lable at ScienceDirect

Heart & Lung 43 (2014) 516e526

Contents lists avai

Heart & Lung

journal homepage: www.heartandlung.org

Electrocardiogram-based predictors of clinical outcomes:A meta-analysis of the prognostic value of ventricular repolarization

Salah S. Al-Zaiti, PhD a,*, James A. Fallavollita, MDb,c, Yow-Wu B. Wu, PhD b,Machiko R. Tomita, PhD b, Mary G. Carey, PhD d

aUniversity of Pittsburgh, Pittsburgh, PA, USAb The State University of New York University at Buffalo, Buffalo, NY, USAc The VA of WNY Healthcare System, Buffalo, NY, USAd The Strong Memorial Hospital at University of Rochester Medical Center, Rochester, NY, USA

a r t i c l e i n f o

Article history:Received 30 October 2013Received in revised form3 May 2014Accepted 4 May 2014Available online 29 June 2014

Keywords:Meta-analysisRepolarizationElectrocardiogramMortality

Abbreviations: ECG, electrocardiogram; SCAE, sudlent; RR, risk ratio.Funding sources: this study is a part of doctoral d

supported by a Professional Development Award froDepartment and Graduate Student Employee Union oYork, and by a Research Award from Sigma Theta TaUniversity at Buffalo.* Corresponding author. University of Pittsburgh Sch

Street, 336 Victoria Building, Pittsburgh, PA 15261, Ufax: þ1 412 383 7227.

E-mail address: ssa33@pitt.edu (S.S. Al-Zaiti).

0147-9563/$ e see front matter � 2014 Elsevier Inc.http://dx.doi.org/10.1016/j.hrtlng.2014.05.004

a b s t r a c t

Objectives: To estimate age- and sex-specific prognostic values of eight electrocardiographic repolari-zation descriptors to predict various mortality endpoints.Background: Using electrocardiographic markers for risk stratification is well studied; however, theprognostic value of many markers is controversial, and their clinical utility remains debatable. No meta-analyses exist that address the prognostic value of ECG markers.Methods: Data were synthesized from 106 primary studies using a random-effect variance model. Ageand sex subgroups were analyzed using sensitivity analysis.Results: Four classic (i.e., duration, amplitude, inversion, and ST-T changes) and four novel (i.e., axis, loop,wavefront direction, and waveform complexity) repolarization descriptors were studied. These noveldescriptors were particularly useful in predicting sudden death. Abnormal repolarization duration,vectors, and loops have greater impact on negative cardiovascular outcomes in women compared tomen; additionally, ischemic repolarization changes have greater impact on negative cardiovascularoutcomes in younger versus older adults.Conclusions: Assessing repolarization abnormalities is particularly helpful in women and younger adults.Researchers need to further explore the clinical utility of these abnormalities in management algorithms.

� 2014 Elsevier Inc. All rights reserved.

Introduction

Cardiovascular disease in the United States is a nationwidehealth problem that not only burdens the U.S. healthcare system,but also accounts for the greatest number of deaths in the U.S. eachyear.1 The proper identification of those at greater risk for futurecardiac events would result in a change in therapy; therefore, suchrisk stratification is clinically warranted. The ECG is a simple tool

den cardiac arrest or equiva-

issertation that was partiallym New York State Educationf the State University of Newu Gamma Kappa Chapter of

ool of Nursing, 3500 VictoriaSA. Tel.: þ1 412 624 4369;

All rights reserved.

that can provide valuable information about disease pathophysi-ology among various cardiac populations. The last century wit-nessed a tremendous growth in ECG technology, which has led tothe great interest many researchers and clinicians have in using thisnon-invasive tool to risk stratify different clinical populations.2

Among all of the various ECG waveforms, the T wave wasrepeatedly shown to possess the greatest prognostic value, giventhat the vulnerable period for cardiac events primarily occursduring the strongest, local gradients of repolarization (e.g., phase 3action potential) and not in the PR or ST segments of the ECG.3,4

Earlier efforts to characterize repolarization signals focused onthe qualitative (i.e., visual) evaluation of the T wave on individualECG leads. However, such classic approaches primarily relied ontime domain measures (e.g., the precise detection of T-wave offset),which are associated with a wide range of technical difficulties andlead to the poor reproducibility of suchmarkers. To overcome thesetechnical challenges, later efforts focused on the quantitative(i.e., mathematical) evaluation of the global repolarization signalto provide more superior markers with greater objectivity andprecision.5 Compared to previous classical approaches, the new

Fig. 1. Characteristics of T wave morphology, vectors, and loops. (A) Reconstructed T eigenvalue vectors of common leads (I, II, V1eV6). These waveform complexity vectors arenecessary to construct the T loop. (B) Reconstructed R and T loops with mean vector axes showing the spatial wavefront direction.

S.S. Al-Zaiti et al. / Heart & Lung 43 (2014) 516e526 517

mathematical approaches characterized the morphology differ-ences between standard leads (i.e., spatial domain) and the changesin interlead relationships (i.e., temporal domain); both approachesassessed different ECG qualities that do not rely on the precisedetection of T-wave offset. Accordingly, the latter mathematicalmarkers were generally more reproducible and led to a more sig-nificant separation between normal and abnormal ECGs.5 Thesemarkers came to be referred to as the novel ECG markers (Fig. 1).The classic and novel repolarization markers that have been pub-lished in the literature are summarized in Table 1.

Recent findings from large, multicenter studies provide evi-dence that novel ECG markers can provide clinically importantfindings beyond those readily available for clinicians.6 To date,many clinicians are not familiar with spatial and temporal ECGdescriptors. Additionally, there remains no general consensus onthe proper use of such ECG risk markers in clinical practice, andthere are no meta-analyses concerning this topic. More important,the effect of age and sex on various ECG time-intervals (e.g., QRSduration, QT interval, ST segment) is well known and well-docu-mented.7e9 The most recent guidelines, for example, has beenexpanded to include age and sex specific criteria of acute STelevation in the context of myocardial infarction diagnosis andmanagement.10 Similar reference criteriawere established for otherimportant ECG parameters (i.e., QT prolongation).11 However, itremains unclear how age and sex differences impact the clinicalutility of novel ECG risk markers that rely on spatial and temporal

Table 1Description of classic and novel repolarization parameters.

Repolarization marker Definition

Classic markers These are markers that rely on the qualitative (visual) e1. T wave duration The evaluation of JT or T peak-end intervals. JT interval

QT interval minus Q-T peak.2. T wave amplitude The evaluation of T peak amplitude in various leads. An

in leads III or aVL; �10 mm in leads V2eV4; �7.5 mmin leads V5eV6; or �1.8 mm in lead V5.

3. T wave inversion The evaluation of the direction of T peak in various leadaVF, V2eV6 (Minnesota code 5).

4. Non-specific ST-Tchanges

The evaluation of the morphological changes in the ST(elevation or depression) with or without T changes (in

Novel markers These are global markers that rely on the quantitative ewhich does not depend on the precise determination o

5. T wave axis This includes computing the spatial direction of the me6. T loop morphology This includes computing the mathematical characterist

orthogonal T wave eigenvectors (Fig. 1). T loop charactperiphery of the T loop and both sides of the preferentiof the corresponding loop).

7. Wavefront direction This includes computing the magnitude of deviation beusually used: (1) QRS-T angle (QRSTA) as the spatial orR-to-T (TCRT) as the deviation between R and T loops i

8. Waveform complexity This includes the principal components analysis (PCA) oratio as the dipolar content of T wave (ratio between thof T wave (ratio between the five lowest T wave eigenv

qualitiesdrather than time qualitiesdof the standard ECG. In fact,some studies suggest that spatial ECG qualities are affected by sexdifferences as well,12 which warrant further evaluation of theimpact of such demographic confounders on the prognostic valueof novel ECG markers.

Published studies are largely heterogenous in their design andmethods, resulting in controversial findings about the magnitudeand clinical value of different ECG risk markers. The contradictorynature of these findings has resulted in some questions regardingthe value of the ECG.13,14 A classic example in the ECG literature isQT interval dispersion, which involves individual inter-lead differ-ences in the QT interval seen on the standard ECG. This risk markerwas first proposed as a simple global measure of abnormal repo-larization.15,16 However, later reports discouraged its use17e19 anddemonstrated that such lead differences were merely a machinemeasurement error in most cases.20e22 The use of prospectiveECG risk markers, therefore, requires thorough clinical testingand evaluation prior to any clinical practice recommendations.Although the amount of literature on recent novel markers is hugeand rapidly continues to grow, it is still unknown whether theseECG risk markers can provide valuable prognostic information invarious clinical populations and whether age and sex differencescan impact their clinical utility. Therefore, the primary purpose ofthis meta-analysis of published literature was to answer twoquestions: (1) What is the overall risk ratio (RR) of different repo-larization descriptors to predict various mortality endpoints? (2)

valuation of the ECG in individual leads.is the max QRS offset to T offset, whereas T peak-end interval is the max

abnormal amplitude is defined as �5 mm in leads I, II, aVF, V1, or V6; �2.5 mmin lead V5; �0 mm in lead aVR; �0 mm in lead I, �1 mm in lead II; �2.35 mm

s. An abnormal direction is defined as T peak �0 mm in leads I-III, aVL,

segment and T wave. An abnormal morphology includes ST deviationverted or biphasic) (Minnesota code 4).valuation of repolarization signal derived simultaneously from all leads,f the onset or the offset of the T waveform.an T vector axis in 3-D space.ics of the T loop that is generated by projecting the XYZ median beats onto theeristics include: (1) loop area; (2) loop vector length (mean distance between theal plane); (3) loop morphology dispersion (area containing a part of the border

tween depolarization and repolarization waveforms directions. Two indices arefrontal angle between the reconstructed mean R and T vectors; (2) total cosinen the 3-D plane.f the median T waves of the ECG. Two indices are usually used: (1) complexitye three largest T eigenvalues); (2) T wave residua as the non-dipolar contentalues).

S.S. Al-Zaiti et al. / Heart & Lung 43 (2014) 516e526518

How do age and sex interact with the prognostic value of theserepolarization descriptors? Answers to these questionsmight makeit possible to develop recommendation insights for future researchand for the proper use of ECG for risk stratification in clinicalpractice.

Methods

Search strategy for primary studies

To ensure retrieval of all relevant reports and studies, a com-prehensive search strategy was employed using searches in elec-tronic databases and relevant journals, hand searches for milestonetrials, and ancestry searches of reference lists of eligible primarystudies. Web of Science, Medline, PubMed, and CINAHL registrieswere searched using the following keywords: mortality, follow up,prediction, risk stratification, T wave, heterogeneity, repolarization,loop morphology, and electrocardiogram. Because ECG studies tendto appearmore often in specialized journals, specific searches usingsimilar and additional key words were conducted in 13 selectedcardiovascular journals, which included Circulation, Heart Rhythm,and European Heart Journal. All searches were conducted by anexperienced research scientist. Titles and abstracts of all researchreports resulted from this search (n ¼ 1487) were screened foreligibility, and full-texts of relevant primary studies (n ¼ 143) wereretrieved for further evaluation and coding.

Inclusion criteria of primary studies

Methods for the precise determination of T wave offset werefirst introduced in 1999.5 Therefore, eligible primary studies had tobe published (peer-reviewed) in the English language within thepast 15 years. The research design could be either a randomizedclinical trial or an observational cohort study. Eligible studies had atleast one T wave descriptor and one endpoint outcome. There wereno restrictions on age limits, sex distribution, or the investigateddisease population included in primary studies.

Description of endpoints

Three primary mortality endpoints were studied in this meta-analysis:

1. Sudden Cardiac Arrest or Equivalent (SCAE) was defined as (a)unexpected death within 24 h of having been observed aliveand symptom free or within 1 h after the onset of symptomswithout any defined cause of death or (b) the presence ofresuscitated or non-resuscitated fatal tachyarrhythmia (ven-tricular fibrillation).23,24

2. Cardiovascular death was defined (a) according to the 9threvision of International Classification of Disease codes 390 to459 or 10th revision codes 100 to 199 or (b) as death due tofatal or non-fatal myocardial infarction.25

3. All-cause death was defined as the cause recorded on deathcertificates, in medical records, or in national registries.26

Data coding & checking

A coding scheme that includes the following three categorieswas developed, revised, and validated by three research scientists:(1) study and sample characteristics; (2) methods and designcharacteristics; (3) effect size (ES) computation. The coding schemeoutlined a standardized approach to compute total effect sizes from

reported risk ratio, logged ratio, Cohen’s d, or reconstructed 2 � 2tables.27 The final coding scheme was pilot tested on a randomset of primary studies (n ¼ 5) by two independent coders, and thefinal agreement on effect size values between coders was high(i.e., 89%). All discrepancies were resolved through consensusamong the coders. All eligible primary studies were coded byexperienced research scientists with periodic data quality checksand management.

If more than one ECG predictor and/or endpoint was reported ina single primary study, then a separate ES was calculated for eachpredictor. If outcomes were reported at different time intervals offollow up in the same study (which could be in the same publica-tion or in separate publications), then only the final follow-upoutcomes were used. Studies reporting on overlapping sampleswere only included if they reported different predictors and/orendpoints. The quality of eligible primary studies was blindlyevaluated using QUADAS (Quality Assessment of Diagnostic Accu-racy Studies), which was previously validated for use in systematicreviews.28 Eligible studies had to have a quality score of greaterthan or equal to 75%.

Statistical analysis

The risk ratio (RR) is a total and stable measure of hazardfunctions and, therefore, was used as the primary outcome mea-sure in our meta-analysis. Studies that reported different outcomemeasures (i.e., the odds ratio or Cohen’s d) had these measuresconverted to RR as previously described.29,30 All mathematicalcomputations were calculated using the log of RR to satisfy normaldistribution assumptions.27 Since the confidence interval (or vari-ance) of each outcome measure resembles the precision of theestimate as an indirect measure of sample size, primary studieswere weighted by the inverse of their variance. To account forknown methodological differences among included studies, theheterogeneity of RR measures was treated as a covariate in thestatistical pooling (i.e., random-effect model). A minimum pooledsample size of 1865 subjects was necessary to detect an event rateof 10%, when RR ¼ 2.0, and type I and II errors were 5% and 20%,respectively. This assumption was satisfied in 22 out of 24 total RRestimates calculated in our meta-analysis.

To evaluate the extent of methodological dissimilarities amongstudies, sensitivity analysis (i.e., Ǫ statistic) was conducted. Thisanalysis assesses whether or not the variation in effect sizes amongpooled studies falls within expected sampling error. If thisassumption was violated, then age and sex subgroup moderatoranalysis, which performs the analog to one-way ANOVA, wasconducted. Primary studies were coded as either (1) youngerpopulation (mean age < 65 years) or older population (meanage � 65 years) or (2) male-dominant (�80% men) or female-dominant (�80% women). Finally, to evaluate publication biasstemming from missing studies or search limitations, we used thetrim-and-fill funnel plot method.31 This method evaluates for theasymmetry of distribution of studies around their weighted mean.All analyses were performed using Statistical Package for SocialSciences (SPSS 19.0, IBM Statistic, IL) andMeta-Analysis in Excel (MIX2.0 Pro, BiostatXL, NJ).

Results

Characteristics of primary studies

Of 143 full-texts reviewed, (1) 12 studies discussed duplicatefindings from the same samples, (2) nine studies did not haveadequate data to calculate the ES, and their authors were unavai-lable, (3) 10 studies had a mean quality score of less than 75%, and

Fig. 2. Search strategy for inclusion of primary studies included in the analysis. Thechart shows the number of articles resulting from the literature search of all sources; italso shows the final selection of primary studies included in this analysis.

S.S. Al-Zaiti et al. / Heart & Lung 43 (2014) 516e526 519

(4) six studies had a predictor (e.g., early repolarization) or outcome(e.g., re-hospitalization) that was irrelevant (Fig. 2). After excludingthese studies, 106 studies were included in our meta-analysis.

Most studies were funded (62%), used standard 12-lead ECG(95%), and performed the analysis in a blinded fashion (85%). Therewere 18 (16%) randomized clinical trials and 88 (84%) observationalstudies. Less than half (42%) of the studies used digital ECG pro-cessing, and 41% were performed in Europe and 33% in NorthAmerica. Using the QUADAS tool to evaluate designmethodology ofprimary studies, themean quality score of all studies was calculatedas 88 � 9% (range 75e100%). Eligible primary studies are summa-rized in Appendix 1.

Characteristics of the pooled sample

The pooled sample size included 349,756 patients with a meanage of 57 years. Men and women were equally presented in thesample, which was predominantly composed of whites (Table 2).Nearly one half (55%) of the sample included patients with differentcardiac diseases (e.g., coronary artery disease, heart failure, andcardiomyopathy), and the other half was composed of other

Table 2Baseline characteristics of the pooled sample (n ¼ 349,756).

Sample characteristics n (%)

Disease populationGeneral healthy population 107,375 (30.7%)Cardiac population 192,016 (54.9%)Non-cardiac population 50,365 (14.4%)

SexMale 178,725 (51.1%)Female 164,735 (47.1%)Not reported 6296 (1.8%)

RaceWhite 111,572 (31.9%)Black 13,640 (3.9%)Not reported 224,544 (64.2%)

HypertensionYes 88,838 (25.4%)No 146,547 (41.9%)Not reported 114,371 (32.7%)

DiabetesYes 31,128 (8.9%)No 209,503 (59.9%)Not reported 109,125 (31.2%)

Active smokingYes 69,252 (19.8%)No 166,833 (47.7%)Not reported 113,671 (32.5%)

clinical, non-cardiac patients (e.g., having subarachnoid hemor-rhage or renal failure) or those from the non-specific generalpopulation. In addition, other traditional risk factors (i.e., hyper-tension, diabetes, and smoking) were present in less than onequarter of the pooled sample.

Overall prognostic value of repolarization descriptors

The overall summary of the total RR for each repolarizationparameter is presented in Fig. 3. Almost all assessable repolariza-tion parameters were predictive of sudden and non-sudden car-diovascular death, with RR estimates equal to or greater than 2.0folds. Novel repolarizationmarkers weremore predictive of suddendeath compared to other classic markers. In contrast, both classicand novel markers were poorly predictive of all causes of death,with RR estimates less than 2.0 folds. Of note, the computation ofthe prognostic value of each repolarization parameter includedonly a subset of the pooled sample size for which outcome datawere available. Fig. 4A shows an example of a single, sub-meta-analysis that included 17 primary studies, which examined thepredictive value of cardiovascular death using T wave inversion.

The moderator effects of age and sex on the prognostic valueof different repolarization descriptors are presented in Table 3.Overall, the results suggest that negative cardiovascular outcomesare influenced by the age and sex of patients, specifically (1) al-terations in T duration, inversion, loop morphology, and waveformcomplexity are particularly important in women, (2) alteration in Tamplitude, inversion, and NSSTTchanges are particularly importantin younger adults, (3) alterations in Taxis are particularly importantin older men, and (4) wavefront direction is least influenced by ageand sex. Fig. 4B shows an example of a subgroup analysis of a single,sub-meta-analysis. In this single example, negative cardiovascularoutcomes associated with inverted T waves are more pronouncednot only in younger adults compared to older adults, but also inwomen compared to men.

Finally, publication bias was observedmainly in the literature onnovel repolarization descriptors (10 out of 11 analyses). Thisobserved discrepancy between individual primary studies in theliterature concerning novel markers largely can be attributed to thelack of estimate precision in many studies that included smallsample sizes. Fig. 5 shows an example of the trim-and-fill funnelplot with its proposed mathematical correction. In this singleexample, it seems as though the cardiovascular risk associated withabnormal wavefront direction might be influenced through biasfrom less rigorous studies, and the true risk incurred might belower than observed. Because there is no general consensus for theevaluation and correction of publication bias, our current resultsneed to be interpreted with caution.

Discussion

In this meta-analysis, we pooled data from 106 primary studiesto estimate the overall RR of various repolarization descriptors.The greatest prognostic value was observed when using novelrepolarization descriptors to predict SCAE, specifically measuresof T axis, loop morphology, and wavefront direction. T axis devia-tion was previously described as a simple marker of subclinicalmyocardial damage and global repolarization abnormality.32 Dis-tortions in T loop morphology reflect T wave shape dissimilarity inindividual leads, which resembles global repolarization disper-sion.33 Altered wavefront direction reflects distorted propagationpattern between depolarization and repolarization waves and,therefore, resembles heterogenous repolarization.34 All theseelectrophysiological alterations can induce intramural reentry andaction potential alternans in diseasedmyocardium, which resemble

Fig. 3. Overall prognostic value of repolarization descriptors. This figure represents total risk ratio (RR) estimates for each repolarization descriptor to predict various clinicaloutcomes. ‘k’ denotes the number of studies in each sub meta-analysis; ‘n’ denotes the pooled sample size in that sub meta-analysis.

S.S. Al-Zaiti et al. / Heart & Lung 43 (2014) 516e526520

the pathophysiological mechanism of fatal tachyarrhythmias.35

Other classic pathological mechanisms such as prolonged repolar-ization (i.e., T duration)36 and ischemic myocardial changes (i.e.,T inversion and ST-T changes)35,37 in this analysis were still pre-dictive of SCAE but less predictive than other novel repolarizationdescriptors.

Most repolarization descriptors were modestly predictive ofcardiovascular death; moreover, there were no distinct differencesbetween classic and novel parameters in terms of their prognosticvalue to predict cardiac (i.e., non-arrhythmic) events. Interestingly,most repolarization parameters had poor clinical value (i.e.,RR < 2.0) to predict all-cause death. This in part makes sensebecause repolarization abnormalities reflect underlying cardiacdisease, which contributes to disease-specific death rather than all-cause death. Additionally, the statistical significance usuallyobserved in composite endpoints might be highly attributable to

Fig. 4. A selected example of a single sub-meta-analysis. (A) shows a synthesis forest plot foreffect of age and sex on the total risk ratio estimate. * denotes statistical difference betwee

disease-specific mortality in the combined endpoint,38 whichjeopardizes the validity of all-cause death as an endpoint categoryin biomedical research.

On the other hand, the effects of age and sex on the prognosticmagnitude of different repolarization parameters were quiteevident in the subgroup analysis of this meta-analysis. Age and sex-specific interpretation of the ECG have been repeatedly empha-sized.7,8,11,39 Our analysis suggests that alterations in repolarizationduration, vectors, and loops have greater impact on women thancompared to men. This finding is important, given the increasedprevalence of cardiovascular risk factors among women globally.40

Unlike men, women tend to exhibit a different blood clottingmechanism that triggers myocardial infarction through plaqueerosion and a state of hypercoagulability.41 Our finding suggeststhat more research into this phenomenon would be prudent. Ouranalysis also suggests that ischemic myocardial changes have

estimating total risk ratio of cardiovascular death using T wave inversion. (B) shows then groups.

Table 3Risk ratio estimates for age and sex subgroup analysis.b

T wave parameter Endpoint

SCAE Cardiovascular death All-cause death

Age Sex Age Sex Age Sex

<65 �65 Men Women <65 �65 Men Women <65 �65 Men Women

T wave duration 1.9 2.1 1.8 3.0a 1.2 1.6 1.3 1.9a 1.6 1.5 1.5 1.6T wave amplitude e e e e 3.0a 1.2 3.3 3.0 2.1a 1.7 1.9 2.2T wave inversion e e 1.8 2.7a 2.2a 1.6 1.7 2.4a 1.3 1.2 1.3 1.2NSSTT changes e e e e 2.3a 1.9 2.1 2.2 1.8a 1.5 1.7 1.5T axis e e e e 1.8 2.1 2.5a 1.1 1.4 2.3a 1.9a 1.3T loop morphology e e e e 1.5 1.7 1.4 2.8a 1.5a 1.1 1.2 1.3Wavefront direction 3.0 2.8 e e 2.1 2.0 1.8 1.9 1.8 1.8 1.8 1.8Waveform complexity e e e e 1.5 1.5 1.4 1.8a 1.3 1.3 1.3 1.3

NSSTT: non-specific ST-T changes. Empty cells indicate inadequate sample size.a Denotes significant differences between subgroups.b Comparisons between subgroups were done using sensitivity moderator analysis (analog to one-way ANOVA). Each comparison included only a subset of studies for

which outcome data were available. A minimum of two studies was required in each subgroup for completing the sensitivity analysis.

S.S. Al-Zaiti et al. / Heart & Lung 43 (2014) 516e526 521

greater impact on younger compared to older adults. This finding iscongruent with our knowledge that the poor collateral circulationin younger adults predisposes them to less favorable outcomesduring life-threatening ischemic events.

Limitations

As with any meta-analysis, methodological differences amongpooled studies make it difficult to draw precise conclusions;therefore, our results need to be considered suggestive rather thanconclusive. The reasons are as follows. First, although we assessed106 studies in this meta-analysis, this was still insufficient tocalculate reliable estimates for predicting SCAE using T waveamplitude or waveform complexity. Second, publication bias wasevident in many of the studies assessed in this meta-analysis, andthe commonly used methods for such correction are not ideal,warranting not only further caution in the interpretation of ourresults, but also further research on the topic. Third, because ourage and sex subgroup analysis does not draw on clinical data onwhich to base biological speculation, our results, therefore, shouldalso be considered suggestive rather than conclusive. Finally, moststudies included in our meta-analysis not only computed ECGmarkers from a single point in time, but also used that single time

Fig. 5. Trim and fill funnel plot of selected T wave parameters. This figure shows aselected example of publication bias in estimating cardiovascular death using wave-front direction. The correction is made using the trim-and-fill correction method,which evaluates for the asymmetry of study distribution around their weighted mean.‘md’ denotes original unadjusted risk ratio estimate; ‘m`d’ denotes suggested correctionto that risk ratio estimate; precision refers to the inverse of standard error.

point to predict clinical outcomes. Because there is no clear rule ofthumb as to what point in time is required to withdraw thenecessary prognostic information from ECG, additional research isneeded to shed light on this issue.

Conclusions and recommendations

This meta-analysis demonstrates that ECG repolarization pa-rameters are useful tools in predicting arrhythmic and cardiovas-cular death. Novel repolarization descriptors can provide moreinsight into proper disease-specific risk assessment, especially forSCAE risk stratification, which is very useful in the proper targetingof anti-arrhythmic drugs and implantable-cardioverter defibrillatortherapies in selected patients to prevent arrhythmic death. More-over, our analysis demonstrates that women and younger adults areat a particularly greater risk of cardiac events when they presentwith repolarization abnormalities on their 12-lead ECG. This war-rants further investigation of these subgroups, especially womenwho tend to have atypical presentations. In addition, our analysisshows the need for further investigation of the interaction betweenrepolarization abnormalities and patient demographics, whichsuggests that clinicians should use a risk assessment approachthat is both age and sex specific. Researchers also need toexplore the clinical utility of novel ECG markers in patient carealgorithms, which will validate the clinical utility of these markersand provide data on the clinical the impact on patient outcomes.Finally, future research needs to not only focus more specificallyon disease-specific endpointsdrather than an “all-cause death”endpointdas a primary objective in outcome studies, but also de-termines the proper point in time to complete such necessary riskassessment.

Acknowledgments

None.Conflicts of interest: None.

References

1. Lloyd-Jones D, Adams RJ, Brown TM, et al, Committee obotAHAS, Stroke Sta-tistics Subcommittee. Heart disease and stroke statisticse2010 update: a reportfrom the American Heart Association. Circulation. 2010;121:e46ee215.

2. Macfarlane P, Lawrie V. Comprehensive Electrocardiology, Theory and Practice inHealth and Disease. New York, USA: Pergamon Press; 1989.

3. Yan G-X, Lankipalli RS, Burke JF, Musco S, Kowey PR. Ventricular repolarizationcomponents on the electrocardiogram: cellular basis and clinical significance.J Am Coll Cardiol. 2003;42:401e409.

S.S. Al-Zaiti et al. / Heart & Lung 43 (2014) 516e526522

4. Al-Zaiti SS, Runco KN, Carey MG. Increased T-wave complexity can indicatesubclinical myocardial ischemia in asymptomatic adults. J Electrocardiol.2011;44:684e688.

5. Acar B, Yi G, Hnatkova K, Malik M. Spatial, temporal and wavefront directioncharacteristics of 12-lead t-wave morphology. Med Biol Eng Comput. 1999;37:574e584.

6. Lown MT, Munyombwe T, Harrison W, et al. Association of frontal QRS-Tangleeage risk score on admission electrocardiogram with mortality in pa-tients admitted with an acute coronary syndrome. Am J Cardiol. 2012;109:307e313.

7. Macfarlane PW. Age, sex, and the ST amplitude in health and disease.J Electrocardiol. 2001;34:235e241.

8. Macfarlane PW, McLaughlin SC, Devine B, Yang TF. Effects of age, sex, and raceon ECG interval measurements. J Electrocardiol 1994;(27 suppl):14e19.

9. Triola B, Olson MB, Reis SE, et al. Electrocardiographic predictors of cardio-vascular outcome in women: the National Heart, Lung, and Blood Institute-sponsored Women’s Ischemia Syndrome Evaluation (wise) study. J Am CollCardiol. 2005;46:51e56.

10. Bonaca MP, Wiviott SD, Braunwald E, et al. ACC/AHA/ESC/WHF universaldefinition of myocardial infarction classification system and the risk of car-diovascular death: observations from the triton-timi 38 trial (trial to assessimprovement in therapeutic outcomes by optimizing platelet inhibition withprasugrelethrombolysis in myocardial infarction 38). Circulation. 2012;125:577e583.

11. Rautaharju PM, Surawicz B, Gettes LS. AHA/ACCF/HRS recommendations forthe standardization and interpretation of the electrocardiogram: Part IV: the STsegment, T and U waves, and the QT interval. A scientific statement from theAmerican Heart Association Electrocardiography and Arrhythmias Committee,Council on Clinical Cardiology; the American College of Cardiology Foundation;and the Heart Rhythm Society endorsed by the International Society forComputerized Electrocardiology. J Am Coll Cardiol. 2009;53:982e991.

12. Zhang ZM, Prineas RJ, Case D, Soliman EZ, Rautaharju PM. Comparison of theprognostic significance of the electrocardiographic QRS/T angles in predictingincident coronary heart disease and total mortality (from the atherosclerosisrisk in communities study). Am J Cardiol. 2007;100:844e849.

13. Macfarlane PW, Norrie J. Looking for prognostic information in the ST-Tsegmentdis it really worth it? J Electrocardiol. 2004;37:209e213.

14. Carey M, Al-Zaiti S, Canty J, Fallavollita J. High-risk electrocardiographic pa-rameters are ubiquitous in patients with ischemic cardiomyopathy. AnnNoninvasive Electrocardiol. 2012;17:241e251.

15. Barr CS, Naas A, Freeman M, Lang CC, Struthers AD. QT dispersion and suddenunexpected death in chronic heart failure. Lancet. 1994;343:327e329.

16. Day CP, McComb JM, Campbell RW. QT dispersion: an indication of arrhythmiarisk in patients with long QT intervals. Br Heart J. 1990;63:342e344.

17. Gang YI, Ono T, Hnatkova K, et al, on behalf of the EIIi. QT dispersion has noprognostic value in patients with symptomatic heart failure: an ELITE II sub-study. Pacing Clin Electrophysiol. 2003;26:394e400.

18. Zabel M, Klingenheben T, Franz MR, Hohnloser SH. Assessment of QT disper-sion for prediction of mortality or arrhythmic events after myocardial infarc-tion : results of a prospective, long-term follow-up study. Circulation. 1998;97:2543e2550.

19. Zabel M, Malik M. Predictive value of T-wave morphology variables and QTdispersion for postmyocardial infarction risk assessment. J Electrocardiol.2001;34:27e35.

20. Goldberger JJ, Cain ME, Hohnloser SH, et al. AHA/ACC/HRS scientific statementon noninvasive risk stratification techniques for identifying patients at risk forsudden cardiac death: a scientific statement from the American Heart

Association Council on Clinical Cardiology Committee on Electrocardiographyand Arrhythmias and Council on Epidemiology and Prevention. Circulation.2008;118:1497e1518.

21. Kors JA, van Herpen G. Measurement error as a source of QT dispersion: acomputerised analysis. Heart. 1998;80:453e458.

22. Kautzner J, Gang Y, Kishore R, et al. Interobserver reproducibility of QT intervalmeasurement and QT dispersion in patients after acute myocardial infarction.Ann Noninvasive Electrocardiol. 1996;1:363e374.

23. Zheng Z-J, Croft JB, Giles WH, Mensah GA. Sudden cardiac death in the unitedstates, 1989 to 1998. Circulation. 2001;104:2158e2163.

24. Chugh SS, Jui J, Gunson K, et al. Current burden of sudden cardiac death:multiple source surveillance versus retrospective death certificate-based re-view in a large U.S. community. J Am Coll Cardiol. 2004;44:1268e1275.

25. National Center for Health Statistics. Classification of Diseases, Functioning, andDisability; 2012.

26. Heron M. Deaths: leading causes for 2007. Natl Vital Stat Rep. 2011;59:1e95.27. Tierney J, Stewart L, Ghersi D, Burdett S, Sydes M. Practical methods for

incorporating summary time-to-event data into meta-analysis. Trials. 2007;8:16.

28. Whiting P, Rutjes A, Reitsma J, Bossuyt P, Kleijnen J. The development ofQUADAS: a tool for the quality assessment of studies of diagnostic accuracyincluded in systematic reviews. BMC Med Res Methodol. 2003;3:25.

29. Zhang J, Y KF. What is the relative risk?: a method of correcting the odds ratioin cohort studies of common outcomes. J Am Med Assoc. 1998;280:1690e1691.

30. Cooper H, Hedges L, Valentine J. The Handbook of Research Synthesis and Meta-analysis. New York, NY.: Russel Sage Foundation; 2009.

31. Cooper H. Research Synthesis and Meta-analysis: A Step-by-step Approach. 4thed. Thousand Oaks, CA: SAGE Publications, Inc.; 2010.

32. Kors JA, de Bruyne MC, Hoes AW, et al. T axis as an indicator of risk of cardiacevents in elderly people. Lancet. 1998;352:601e605.

33. Aigars R, Lennart B, Lars R, Jens J. Prediction of cardiovascular death andmyocardial infarction by the QRS-T angle and T vector loop morphology afterangioplasty in stable angina pectoris: an 8-year follow-up. J Electrocardiol.2010;43:310e317.

34. Huang H-C, Lin L-Y, Yu H-Y, Ho Y-L. Risk stratification by T-wave morphologyfor cardiovascular mortality in patients with systolic heart failure. Europace.2009;11:1522e1528.

35. Bernus O, Zemlin CW, Zaritsky RM, Mironov SF, Pertsov AM. Alternating con-duction in the ischaemic border zone as precursor of reentrant arrhythmias: asimulation study. Europace. 2005;7:S93eS104.

36. Panikkath R, Reinier K, Uy-Evanado A, et al. Prolonged Tpeak-to-tend intervalon the resting ECG is associated with increased risk of sudden cardiac death.Circ Arrhythm Electrophysiol. 2011;4:441e447.

37. Cuddy T, Tate R. Sudden unexpected cardiac death as a function of time sincethe detection of electrocardiographic and clinical risk factors in apparentlyhealthy men: the Manitoba follow-up study, 1948 to 2004. Can J Cardiol.2006;22:205e211.

38. Black WC, Haggstrom DA, Gilbert Welch H. All-cause mortality in randomizedtrials of cancer screening. J Natl Cancer Inst. 2002;94:167e173.

39. Bidoggia H, Maciel JP, Capalozza N, et al. Sex-dependent electrocardiographicpattern of cardiac repolarization. Am Heart J. 2000;140:430e436.

40. Yusuf S, Hawken S, Ôunpuu S, et al. Effect of potentially modifiable risk factorsassociated with myocardial infarction in 52 countries (the INTERHEART study):case-control study. Lancet. 2004;364:937e952.

41. Vaccarino V, Parsons L, Every NR, Barron HV, Krumholz HM. Sex-based dif-ferences in early mortality after myocardial infarction. N Engl J Med. 1999;341:217e225.

Author (year) Study name and reference Disease population n Follow upa T waveparameter

Endpoint

Aigars (2010) e

J Electrocardiol, 43(4):310e7CAD 187 8.00 Tpeak-Tend, QRSTA, T loop CV

Anastasiou-Nana (2000) e

Am J Cardiol, 85(10):1212e7CHF 104 1.67 JT interval SCD, CV

Angeli (2010) PIUMAMaturitas, 67(2):159e65

HTN 908 8.60 STT ALL

Anttila (2011) FHSAm J Cardiol, 108(12):1735e40

General population 6354 8.20 T amplitude CV, ALL

Aro (2012) CHDCirculation, 125(21):2572e7

General population 10,899 30.00 TWI SCD, CV, ALL

Aro (2012) CHDEuropace, 14(6):872e6

General population 10,957 30.00 QRSTA, T axis SCD, CV, ALL

Atar (2007) GUSTO-IIbAm J Cardiol, 99(7):934e8

NSTEMI 6770 1.00 STT ALL

Bacquer (1998) BIRNAHHeart, 80(6):570e7

General population 9117 10.00 STT, TWI CV

Bacquer (1998) BIRNAHJ Am Coll Cardiol, 32(3):680e5

General population 9954 10.00 TWI ALL

Badheka (2012) NHANES IIIAm J Cardiol, 110(4):521e5

General population 4426 13.50 STT, TWI CV, ALL

Barrabes (2000) e

J Am Coll Cardiol, 35(7):1813e9NSTEMI 432 0.08 TWI CV

Batchvarov (2003) e

PACE, 26(1p2):373e6Post AMI 681 2.93 TCRT CV

Batchvarov (2004) e

Clin Cardiol, 27(11):653e9Post AMI 334 3.42 TCRT SCD, CV

Beckerman (2005) e

Ann Noninvasive Electrocardiol, 10(2):146e51Clinical population 31,074 6.00 STT, TWI CV

Boersma (2000) PURSUITCirculation, 101(22):2557e67

UA/NSTEMI 9461 0.08 TWI ALL

Borleffs (2009) e

Circ Arrhythm Electrophysiol, 2(5):548e54CAD 412 1.83 QRSTA SCD, ALL

Cannon (1997) TIMI-IIIJ Am Coll Cardiol, 30(1):133e40

UA/NSTEMI 1416 1.00 TWI ALL

Corbalan (1999) GEMIAm Heart J, 138(3):533e9

AMI 967 0.08 TWI ALL

Crow (1997) MRFITAm J Cardiol, 80(2):138e44

Apparently healthy 12,866 16.00 STT CV, ALL

Cuddy (2006) MFUSCan J Cardiol, 22(3):20511

Healthy veterans 3983 56.00 STT, TWI SCD

Damman (2012) FRISC/ICTUS/RITA 3Am J Cardiol, 109(1):6e12

UA/NSTEMI 5420 5.00 TWI CV

Das (2009) e

Am J Cardiol, 104(12):1631e7ACS 896 2.83 TWI ALL

Daviglus (1998) CWESJAMA, 281(6):530e6

General population 1673 29.00 STT CV, ALL

Dib (2009) e

Am Heart J, 157(5):933e8Cardiomyopathy 105 2.50 TWI ALL

Fu (1997) e

Eur Heart J, 18(2):281e9CHF 183 2.17 JT interval SCD, CV

Gang (2003) ELITE-IIPACE, 26(1p2):394e400

HF 986 1.50 JT interval SCD, CV, ALL

Giannopoulos (2009) e

J Electrocardiol, 42(1):79e84Post AMI 151 1.00 QRSTA CV

Gorodeski (2009) e

Am Heart J, 158(1):61e70ETT referrals 18,964 10.70 T amplitude ALL

Granger (2003) GRACEArch Intern Med, 163(19):2345e53

STEMI/NSTEMI 11,389 0.08 TWI ALL

Greenland (2003) CHADPIAm J Cardiol, 91(9):1068e74

General population 17,615 22.00 STT, TWI CV, ALL

Harrmark (2009) e

J Electrocardiol, 42(6):555e60STEMI 101 1.83 Tpeak-Tend ALL

Hart (1997) WOSCOPSJ Epidemiol, 26(3):508e15

General population 15,411 15.00 STT CV

Hevia (2006) e

J Am Coll Cardiol, 47(9):1828e34Brugada syndrome 29 3.55 Tpeak-Tend SCD

(continued on next page)

Appendix 1. Summary of primary studies included in the meta-analysis (n [ 106)

S.S. Al-Zaiti et al. / Heart & Lung 43 (2014) 516e526 523

Appendix 1 (continued )

Author (year) Study name and reference Disease population n Follow upa T waveparameter

Endpoint

Ho (2002) e

Mayo Clinic, 77(6):515e21Chest pain 2255 6.9 STT CV

Hochrein (1998) GUSTO-IAm J Cardiol, 81(9):1078e84

STEMI 3317 0.08 T amplitude ALL

Holmvang (1998) TRIMCirculation, 98(19):2004e9

UA/NSTEMI 502 0.08 STT CV

Holmvang (1999) TRIMAm J Cardiol, 83(5):667e74

UA/NSTEMI 308 0.08 TWI ALL

Huang (2009) e

Am J Emerg Med, 30(5):732e6HF 650 2.70 TCRT, TMD CV, ALL

Huang (2009) e

Europace, 11(11):1522e8SAH 222 0.08 T amplitude, STT, TWI ALL

Hyde (1999) e

Am J Cardiol, 84(4):379e85NSTEMI 353 4.30 TWI ALL

Jacobsen (2005) FRISC IIEur Heart J, 26(2):112e8

NSTEMI 1609 1.00 T amplitude ALL

Jernberg (1999) FASTJ Am Coll Cardiol, 34(5):1413e9

Chest pain 630 0.50 TWI CV

Jernberg (2002) FASTAm Heart J, 144(5):804e10

Chest pain 710 3.33 TWI ALL

Kawaski (2002) e

Circulation J, 66(6):567e70SAH 122 0.08 T amplitude, TWI ALL

Kentta (2011) FCSEuropace, 13(5):701e7

CAD 1297 3.75 TCRT, QRSTA SCD, CV, ALL

Kesek (2004) e

PACE, 27(10):1378e87Chest pain 800 2.92 STT, TWR ALL

Korhonen (2009) e

Europace, 11(4):514e20Post AMI 158 4.17 TCRT, PCA, T loop SCD, ALL

Kors (1998) RotterdamLancet, 352(9128):601e5

Healthy elderly 5815 4.00 T loop SCD, CV

Kors (1998) RotterdamJ Electrocardiol, 31(S1):54e9

Healthy elderly 5781 4.00 T axis SCD, CV, ALL

Kors (2004) RotterdamJ Electrocardiol, 37(S1):198e200

Healthy elderly 6134 6.70 PCA CV

Kumar (2008) CHSCirculation, 118(25):2790e6

Clinical population 3224 15.10 STT SCD, CV, ALL

Lancellotti (2002) e

Am J Cardiol, 90(8):833e7STEMI 147 5.00 TWI CV

Larsen (2002) CCHSEur Heart J, 23(4):315e24

General population 10,982 21.00 STT, TWI CV

Lin (2007) e

Nephrol Dial Transpl, 22(9):2645e52Hemodialysis 325 2.13 TCRT, TWR, T loop SCD, CV, ALL

Lipton (2010) e

Coron Artery Dis, 21(1):26e32Clinical population 2347 7.00 QRSTA CV, ALL

Lown (2012) EMMACEAm J Cardiol, 109(3):307e13

ACS 2393 2.00 QRSTA, T axis ALL

Lubinski (2000) PACE, 23(11p2):1957e9 Post AMI 34 NA Tpeak-Tend SCDMacfarlane (2007) WOSCOPS

J Electrocardiol, 40(1):101e9Post AMI 6595 4.90 STT, T axis, T amplitude CV, ALL

Malik (2004) e

J Electrocardiol, 37(S1):201e8Post AMI 466 5.00 TCRT SCD, ALL

Miller (2001) e

Am J Cardiol, 87(9):1045e50AMI 907 5.00 TWI ALL

Mueller (2004) e

Am J Med, 117(3):145e50UA/NSTEMI 1450 3.00 TWI ALL

Norgard (1999) TRIMJ Am Coll Cardiol, 33(6):1519e27

UA/NSTEMI 230 0.08 STT CV

Oikarinen (2004) LIFEHypertension, 43(5):1029e34

HTN 5429 4.90 STT CV, ALL

Okin (2002) Strong HeartCirculation, 105(6):714e9

Healthy elderly 1839 3.70 PCA CV

Okin (2004) Strong HeartDiabetes, 53(2):434e40

Type II diabetes 994 4.70 PCA ALL

Okin (2004) LIFEHypertension, 44(1):48e54

HTN 8854 5.00 STT CV, ALL

Okin (2005) Strong HeartJ Cardiovasc Electr, 16(9):945e51

Healthy elderly 1729 4.80 TCRT, PCA, TWR, T loop CV, ALL

(continued on next page)

S.S. Al-Zaiti et al. / Heart & Lung 43 (2014) 516e526524

Appendix 1 (continued )

Author (year) Study name and reference Disease population n Follow upa T waveparameter

Endpoint

Olson (2002) EDCAnn Epidemiol, 12(5):331e7

Type I diabetes 658 10.00 STT ALL

Panikkath (2011) OSUDSCirc Arrhythm Electr, 4(4):441e7

Clinical population 695 NA Tpeak-Tend SCD

Pavri (2008) DEFINITECirculation, 117(25):3181e6

Cardiomyopathy 455 2.48 QRSTA SCD, ALL

Pei (2012) e

Europace, 14(8):1180e7CHF 1570 3.00 Tpeak-Tend SCD, CV, ALL

Perkiomaki (2002) THROMBOAm J Cardiol, 90(3):205e9

Post AMI 1034 2.10 STT, TWI CV

Perkiomaki (2006) MR-FATAm J Cardiol, 97(3):353e60

Post AMI 437 3.58 TCRT, T loop CV, ALL

Peters (1999) e

Int J Cardiol, 71(3):243e50Cardiomyopathy 121 NA TWI SCD

Porthan (2009) FHSHeart Rhythm, 6(8):1202e8

General population 5917 5.90 TCRT, PCA, TWR, T loop CV, ALL

Prineas (2002) MRFITAm J Cardiol, 90(12):1391e5

General population 11,797 18.00 TWI, T axis CV, ALL

Rautaharju (2001) CHSAm J Cardiol, 88(2):118e23

Healthy elderly 4173 7.40 T axis CV, ALL

Rautaharju (2006) CHSAm J Cardiol, 97(3):309e15

Healthy elderly 4912 9.10 QRSTA CV, ALL

Rautaharju (2006) WHICirculation, 113(4):473e80

Postmenopausal women 38,283 9.20 STT, T amplitude, QRSTA,PCA, TWR

CV

Rumana (2011) NIPPON-DATAAm J Cardiol, 107(12):1718e24

General population 8572 21.20 STT CV

Salles (2003) e

Circulation, 108(3):305e12Chagas’ disease 738 4.83 Tpeak-Tend, STT ALL

Salles (2004) e

Am J Cardiol, 93(9):1136e40Chagas’ disease 738 4.83 T axis SCD, CV, ALL

Salles (2009) e

Hypertension, 27(5):1094e101HTN 538 4.80 Tpeak-Tend CV, ALL

Salles (2010) e

Hypertension, 28(8):1715e23HTN 532 4.80 STT CV, ALL

Savonitto (1999) GUSTO-IibJAMA, 281(8):707e13

AMI 12,142 0.50 TWI ALL

Savonitto (2005) GUSTO-IIbEur Heart J, 26(20):2106e13

NSTEMI 5192 0.08 STT ALL

Schillacia (2004) PIUMAHypertension, 22(2):407e14

HTN 1970 4.70 STT CV

Seres (1999) RESCATEAm J Cardiol, 84(9):963e9

UA 839 0.50 STT CV

Sherrid (2009) ICD-II RegistryAm J Cardiol, 104(12):1722e6

Cardiomyopathy 330 3.70 Tpeak-Tend, TWI SCD

Smetana (2011) e

J Electrocardiol, 44(3):301e8General CVD 681 7.30 Tpeak-Tend, TWR ALL

Taglieri (2011) e

Am J Cardiol, 108(1):21e8UA/NSTEMI 1042 1.00 TWI CV

Tan (2008) Ann Noninvasive Electrocardiol., 13(2):113e9 Clinical Population 24,270 7.50 T amplitude CV, ALLTeodorescu (2011) OSUDS

Heart Rhythm, 8(10):1562e7CAD 1092 NA JT interval SCD

Torbal (2004) e

Cardiology, 101(4):199e207Chest pain 2261 6.30 T axis, QRSTA ALL

Triola (2005) WISEJ Am Coll Cardiol, 46(1):51e6

Suspected CAD 143 3.30 QRSTA ALL

Watanabe (2004) e

J Electrocardiol, 37(3):191e200Clinical population 65 2.10 Tpeak-Tend SCD

Whang (2012) NHANES IIIAm J Cardiol, 109(7):981e7

General population 7052 14.00 QRSTA CV, ALL

Yamazaki (2005) e

Heart Rhythm, 2(1):73e8Clinical population 46,573 6.00 QRSTA CV

Yamazaki (2005) e

Am J Cardiol, 95(2):300e4Clinical population 41,997 6.00 T amplitude, TWI CV

Yokoyoma (2000) BARIAm J Cardiol, 86(8):819e24

CAD 3229 5.00 STT CV

Zabel (1998) e

Circulation, 97(25):2543e50Post AMI 280 2.67 Tpeak-Tend SCD, ALL

Zabel (2000) e

Circulation, 102(11):1252e7Post AMI 280 2.67 TCRT, PCA, T loop SCD, ALL

Zabel (2002) e

Circulation, 105(9):1066e70CAD 772 10.40 TCRT, PCA, TWR, T loop ALL

(continued on next page)

S.S. Al-Zaiti et al. / Heart & Lung 43 (2014) 516e526 525

Appendix 1 (continued )

Author (year) Study name and reference Disease population n Follow upa T waveparameter

Endpoint

Zhang (2007) ARICAm J Cardiol, 100(5):844e9

General population 13,973 14.30 STT, T axis, QRSTA CV, ALL

Zhang (2010) WHIAm J Cardiol, 106(1):18e25

Healthy women 64,597 8.00 STT, TWI CV, ALL

Zhang (2011) CHSAm J Cardiol, 107(6):817e20

Healthy elderly 4988 17.00 STT CV, ALL

ACS: acute coronary syndrome, ALL: all-cause death, AMI: acute myocardial infarction, ARIC: atherosclerosis risk in communities, BARI: Bypass Angioplasty RevascularizationInvestigation, BIRNAH: Belgian Interuniversity Research on Nutrition and Health, CAD: coronary artery disease, CCHS: Copenhagen City Heart Study, CHADPI: Chicago HeartAssociation Detection Project in Industry, CHD: Coronary Heart Disease Study, CHF: congestive heart failure, CHS: Cardiovascular Health Study, CV: cardiovascular death, CVD:cardiovascular disease, CWES: ChicagoWestern Electric Study, DEFINITE: defibrillators in non-ischemic cardiomyopathy treatment evaluation, EDC: Pittsburgh Epidemiologyof Diabetes Complications, ELITE: Losartan Heart Failure Survival Study, EMMACE: evaluation of methods and management of acute coronary events, ETT: exercise treadmilltesting, FAST: fast assessment of thoracic pain, FCS: Finnish Cardiovascular Study, FHS: Finnish Health Study, FRISC: Fragmin and fast revascularization during instability incoronary artery disease, GEMI: “Grupo de Estudio Multicentrico del Infarto del Miocardio” Study, GRACE: Global Registry Of Acute Coronary Events, GUSTO: Global Use ofStrategies to Open Occluded Coronary Arteries, HF: Heart Failure, HTN: hypertension, ICD: implantable cardioverter-defibrillators, ICTUS: Invasive Versus ConservativeTreatment in Unstable Coronary Syndromes, IHAST: Intraoperative Hypothermia Aneurysm Surgery Trial, LIFE: Losartan Intervention For Endpoint Reduction in Hypertension,MFUS: Manitoba Follow-up Study, MR-FAT: Multiple Risk Factor Analysis Trial, MRFIT: Multiple Risk Factor Intervention Trial, NA: Denotes cross sectional study so the followup time is “not applicable,” NHANES: National Health and Nutrition Examination Survey, NIPPON DATA: National Integrated Project for Prospective Observation of Non-Communicable Disease and its Trends in the Aged, NSTEMI: Non-ST Elevation Myocardial Infarction, OSUDS: Oregon Sudden Unexpected Death Study, PCA: principalcomponent analysis, PURSUIT: platelet GLYCOPROTEIN IIb/IIIa in Unstable angina: Receptor Suppression Using Integrilin (eptifibatide) Therapy, PIUMA: “Progetto IpertensioneUmbria Monitoraggio Ambulatoriale” Study, QRSTA: QRS-T Angle, RESCATE: Resources Used in Acute Coronary Syndromes and Delays in Treatment, RITA: RandomizedIntervention Trial of Unstable Angina, SAH: subarachnoid hemorrhage, SCD: sudden cardiac death, STEMI: ST-elevation myocardial infarction, STT: non-specific changes of theSTT complex, TCRT: total cosine R-to-T, THROMBO: thrombogenic factors and recurrent coronary events, TIMI: thrombolysis in myocardial ischemia, TMD: T loop morphologydispersion, TRIM: thrombin inhibition in myocardial infarction, TWI: T wave inversion, TWR: T wave residua, UA: unstable angina, WHI: Women Health Initiative, WISE:Women’s Ischemia Syndrome Evaluation, WOSCOPS: West of Scotland Population Study.

a Follow up time in years.

S.S. Al-Zaiti et al. / Heart & Lung 43 (2014) 516e526526