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Relation of Death Within 90 Days of Non-ST-Elevation AcuteCoronary Syndromes to Variability in Electrocardiographic

Morphology

Zeeshan Syed, MEnga,b, Benjamin M. Scirica, MD, MPHc, Satishkumar Mohanavelu, MSc,Phil Sung, SBb, Eric L. Michelson, MDd, Christopher P. Cannon, MDc, Peter H. Stone, MDc,

Collin M. Stultz, MD, PhDa,b, and John V. Guttag, PhDb,*

Electrocardiographic measures can facilitate the identification of patients at risk of deathafter acute coronary syndromes. This study evaluates a new risk metric, morphologicvariability (MV), which measures beat-to-beat variability in the shape of the entire heartbeat signal. This metric is analogous to heart rate variability (HRV) approaches, whichfocus on beat-to-beat changes in the heart rate. MV was calculated using a dynamictime-warping technique in 764 patients from the DISPERSE2 (TIMI 33) trial for whom24-hour continuous electrocardiograph was recorded within 48 hours of non-ST-elevationacute coronary syndrome. The patients were evaluated during a 90-day follow-up for theend point of death. Patients with high MV showed an increased risk of death duringfollow-up (hazard ratio 8.46; p <0.001). The relationship between high MV and deathcould be observed even after adjusting for baseline clinical characteristics and HRVmeasures (adjusted hazard ratio 6.91; p � 0.001). Moreover, the correlation between MVand HRV was low (R <0.25). These findings were consistent among several subgroups,including patients under the age of 65 and those with no history of diabetes or hyperlip-idemia. In conclusion, our results suggest that increased variation in the entire heart beatmorphology is associated with a considerably elevated risk of death and may provideinformation complementary to the analysis of heart rate. © 2009 Elsevier Inc. (Am J

Cardiol 2009;103:307–311)

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We evaluated a new metric, morphologic variabilityMV), which measures beat-to-beat variability in the mor-hology of heart beats.1 MV uses a dynamic time-warpinglgorithm to quantify differences in the shape, or morphol-gy, of consecutive beats. Time-warping is defined as therocess of aligning heart beats so that matching samples cane compared while quantifying energy changes between theignals (Figure 1). Using this algorithm, we re-expressed anlectrocardiographic (ECG) signal as a morphologic dis-ance time series that is similar to the RR Interval timeeries of heart rate variability (HRV), that is, the NN timeeries. MV is measured as the high-frequency variability inhe morphologic distance time series. We studied the asso-iation between MV and death over a 90-day period afteron-ST-elevation acute coronary syndromes (NSTE-ACS).

aHarvard-MIT Division of Health Sciences and Technology, Cam-ridge, Massachusetts; bMIT Department of Electrical Engineering andomputer Science, Cambridge, Massachusetts; cTIMI Study Group, Car-iovascular Division, Department of Medicine, Brigham and Women’sospital, Boston, Massachusetts; and dAstraZeneca LP, Wilmington, Del-

ware. Manuscript received July 28, 2008; revised manuscript received andccepted September 12, 2008.

This work was supported, in part, by Center for Integration of Medicinend Innovative Technology, Harvard-MIT Division of Health Sciences andechnology, and Industrial Technology Research Institute (Taiwan). TheISPERSE2 trial was supported by AstraZeneca.

*Corresponding author: Tel: 617-253-6022; fax: 617-253-8460.

mE-mail address: guttag@mit.edu (J.V. Guttag).

002-9149/09/$ – see front matter © 2009 Elsevier Inc.oi:10.1016/j.amjcard.2008.09.099

ethods

To explore the ability of MV to discriminate betweenow- and high-risk patients in the setting of NSTE-ACS,e used data retrospectively from the DISPERSE2 (TIMI3) study,2 which compared the safety and efficacy ofZD6140 and clopidogrel in patients with NSTE-ACS.atients were enrolled if they experienced ischemic symp-

oms at rest for a duration exceeding 10 minutes with eitheriochemical marker evidence of myocardial infarction (de-ned as Tronponin-T, -I, or creatinine kinase–MB elevationreater than the local myocardial infarction decision limit)r ECG evidence of ischemia (defined as the presence ofew or presumably new ST-segment depression �0.05 mV,ransient ST-segment elevation �0.1 mV, or T-wave inver-ion �0.1 mV in 2 or more contiguous leads). As part ofhis study, continuous electrocardiograph was recorded for aedian duration of 4 days. Three-lead LifeCard CF Holteronitors were placed within 48 hours of the initial event,

nd the data were sampled at 128 Hz. Patients were fol-owed up for a period of 90 days.

The DISPERSE2 study randomized 990 patients. ECGecordings were available for 862 patients after accountingor technical defects. For each of these cases, at least 24ours of continuous ECG were available. Fifteen deathsccurred in this population during the follow-up period.

We preprocessed the ECG signal in a fully automatedanner for noise removal and signal rejection. Noise re-

oval was carried out in 2 steps. Baseline wander was

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308 The American Journal of Cardiology (www.AJConline.org)

emoved by subtracting an estimate of the wander obtainedy median filtering the original ECG signal.3 The ECGignal was then filtered using wavelet denoising with a softhreshold and normalized by the mean R-wave amplitude.4

ignal rejection, intended to remove segments of the ECGignal where the signal-to-noise ratio was sufficiently lowven after noise removal, was also carried out in 2 steps.arts of the ECG signal with a low signal quality index were

dentified and removed using the Physionet Signal Qualityndex (SQI) package.5 The remaining data was divided intoalf-hour windows, and for each window, the SD of the-wave amplitudes was calculated. If this value exceeded.2887 (the SD for a uniform distribution between 0.5 and.5), the window was discarded. This heuristic is based onhe belief that given the earlier normalization of the ECGignal, a SD exceeding 0.2887 is unlikely to be physiolog-cal because it would correspond to the R-wave amplitude,hanging uniformly by more than 50% of its mean value.

To allow direct comparison with typical HRV analysis,he preprocessing stage also removed ectopic segmentsf the electrocardiograph, using the Physionet SQI pack-ge.6 This step ensured that the major difference betweenV and HRV analysis was not in the signal analyzed but in

he use of morphology rather than the length of heart beats.e also limited analysis of the ECG signal to the first 24

ours of recording because this data was available for allatients and allowed for uniform analysis.

MV was calculated1 for every patient. For each pair ofonsecutive heart beats in the ECG signal, differences inorphology were quantified by calculating an energy dif-

erence between them. The simplest way to calculate thisnergy difference would be to subtract the samples of 1 beatrom another. However, if samples are compared based

igure 1. Alignment of beats by dynamic time warping. Vertical linesonnect corresponding samples from the top beat to the bottom. If samplesre compared strictly on their distance from the start of the beat, thisrocess may end up computing the difference between unrelated parts ofhe 2 signals. One consequence of this approach is that samples represent-ng the T-wave of the top beat are compared with samples following thend of the T-wave in the bottom beat (above left). A more accuratepproach would align each sample in 1 beat with 1 or more matchingamples in the other beat (above right). The dynamic time-warping algo-ithm produces the optimal alignment of 2 sequences of possibly differentengths, where optimality is defined as minimizing the overall distortionesulting from differences in the amplitude and timing of ECG waves.lthough this example illustrates variability in the T-wave, dynamic timearping is designed to more generally capture changes spanning the entire

ardiac cycle.

trictly on their distance from the start of the P-wave, T

his process may lead to computing differences betweenamples associated with different waves or intervals in the 2ignals (Figure 1). MV uses a variant of dynamic timearping (DTW)6 to address this issue and to align samples

hat correspond to the same kind of underlying physiolog-cal activity. Given 2 beats, x1 and x2, of length l1 and l2,espectively, DTW produces an alignment of the 2 se-uences by first constructing an l1-by-l2 distance matrix d.ach entry (i,j) in this matrix d represents the square of theifference between samples x1[i] and x2[j]. A particularlignment then corresponds to a path, �, through the dis-ance matrix of the form:

�(k) � (�1(k), �2(k)), 1 � k � K (1)

here �1 and �2 represent row and column indices into theistance matrix, and K is the alignment length.

The optimal alignment produced by DTW minimizes theverall cost:

C(x1, x2) � min C��

(x1, x2) (2)

here C� is the total cost of the alignment path � and isefined as:

C�(x1, x2) � �k�1

K

d(x1[�1(k)], x2[�2(k)]) (3)

The search for the optimal path is carried out in anfficient manner using dynamic programming.7 The finalnergy difference between the beats x1 and x2 is given by theost of their optimal alignment and depends on both themplitude differences between the 2 signals and the length K ofhe alignment (which increases if the 2 beats differ in theiriming characteristics). In this way, the technique describedere measures changes in morphology resulting from bothmplitude and timing differences between the 2 beats.

The optimal alignment cost of DTW transforms the orig-nal ECG signal from a sequence of beats to a sequence ofnergy differences. This new signal, comprising pair-wise,ime-aligned energy differences between beats, was thenmoothed using a median filter of length 8. The resultingime series was defined as the morphologic distance timeeries for the patient. MV was calculated as the energy inhe morphologic distance time series between 0.30 to 0.55z. The MV risk variable was dichotomized at the highestuartile (i.e., high values corresponding to increased risk)linded to outcome.

HRV was measured using the SDNN (SD of NN inter-als for the entire 24-hour recording), SDANN (SD of theverage NN intervals for all 5-minute segments of a 24-hourecording), RMSSD (square root of the mean squared dif-erences of successive NN intervals), pNN50 (the ratioerived by dividing the number of interval differences ofuccessive NN intervals �50 ms by the total number of NNntervals), HRVI (total number of all NN intervals dividedy the height of the histogram of all NN intervals measuredn a discrete scale with bins of 7.8125 ms), and LF/HFaverage ratio of the power in the frequency spectrum of-minute windows of the time series between 0.04 to 0.15z and 0.15 to 0.4 Hz) metrics, which were proposed by the

ask Force of the European Society of Cardiology and the

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309Coronary Artery Disease/Morphologic Variability

orth American Society of Pacing and Electrophysiology.8

ll metrics were dichotomized at the lowest quartiles (i.e.,ow values corresponding to increased risk).

Additionally, the following dichotomized risk variablesere prospectively selected for analysis: age �65, smokingistory, diabetes mellitus, hypertension, hyperlipidemia,istory of coronary heart disease, previous myocardial in-arction, previous angina, index diagnosis (unstable anginar NSTE-myocardial infarction), and ST-segment depres-ion �0.5 mm on qualifying 12-lead electrocardiograph.

Statistical analyses were performed using the MATLABtatistics Toolbox 6.2 (Mathworks Inc., Natick, Massachu-etts). Hazard ratios and 95% confidence intervals werestimated using a Cox proportional hazards regressionodel. All risk predictors were included in a univariate

nalysis of the outcome of death. The risk variables werelso examined using multivariate analysis in 2 models.

able 1atient characteristics for MV � 1 and MV � 0 groups

arameter Patients With HighRisk Morphologic

Variability(n � 191)

Patients With LowRisk Morphologic

Variability(n � 573)

p Value

ge (yrs) 66 (54 to 78) 61 (50 to 73) � 0.001omen 38% 36% 0.505

moker 59% 55% 0.388ypertension 71% 68% 0.353iabetes 30% 21% 0.012yperlipidemia 58% 66% 0.066istory of coronaryheart disease

35% 37% 0.567

able 2nivariate association between clinical and ECG variables and deathver 90 days following NSTE-ACS (n � 764)

arameter HazardRatio

95% ConfidenceInterval

pValue

ge �65 3.72 1.19–11.70 0.024omen 2.76 0.98–7.75 0.054

moker 0.53 0.19–1.48 0.225ypertension 6.66 0.87–50.67 0.067iabetes 2.77 1.00–7.65 0.049yperlipidemia 0.66 0.24–1.82 0.422istory of coronary heartdisease

0.13 0.02–0.95 0.045

revious myocardial infarction 1.94 0.69–5.45 0.210revious angina 2.86 0.81–10.15 0.103ndex diagnosis of non-ST-

elevation myocardialinfarction

1.06 0.38–2.99 0.906

T depression �0.5mm 2.69 0.86–8.45 0.091RV (SDNN) 1.50 0.51–4.40 0.460RV (SDANN) 2.76 1.00–7.61 0.050RV (RMSSD) 0.45 0.10–1.98 0.290RV (pNN50) 0.46 0.10–2.03 0.303RV (HRVI) 2.09 0.75–5.89 0.161RV (LF/HF) 2.83 1.02–7.83 0.045V 8.46 2.69–26.58 �0.001

he first model included all risk variables, but the second p

odel included only variables demonstrating an associationith outcomes on univariate analysis (p �0.1). To estimate

he discriminative value of MV, we also calculated the areander the receiver operating characteristic curve for theredicted survival of subjects.9

The AZD6140 and clopidogrel patient populations werexamined both together and separately. There was no sta-istically significant difference detected in the 2 populationsith treatment.

esults

During initial ECG preprocessing, 98 patients (11%)ere automatically excluded because of the presence of10 hours of clean data for analysis. For the remaining 764

atients, preprocessing removed, on average, 4.97% (me-ian 1.30%) of the segmented beats over 24 hours. Ex-luded patients had similar characteristics to those retainedor our study except for age, with excluded patients being,n average, older (66 vs 62, p �0.001). There were noeaths among the excluded group.

The highest-quartile cutoff for MV was 52.5. We usedhis value to dichotomize patients into low-risk (�52.5) andigh-risk (�52.5) groups. The clinical characteristics ofatients in the high-risk (MV � 1) and low-risk (MV � 0)roups according to MV are listed in Table 1.

Results of the univariate association between the riskariables and the outcome of death are listed in Table 2. Onnivariate analysis, MV was the most significant predictorf death (p �0.001). The highest hazard ratio of 8.46 wasound in patients belonging to the top quartile of morpho-ogic variability. For the clinical characteristics studied,nly age �65, diabetes, and a history of coronary heartisease showed a significant association with death in thisohort. In the case of heart rate measures, both the time-omain (SDANN) and frequency-domain (LF/HF) metricsf HRV showed an association with death during the 90-dayollow-up. The receiver operating characteristic curve C-tatistic for MV was 0.77. For comparison, the C-statisticsor HRV-SDANN and HRV-LF/HF were 0.56 and 0.67,espectively.

This relationship between MV and death was even more

igure 2. Kaplan-Meier mortality curves comparing patients in the highestuartile of MV with the remainder of the cohort. HR � hazard ratio.

ronounced for the first 30 days following NSTE-ACS

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310 The American Journal of Cardiology (www.AJConline.org)

hazard ratio 12.30; p � 0.002). The only other risk variableignificantly associated with death over this period was age65 (hazard ratio 5.44; p � 0.032). The C-statistic for MV

n this case was 0.81.Kaplan-Meier mortality curves for patients in the high-

nd low-risk populations according to MV are listed inigure 2. Patients in the highest quartile of MV were atignificantly elevated risk of death over the subsequent 90ays, with the difference apparent within the first 30 days.

Patients with high MV remained at a significantly ele-ated risk of death even after controlling for the other riskariables (Table 3). In multivariate analysis, patients in theighest quartile of MV were consistently associated witheath, regardless of whether the model was built using allnivariate variables (adjusted hazard ratio 6.91; p � 0.001)r only those associated with death at p �0.1 (adjustedazard ratio 6.75; p � 0.002). No other risk variable wasndependently associated with death in the multivariateodels. We included HRV-LF/HF in the multivariateodel as the best performing HRV measure. Virtually iden-

ical negative results were seen when HRV-SDANN wasncluded instead. The correlation between MV and bothRV measures was low, (i.e., –0.24 for MV and HRV-F/HF and 0.02 for MV and HRV-SDANN).

As in the univariate case, the relationship between MVnd death on multivariate analysis was even more markedver the first 30 days after NSTE-ACS in both the modelncluding all risk variables (adjusted hazard ratio 9.41; p �.006) and the model constructed with only those variableshat were associated with death at p �0.1 (adjusted hazardatio 9.51; p � 0.005).

The association between MV and death was also consis-

able 3ssociation between clinical and ECG variables and death using 2ultivariable models. Model 1 included all variables in the study. Modelincluded only variables associated with death on univariate analysis (p0.1). HRV here corresponds to the best performing HRV measure on

nivariate analysis.

arameter MultivariateModel 1

MultivariateModel 2

HazardRatio

p Value HazardRatio

p Value

ge �65 1.11 0.871 1.67 0.399omen 1.74 0.375 . . . . . .

moker 1.11 0.870 . . . . . .ypertension 5.56 0.109 . . . . . .iabetes 1.78 0.286 2.12 0.151yperlipidemia 0.52 0.279 . . . . . .istory of coronary heartdisease

0.15 0.067 0.16 0.077

revious myocardialinfarction

1.68 0.389 . . . . . .

revious angina 1.94 0.344 . . . . . .ndex diagnosis of non-ST-

elevation myocardialinfarction

0.82 0.722 . . . . . .

T depression �0.5mm 1.21 0.756 . . . . . .RV (LF/HF) 1.35 0.595 1.31 0.620V 6.91 0.001 6.75 0.002

ent in several subgroups, including patients under the age

f 65 and those with no history of diabetes or hyperlipid-mia (Figure 3).

iscussion

We explored the information provided by MV, HRV,nd baseline clinical characteristics in identifying patients atncreased risk of death post-NSTE-ACS using data from thehrombolysis In Myocardial Infarction (TIMI) DISPERSE2tudy.2 In the present work, we found that information in

V is uncorrelated to information in HRV. MV was sig-ificantly, and independently, associated with death over a0-day period after hospital admission for NSTE-ACS. Thencreased hazard for death was consistent among manyifferent subgroups, including patients under the age of 65nd those patients with no evidence of diabetes or hyper-ipidemia. In addition, the ROC curve for MV showed a-statistic of 0.77, suggesting that MV provides a reason-ble measure that can be used to discriminate between low-nd high-risk patients post-NSTE-ACS.

Although our decision to study death over a 90-day timerame was based on a desire to use all follow-up datavailable to us in the DISPERSE2 study, the relation be-ween MV and death was even more marked for the first 30ays after NSTE-ACS. Because MV is designed as a mea-ure of pro-arrhythmicity, this observation could suggest thencreased ability of MV to identify patients likely to sufferatal arrhythmias in the period immediately following thendex event. Patients who died later in the study may alsoave had changes in MV in the period shortly before fatalvents (e.g., patients who might have been missed by MVould potentially have been determined as being at high riskad ECG recordings been available in the weeks immedi-tely preceding the event). More data are needed to evaluatehis hypothesis.

igure 3. Hazard ratios and 95% confidence intervals for MV associationith death using the highest-quartile cutoff in various subgroups. HR �azard ratio.

Our study has limitations. The decision to calculate MV

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311Coronary Artery Disease/Morphologic Variability

nd HRV on 128-Hz Holter ECG signals represents a prac-ical use-case of these methods, (i.e., the ability to computerognostic parameters on data that are typically availableor all admitted patients). With higher sampling rates, how-ver, the performance of these metrics could potentially bemproved—in particular, our ability to recognize differ-nces lasting a few milliseconds. Other limitations of theISPERSE2 dataset included the nonavailability of vari-

bles such as the left ventricular ejection fraction, informa-ion on drug therapy, or the time within 48 hours of admis-ion when ECG monitoring was started. Our study also doesot compare MV, which is designed to measure intrinsicyocardial instability, with other morphologic techniques

uch as T-wave alternans.10,11 These techniques have beenhown to have utility in predicting pro-arrhythmicity butequire specialized equipment and maneuvers to change theeart rate. As a result, metrics such as T-wave alternansould not be computed for the Holter electrocardiographvailable as part of the DISPERSE2 study. We do, however,ote that MV differs from these methods in that it usesnformation from the entire heart beat signal and does notocus on any specific structure to the morphology changes.

e also consider the ability to measure MV on a Holterlectrocardiograph as a potential strength of the method,llowing it to complement other tests based on more spe-ialized equipment. Finally, in the present study, we did notave end points of unambiguous arrhythmic death availableo us. We believe that death in the period after NSTE-ACSs likely the result of fatal arrhythmias but are unable torove this hypothesis on the current dataset. We further notehat although we observed a strong association between MVnd death in our study, additional studies are warranted inarger cohorts that contain a larger number of events to moreully explore and validate any relationship between MV and

eath.

cknowledgment: We thank Gari Clifford for tools toreprocess the electrocardiograph and for input on HRVetrics and Dorothy Curtis for computational resources.e also thank the reviewers and editor for their feedback on

mproving the presentation of this work.

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