existing approach, very substantially soin some cases.Conclusions: Much knowledge of human physiology is formalizedas systems of differential equations. We have devised a method-ology for incorporating this knowledge in a DBN framework anddemonstrated its effectiveness on a nonlinear system whereevidence is sparse and infrequent. The methodology is used topredict a critically ill patient's plasma glucose levels in response toinsulin and glucose infusions. With the data available, which aresporadic and may be inaccurate and incomplete, the DBN approachoutperforms a previous approach demonstrating that the DBNmethod is effective at reestimating model parameters and reasoningwith sparse and potentially unreliable data.

doi: 10.1016/j.jcrc.2012.01.014

O-3Development of a composite variability metric for clinical applicationAndrea Bravi a,b, Geoffrey Green b, André Longtin a, Andrew J.E. Seely a,b

aUniversity of Ottawa, Ottawa, Ontario, CanadabOttawa Hospital Research Institute, Ottawa, Ontario, Canada

Objectives: There is increasing interest in the application ofvariability monitoring to improve clinical outcomes. A broad arrayof measures of variability are collected with the aim of tracking thephysiological condition of patients undergoing different types ofstresses, either physiological (eg, exercise) or pathological (eg,critical illness). The sheer number of variability measures calls for ameans to integrate the data, capable of maintaining clinicallyrelevant information, yet reducing its dimensionality. Thus, ouraims are to introduce a dynamic methodology to compute acomposite measure of variability, designed to integrate the relevantinformation from multiple measures of variability, in a mannertunable to specific clinical applications, and pilot the performanceof the composite measure on a known data set of heart ratevariability monitoring before and during the onset of infection.Methods: Waveforms collected at the bedside (eg, electrocardio-gram, capnograph, etc) undergo cleaning, beat and breath detection,and removal of noise, followed by variability analysis for everyinterval (eg, 5 minutes), iteratively repeated in steps (eg, 2.5

minutes), providing variability monitoring over time. For eachinterval, a composite variability measure is computed based on amodular design, with options based on the intended clinical use.This measure is created by applying the following procedures: (a)filtering, (b) normalization, (c) feature selection, and (d)integration. The normalization ensures that the measures arecomparable with each other. Depending on the type of study, oneor more of the following normalizations may be used: admissioncondition (which measures the deterioration from a baseline ofhealth), population-based (which associates variability to theseverity of illness), or [−1,+1] (used for visualization, or to bindthe values for particular postprocessing). The feature selectionfocuses the analysis on specific types of information, magnifyingwhat is clinically relevant for a specific patient. Modules includethe domain-specific (eg, to investigate the specific alterations inscale-invariant domain variability measures), the physiologicallybased (eg, selecting measures associated with sympatheticactivity), and the clinical-based (eg, selecting measures thatchange in association with specific clinical events). The integrationfuses the relevant information that survived to the previous steps,creating a unique composite measure. Modules of integrationinclude principal component analysis (ie, principal componentanalysis captures the direction of maximum variance, as well asthe directions uncorrelated to it), independent component analysis(ie, independent component analysis [ICA] finds a set ofindependent signals generating the recorded variability, whichmay then be selected based on correlations with known clinicalevents), and the median of the variability (ie, provides a measureof cumulative change in variability).Results: Through a windowed analysis of the R-R interval timeseries, a set of 102 variability time series was extracted for eachpatient from a population of 17 patients, whose heart rate wascontinuously monitored after bone marrow transplant (monitoringlasted 12 ± 4 days). A composite measure of variability was createdfor each subject, using the admission condition normalization(which referenced the data respect to the first 24 hours of recordingafter admission), without any particular selection, and integratingthe information through principal component analysis. Thecomposite measures showed, on average, a 30% drop 42 hoursbefore and a 180% drop in the moment of detection and initiationof therapy for infection.Conclusions: A dynamic modular computational procedure tocreate a composite variability metric from a set of comprehensivearray of variability measures is proposed. This procedure wasretrospectively applied to track the development of infection ina high-risk population, creating a composite measure thatshowed, on average, a 30% change, 42 hours before its detectionby physician.

doi: 10.1016/j.jcrc.2012.01.015

O-4The short-term fractal exponent of heart rate variability is associatedwith mortality in severe sepsis and septic shockSamuel Brown a,b, Subhasis Behera b, Jason Jones c, Michael Lanspa a,b,

Colin Grissom a,b, Kathryn Kuttler a,b, Ben Briggs a, Naresh Kumar a,

V.J. Mathews b

aIntermountain Medical Center, Murray, UT

e3Abstracts

bUniversity of Utah, Salt Lake City, UTcKaiser Permanente Southern California, Pasadena, CA

Objectives: Sepsis is known to affect heart rate variability (HRV),which may be of prognostic or therapeutic significance. Whichparameters of HRV will prove useful is not yet known. Nonlinearparameters of HRV, such as fractal exponents, have shown promisein heart failure and may be of use in sepsis.Methods:We prospectively identified patients with severe sepsis orseptic shock admitted to intensive care units in our academic,tertiary care hospital and recorded their electrocardiographicsignals, sampled at 500 Hz, within 12 hours of intensive care unitadmission. Using detrended fluctuation analysis, we determined theinitial short-term (alpha1) and intermediate-term (alpha2) expo-nents of the time series of R-R intervals from the electrocardio-graphic signal. We also calculated sample entropy on the time seriesof R-R intervals. We determined 28-day mortality as a primaryoutcome. We used Student t test to evaluate the relationshipbetween fractal exponents and mortality.Results:We identified 23 patients, of whom 4 (17%) died within 28days. Among nonsurvivors, alpha1 was significantly lower thanamong survivors (0.64 vs 1.27, P b .05). There was an inversecorrelation (Pearson ρ = −0.66, P b .01) between admissionspecific organ failure assessment (SOFA) score and alpha1. Neitherthe intermediate-term exponent, alpha2, nor sample entropydiffered between survivors and nonsurvivors.Conclusions: Fractal exponent alpha1 appears to be associated with28-day mortality in septic patients; alpha1 is also inverselyassociated with admission SOFA score. In this small sample, wewere unable to evaluate its predictive utility independent of SOFAscore. Regardless, HRVmay be simpler and more reproducible thanSOFA and may represent a potential therapeutic target in sepsis.

doi: 10.1016/j.jcrc.2012.01.016

O-5Multiscale rhythmic influences on heart rate variability inhuman endotoxemiaJeremy D. Scheff a, Steve E. Calvano b, Stephen F. Lowry b,

Ioannis P. Androulakis a,b,c

aDepartment of Biomedical Engineering, Rutgers University, Newark, NJbDepartment of Surgery, UMDNJ-Robert Wood Johnson Medical School,

New Brunswick, NJcDepartment of Chemical and Biochemical Engineering, Rutgers

University, Newark, NJ

Objectives: Endogenous glucocorticoids are secreted by thehypothalamic-pituitary-adrenal (HPA) axis in response to a widerange of stressors. Glucocorticoids exert significant downstreameffects, including the regulation of many inflammatory genes. TheHPA axis functions such that glucocorticoids are released in apulsatile manner, producing ultradian rhythms in plasma glucocor-ticoid levels. It is becoming increasingly evident that this ultradianpulsatility is important in maintaining proper homeostatic regula-tion and responsiveness to stress. Changes in heart rate variability(HRV) reflect alterations in autonomic function, which ispotentially a useful predictor of outcome in myocardial infarction,congestive heart failure, diabetic neuropathy, and neonatal sepsis.We hypothesize that the relationship between ultradian hormonal

variability and health is related to the relationship between theloss of HRV and disease. We test this hypothesis through amathematical model that encompasses rhythmicity in both the HPAaxis and in cardiac neuron firing.Methods: Much of the complexity of HRV is generated by high-frequency neural oscillations in the sympathetic and parasympa-thetic branches of the autonomic nervous system. The autonomicnervous system is critical in the progression of the inflammatoryresponse, through the release of hormones like cortisol andepinephrine as well as the activation of the cholinergic anti-inflammatory pathway. We proposed a model of human endotox-emia that links the receptor-mediated activation of cytokinesignaling in leukocytes with central autonomic modulation,culminating in perturbed heart rate and HRV through an integralpulse frequency modulation model. We have also proposed a modellinking the ultradian release of glucocorticoids by the HPA axiswith altered regulation of glucocorticoid-responsive genes, as hasrecently been observed experimentally. To further explore theinteractions between these 2 systems, we evaluated the effect ofrhythmicity at the scale of ultradian rhythms on the rhythmicityof heart beats.Results: Due to the nonlinear binding relationship betweenglucocorticoids and the glucocorticoid receptor, oscillatory gluco-corticoid exposure produces a larger downstream transcriptionalresponse than constant exposure to the same total level ofglucocorticoids. In the larger context of our model of inflammation,this leads to constitutive heightened activation of the sympatheticnervous system and elevated resting levels of anti-inflammatorycytokines and hormones such as epinephrine. Propagated throughthe integral pulse frequency modulation model, it is predictedthat the loss of ultradian hormone rhythmicity will increase heartrate and decrease HRV. This heightens susceptibility to a large,unresolving response.Conclusions: The results shown here illustrate how ultradian rhy-thms in glucocorticoid secretion can propagate through to HRV.This work provides the foundation for evaluating both pathophy-siologic HPA axis behavior and in considering the clinical use ofglucocorticoids, which are typically not given in an ultradianmanner. In the context of these scenarios, where the loss of normalhormone rhythmicity impacts the host, response of the system toendotoxemia, as assessed by HRV, serves as a step toward gaininginsight into similar changes in HRV observed clinically in responseto stress.

doi: 10.1016/j.jcrc.2012.01.017

O-6Dynamic algorithms of biomarkers for monitoringinfection/inflammation processes in pigsTim Tambuyzer a, Tine De Waele b, Geert Meyfroidt c,

Greet Van den Berghe c, Bruno Goddeeris b, Daniel Berckmans a,

Jean-Marie Aerts a

aMeasure, Model and Manage Bioresponses (M3-BIORES), Department of

Biosystems, K.U. Leuven, Kasteelpark Arenberg 30, B-3001 Leuven, BelgiumbLaboratory of Immunology, Faculty of Veterinary Medicine,

Ghent University, Salisburylaan 133, B-9820 Merelbeke, BelgiumcSurgical Intensive Care Unit, Department of Intensive Care Medicine,

K.U. Leuven, Herestraat 49, B-3000 Leuven, Belgium