trocardiology 44 (2011) e1–e64

O27Effects of atrial septal pacing sites on the P-wave duration correlatedwith baseline P-wave duration and morphologyYan Huob, Fredrik Holmqvista, Jonas Carlsona, Arash Aryab,Ulrike Wetzelb, Andreas Bollmannb, Pyotr PlatonovaaArrhythmia Clinic, Skåne University Hospital and Center for IntegrativeElectrocardiology at Lund University (CIEL), Lund, SwedenaDepartment of Electrophysiology, Leipzig Heart Center, LeipzigUniversity, Leipzig, Germany

Background: Atrial septal pacing (SP) has been shown to shorten P-waveduration and lower the risk of atrial fibrillation recurrence in patients withbradyarrhythmias. However, high variability of interatrial conductionpathways and baseline atrial conduction properties may explain the modestclinical benefit of SP and the high number of nonresponders. We performeda study to test the hypothesis whether interatrial conduction propertiesduring sinus rhythm may be used for selection of atrial septal pacing site andpredict the benefit of SP.

Methods: Forty-one consecutive patients (age 48 ± 16 years, 24 men) werestudied. Atrial septal pacing was delivered at high atrial septum (HAS),posterior septum behind fossa ovalis (PSFO), and CS ostium (CSo) with afixed cycle length. Twelve-lead electrocardiogram was recorded during 30secconds at baseline, and during pacing, transformed to orthogonal leads andsignal-averaged for analysis of P-wave duration (PWD).

Results: P-wave duration was significantly shorter during pacing at CSo(112 ± 17 milliseconds) than at HAS (122 ± 14 milliseconds, P = .031) orPSFO (125 ± 25 milliseconds, P = .005). There was a positive linearcorrelation between PWD (PWD = baseline PWD − paced PWD) and PWDat baseline, with longer P waves being associated with more advanced PWD.Eleven of 41 patients had normal PWD at baseline, defined as PWD b 120milliseconds. There were statistically significant differences in PWDbetween PWD b 120 milliseconds and PWD N 120 milliseconds groups atall HAS (−9.27 ± 19.84 vs 12.55 ± 15.62 milliseconds, P = .001), PSFO(−13.45 ± 18.11 vs 11.13 ± 31.16 milliseconds, P = .019), and CSo (−6.09 ±18.05 vs 25.20 ± 21.14 milliseconds, P = .000) pacing sites.

Conclusion: The optimal septal pacing sites can be selected by usingcombination of PWD at baseline and interatrial conduction properties. WhenP wave is prolonged at baseline, pacing at CSo might offer the most optimalbiatrial synchronization. However, the patients with normal PWD might notbenefit from septal pacing.

doi:10.1016/j.jelectrocard.2010.12.035

e12 Abstracts / Journal of Elec

O28The comparison of 6 quantitative waveform measures in measuring thedeterioration of untreated VFLawrence Shermana,b, Thomas Reaa,b, Randi Phelpsb, Carol Fahrenbruchb

aUniversity of Washington School of Medicine, Seattle, WA, USAbKing County Public Health Emergency Services Division

Background: Quantitative waveformmeasures (QWM) of VF are predictiveof shock outcome. These include logarithm of absolute correlations (LAC),median slope (MS), amplitude spectrum area (AMSA), angular velocity(AV), frequency ratio (FR), and cardioversion output predictor (COP).Several important characteristics of these QWM have not been experimen-tally determined: (1) how much variation exists between adjacent segmentsof VF, (2) what is the minimal length of VF required to determine the state ofVF, and (3) how rapidly do the QWM deteriorate over time with untreatedVF. We sought to determine these characteristics.

Methods: We used the automated external defibrillators recordings from 85out-of-hospital VF arrests from a metropolitan EMS system to derive the 6waveform measures: In part 1, one hundred fifty-two 5-second epochs of VFwere isolated. The 6 QWMwere calculated on epochs decreasing from 5- to1-second duration in 0.1-second increments. Altman/Bland analysis wasperformed to compare the truncated segments with the 5-second referencesegment. Bias and standard deviations (SD) of matched points at each timeinterval were performed. In part 2, 14 episodes of VF free of CPR for over 20

seconds were identified. The 6 QWM were then calculated withmeasurements made at 0.1-second intervals over the length of the episode.Altman/Bland analysis was performed to compare the initial interval of VFto each of the subsequent intervals. Bias at each point was then plotted andthe slope of this curve defined the rate of change. The SD between adjacentVF segments in part 2 was taken as the reference used to determine theminimum sufficient length of VF from part 1 with SD less than that.Results: Part 1: Minimum VF duration required for QWM calculation (inseconds): AMSA (2.3), MS (3.5), AV (4.4), COP (4.5), LAC (4.6), and FR(b1). Part 2: The average rate of change in untreated VF (in units/minute):MS (1.728), AV (1.326), AMSA (1.014), FR (0.438), LAC (0.288), andCOP (0.168).Conclusion: The deterioration of VF can be measured quantitatively byQWM. TheMS, AV, and AMSA demonstrate the greatest change. Length ofrecording required for accurate determination of QWM varies betweenmeasures from less than 1 to 4.6 seconds. The MS, AV, and AMSA can beused to monitor VF and guide therapy.

doi:10.1016/j.jelectrocard.2010.12.036

O29Modelling the relationship between electrocardiogram characteristicsand cardiopulmonary resuscitation quality during cardiac arrestKenneth Gundersena,b, Jan Terje KvaløyaaUniversity of Stavanger, NorwaybNorrwegian Air Ambulance Foundation

Background: Cardiopulmonary resuscitation (CPR) and defibrillation arethe main treatment interventions for cardiac arrest patients. In the recentyears, it has become possible to record signals reflecting in detail thecharacteristics of clinical CPR, and it has also been shown thatcharacteristics of the electrocardiogram (ECG) reflect the state (“vitality”)of fibrillating myocardium. Developing a statistical model for therelationship between clinically obtainable physiological measurements(eg, ECG characteristics, end-tidal CO2) and CPR characteristics (eg,compression force, depth) would be interesting for several purposes. Forexample, the CPR characteristics for optimal myocardial perfusion mightbe identified, and a model might be used clinically to guide treatment bypredicting the effect of CPR of some quality on the patient. In general,having a good, descriptive or mechanistic, model that relates importantphysiological measurements and treatment factors can allow for a betterunderstanding of how treatment affect a patient and how differenttreatments interact.Methods: In the current work, we have attempted to develop a minimalmodel for the relationship between median slope (an indicator of the state ofthe myocardium calculated from the ECG) and CPR characteristics by usingmixed-effects stochastic differential equation models and fitting these toobservational data from out-of hospital cardiac arrest episodes. Mixed-effects models are required because we have data from multiple patients ingreatly varying condition, and with possibly varying model parameters dueto differences in anatomy and etiology. Further, a stochastic model, asopposed to an ordinary (deterministic) differential equations model, isnecessary to account for correlation between model residuals and possiblesystem noise. The development with time of median slope, and how this isinfluenced by CPR, is in the current minimal model represented by a singledifferential equation. Candidate models were compared by their Akaikeinformation criterion.Results: In the best identifiedmodel, the presence of chest compressions had asignificant positive effect on the state of the patients asmeasured by themedianslope. However, none of the CPR characteristics compression force, depth,rate, duty cycle or leaning, or ventilation rate were significant in this model.Conclusion: Mixed-effects stochastic differential equation models seems tobe a reasonable choice of model type for the current modeling problem.However, much work remain, both to test and validate the current minimalmodel, to develop more elaborate models, and to develop models includingmore measurements (eg, end-tidal CO2).

doi:10.1016/j.jelectrocard.2010.12.037