the predictive value of left ventricular hypertrophy on...

2017

The predictive value of left ventricular hypertrophy on ECG for cardiovascular outcome in the Thai population

Name: Joanne Bakker

Student number: S2463008

Faculty supervisor: P. van der Meer,

Cardiology UMCG

Second supervisor: P. Vathesatogkit,

Cardiology Ramathibodi Hospital

Location: Department of Cardiology,

Mahidol University, Ramathibodi

Hospital Bangkok

March, 2017

1

SUMMARY

Background: In Thailand, the prevalence of cardiovascular disease (CVD) increased

excessively during the past decades and is the leading cause of death. Left ventricular

hypertrophy (LVH) can be seen on electrocardiogram (ECG) and is associated with poor

cardiovascular outcome, but no incremental predictive value is seen when putting LVH in a

traditional predicting model. Most studies on this specific subject were performed in Western

populations. However, because Asians have a higher prevalence of LVH, we will explore the

predictive value of LVH in the Thai population. When CVD prediction is more accurate,

treatment can be started earlier and CVD events can be prevented. Method: ECG data of 2350

Thai citizens, participating in the EGAT cohort study, are analyzed. LVH on ECG is defined

following the Sokolow-Lyon criteria, the Minnesota criteria and the Cornell criteria. Outcomes

are fatal and nonfatal CVD events, coronary heart disease (CHD), cerebrovascular accident

(CVA) and all-cause mortality. Multivariate Cox regressions are used to explore the association

between LVH and outcome and tests for equality of ROC area are used for comparing predictive

value of the models. Results: A significant association between Sokolow-Lyon LVH,

Minnesota LVH and total LVH and the outcomes CVD, CHD and CVA is seen. In the

multivariate Cox regressions only Sokolow-Lyon criteria and Minnesota criteria were

associated with CVD [HR (95% CI): 1.86 (1.18-2.94) and 1.98 (1.22-3.21) respectively] and

CHD [HR (95% CI): 1.92 (1.06-3.47) and 2.63 (1.48-4.67), respectively]. No incremental

predictive value is observed when putting LVH in the traditional model for predicting CVD or

CHD (all p-values >0.05). For predicting CVD, a model with Sokolow-Lyon criteria and BMI

instead of fasting blood sugar (FBS) and non-HDL, both have the same predictive value [ROC

areas (p-value): 0.69 and 0.70 (0.40), respectively], but these models had better predictive value

than a standard model without these variables (p-values: 0.04 and 0.02, respectively).

Conclusion: Even though LVH is significantly associated with CVD and CHD outcome, there

is no incremental predictive value when the LVH variable is put in the model with traditional

risk factors. For predicting CVD, there seems to be an option to replace variables that require

blood samples (FBS and non-HDL) by Sokolow-Lyon LVH criteria and BMI.

Achtergrond: De prevalentie van cardiovasculaire aandoeningen (CVD) in Thailand is gestegen

gedurende de afgelopen decennia en is de meest voorkomende doodsoorzaak. Linker ventrikel

hypertrophie (LVH) kan worden gedetecteerd op een ECG en is geassocieerd met

cardiovasculaire aandoeningen, maar is niet van toegevoegde waarde in bestaande risico

schatting modellen. De meeste studies over dit onderwerp zijn uitgevoerd in Westerse

populaties. Vanwege het feit dat Aziaten een hogere prevalentie van LVH hebben, onderzoeken

we hier de toevoegende voorspellende waarde van LVH voor de Thaise bevolking. Wanneer

CVD beter wordt voorspeld, kan er eerder worden gestart met behandeling en kan ziekte

worden voorkomen. Methode: ECG data van 2350 Thaise inwoners, deelnemend in de EGAT

cohort studie, is geanalyseerd. LVH op ECG is aan de hand van de volgende criteria

gedefinieerd: Sokolow-Lyon, Minnesota en Cornell. De uitkomsten zijn fatale en niet-fatale

CVD gebeurtenissen, coronaire hart ziekte (CHD), cerebrovasculair accident (CVA) en

algehele mortaliteit. Multivariate Cox regressies zijn uitgevoerd om de associatie tussen LVH

en de uitkomsten te onderzoeken en testen voor de gelijkheid van het ROC gebied, zijn gebruikt

voor het vergelijken van de voorspellende waarde van de modellen. Resultaten: Een significante

associatie wordt gezien tussen Sokolow-Lyon LVH, Minnesota LVH en LVH totaal en de

uitkomsten CVD, CHD en CVA. In multivariate Cox regressies zijn alleen Sokolow-Lyon LVH

en Minnesota LVH geassocieerd met CVD [HR (95% CI): 1.86 (1.18-2.94) en 1.98 (1.22-3.21)

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respectievelijk] en CHD [HR (95% CI): 1.92 (1.06-3.47) en 2.63 (1.48-4.67), respectievelijk].

Er is geen toegevoegde voorspellende waarde gezien, wanneer LVH werd toegevoegd aan het

traditionele model, voor het voorspellen van CVD of CHD (alle p-waarden>0.05). Voor het

voorspellen van CVD hebben een model met Sokolow-Lyon LVH en BMI en een model met

in plaats van die variabelen, nuchtere bloedglucosespiegel (FBS) en non-HDL, dezelfde

voorspellende waarde [ROC gebied (p-waarde): 0.69 en 0.70 (0.40), respectievelijk], en hebben

beide modellen betere voorspellende waarden dan een standaard model zonder deze variabelen

(p-waarden: 0.04 en 0.02, respectievelijk). Conclusie: Alhoewel LVH significant is

geassocieerd met CVD en CHD, geeft LVH geen toegevoegde voorspellende waarde aan een

traditioneel risico model. Voor het voorspellen van CVD, lijkt er een mogelijkheid te zijn voor

het vervangen van variabelen waarvoor bloed monsters nodig zijn (FBS en non-HDL), door

Sokolow-Lyon LVH criteria en BMI.

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TABLE OF CONTENTS

Frequently used abbreviations ............................................................................................. 4

Introduction ......................................................................................................................... 5

Research question ................................................................................................................ 7

Material and methods ......................................................................................................... 8

Results ................................................................................................................................. 11

Discussion ........................................................................................................................... 17

Conclusion ........................................................................................................................... 20

Bibliography ........................................................................................................................ 21

Appendices .......................................................................................................................... 24

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FREQUENTLY USED ABBREVIATIONS

CVD Cardiovascular disease

CHD Coronary heart disease

CVA Cerebrovascular accident

LVH Left ventricular hypertrophy

ECG Electrocardiogram

BMI Body Mass Index

FBS Fasting blood sugar

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INTRODUCTION

CVD in Thailand

Worldwide, cardiovascular disease (CVD) is the leading cause of death, with 17.3 million

deaths per year [1]. Moreover, CVD is not only the leading cause of death in Thailand, but the

prevalence increased excessively during the last decades [2-3]. In 2012, 120,600 deaths were

the results of vascular disease [4] while in 1998 this number was 54,000 [2]. The cause of this

increase can be found in the change from an agricultural to an industrial culture which led to

reduced physical activity and change in food pattern, leading to a higher prevalence of

overweight people [5]. Early prevention of risk factors or early recognition of CVD can prevent

mortality by early treatment. Furthermore, cardiovascular risk factors found for the Thai

population are total cholesterol, blood pressure, body mass index (BMI), fasting blood sugar

(FBS), hypertension, smoking and diabetes mellitus (DM) [6]. In addition, features on

electrocardiogram (ECG) have also been confirmed as predictive for heart diseases [7]. One of

this features is left ventricular hypertrophy (LVH).

LVH on ECG

LVH is an abnormality of the heart caused by a compensatory process often associated with

abnormal load. The three most common causes are hypertension, coronary artery disease and

valvular heart disease [8]. The process of LVH starts when the heart is exposed to an abnormal

load. In order to respond, three compensatory processes are available; the first process is the

Frank-Starling mechanism, which will increase the contraction of the heart muscle. The second

process is increasing the muscle mass to create a condition in which the heart can endure the

extra load. The third process consists of recruiting neurohormonal mechanisms which will

increase contractility. Because the Frank-Starling mechanism has limited capacity and the third

mechanism is detrimental when used in a chronic situation, the heart frequently increases its

muscle mass by hypertrophy of myocytes in a situation with chronic abnormal load. Moreover,

pathological hypertrophy of the left ventricle can be present many years before the appearance

of heart disease [9].

Echocardiography is an accurate method for detecting left ventricular mass and therefore LVH

[10]. Considering the fact that ECG is cheaper and faster than echocardiography, many studies

are performed to explore criteria for diagnosing LVH on a ECG. Another advantage of using

ECG is that it is less operator dependent because when performing an echocardiogram there is

a higher chance of getting inter-observer variability [11]. Many studies focused on finding the

best fitting criteria for LVH on ECG. Findings on ECG that appear during LVH are increased

QRS voltage, increased QRS duration, left axis deviation, repolarization abnormalities and left

atrial abnormality [12-14]. These findings on ECG are converted to criteria to diagnose LVH.

While unfortunately all criteria on ECG are found to have a low sensitivity and a high specificity

[15-16], they still are widely used. Even though the sensitivity is low, LVH on ECG is

associated with poor outcome, such as CVD, coronary heart disease (CHD) events and mortality

[17-24]. These studies are mostly performed in Western populations. Only a few studies

explored this effect for an Asian population [25-26].

What is found in earlier studies?

Most of the studies done, explored the association between LVH on echocardiogram [24] or

ECG and outcome as cardiac death, CVD or stroke. In some studies, only one criterion is used

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for LVH [18, 26] and other studies used many criteria [19-21]. The problem with only using

one criterion is the uncertainty which criteria fits the population best since there is not one

criterion considered as the golden standard. Also for outcome criteria, some studies used only

one outcome [21, 26], while other studies used many outcomes [18-20]. The increased risk of

cardiovascular morbidity and mortality when ECG-LVH exists, is seen in hypertensive patients

[27], but also in the general population [17-24]. For the Thai population this association is not

explored so far. In most Western studies the only question explored, is the association between

LVH on ECG and a cardiovascular outcome, whereby some studies distinguish between sex or

race. As far as we know, only a few studies explored the incremental predictive value of LVH

on the Framingham risk equations. One study was performed in a cohort of white and black

people but Asian people were excluded. In this study no incremental predictive value of LVH

was found [20]. Also in a multi-ethnic population, with 22% Chinese participants, no

incremental predictive value was seen [28]. Santhanakrishnan et al. found that Asians had

greater odds having ECG-LVH compared with Caucasians and this presence had greater effect

on all-cause mortality in Asians compared with Caucasians [26]. Therefore, in this study we

presume this incremental predictive value could be present in the Thai population, when LVH

is added to the traditional risk model.

A frequently used model with traditional risk factors is derived from the Framingham Heart

Study, which is a large study exploring many aspects of heart disease. In 2008 the Framingham

General Cardiovascular Risk score is published, which presents a risk score for all CVD events

and not only fatal events as done before [29]. An aim of this study was to create a predicting

tool that can be used by physicians at the clinic or office. Therefore, a simpler CVD risk

prediction model is tested, using the variable BMI instead of total and HDL cholesterol and this

model worked reasonably well. Hence, in this study we want to explore if LVH can obtain as a

replacement variable of laboratory variables (lipids and glucose) for predicting cardiovascular

outcome in the Thai population.

Relevance

In this study, the first aim is to explore the association between LVH and CVD in the Thai

population, which is found in other populations. Since the prevalence of CVD is growing in the

Asian populations and most studies exploring CVD take part in Western populations, it is of

great importance that CVD will be better understood in Asians. The only conclusion in most

studies is the association whereas, in our perspective, we regard it important to explore the role

of ECG-LVH in the risk prediction of CVD.

Therefore, our second aim is to analyze the possible incremental value of LVH criteria added

to the traditional prediction model. If the association between LVH and CVD exists in the Thai

population, this variable may have incremental value for the existing prediction model. In this

case, ECG-LVH criteria could be included in the existing model and CVD could be predicted

earlier and more accurate. The earlier CVD is detected, the earlier treatment can be started. In

addition, when differences in CVD between Asians and Western people are better understood,

this could have consequences for the kind and intensity of treatment.

The third aim of this study is to explore the possibility of developing a prediction model without

variables that require blood samples. In the model with traditional risk factors, variables are

included that assume the presence of blood samples like lipids and FBS. Because blood samples

are invasive to patients for risk prediction, a new model would make evaluating the risk on

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CVD cheaper, faster and more comfortable for the individual. When the risk prediction is less

anxious for the individual, possibly people come more often for a follow up and in this way the

risk of CVD for the whole population, can be evaluated more frequently.

Research question

In this study we will explore the association between four different criteria for LVH and the

outcomes CVD, CHD, cerebrovascular accident (CVA) and all-cause mortality. Furthermore,

we will add LVH in a predictive model with traditional risk factors to see if LVH has

incremental predictive value. At last we will test if a model with LVH and BMI is as predictive

as a model with variables that require blood samples, in this case the variables FBS and non-

HDL.

Primary research questions:

- Is there an association between ECG-LVH and long term outcome (CVD, CHD, CVA

and all-cause mortality)? If so, how strong is this association?

- Does LVH on ECG has incremental predictive value in the predicting model for CVD,

CHD, CVA and all-cause mortality?

- Can ECG-LVH be used as replacement for laboratory variables in a predicting model

for cardiovascular outcome? (Does a risk model with LVH and BMI has the same

predictive value as a model with FBS and non-HDL?)

Secondary research question:

- Which risk factors have predictive value for the outcome CVD, CHD, CVA and all-

cause mortality? Which combination of risk factors gives the best fitting model for the

outcomes?

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MATERIAL AND METHODS

Study design

This research was part of the Electricity Generating Authority of Thailand (EGAT) study. The

EGAT study is a cohort study which started in 1985. EGAT is a state enterprise in Thailand,

responsible for generating and transmission of electricity. The employees of EGAT were

invited to participate in the cohort study. Employees were invited from all kinds of occupations,

they could work in the headquarter in Bangkok or at three different sites in Northern and

Western Thailand where the dams are located. The Institutional Review Board at Mahidol

University approved the study and informed consent forms are signed by all participants. Every

participant underwent physical examination, provided blood samples while fasting and filled in

questionnaires. With a gap of twelve years because of circumstances, the participants were

contacted again in 1997. Since then, every five years there is a follow-up moment. After the

first cohort, started in 1985 (EGAT 1), another two cohorts started in the EGAT study. EGAT

2 started in 1998 and EGAT 3 started in 2009. Data collected in EGAT 1 is used in this study

because this cohort has the longest follow-up time and we want to explore the effect of LVH

over a longer period.

Study population

In this study 2350 participants were analyzed. The cohort group contains 1855 men and 495

women, aged 47 till 66 at baseline. Because participants had different occupations, the study

contains of a population with a wide range of socio-demographic backgrounds. People were

excluded because of cardiovascular events before 1997(n=39), no ECG available or low quality

of the ECG(n=348).

Study parameters

The primary study parameters are:

1) LVH following one of the four criteria.

2) Fatal and nonfatal cardiovascular outcomes; CVD, CHD, CVA and all-cause mortality.

Secondary study parameters are:

1) Traditional cardiovascular risk factors: age, sex, BMI, diabetes mellitus, lipids, smoking

and hypertension.

2) Alcohol current/not current.

Study procedure

In this research ECGs of all the participants of EGAT 1 that were available, are evaluated.

Because the ECGs made in 1985 were not usable, we used the ECGs collected in 1997, during

the first follow up. The ECGs were obtained at a paper speed of 25 mm/s and stored after. Later

in time, the ECGs were digitally scanned. The ECGs were manually read with a ruler with 0.5

mm gradations. The case report form used for measuring the ECGs is seen in appendix 1. The

amplitudes of the following waves are assessed: R wave in lead I, R wave in lead II, R wave in

lead III, R wave in lead VL, R wave in lead VF, S wave in lead V1, S wave in lead V3, R wave

in lead V5 and R wave in lead V6. LVH is defined by the criteria seen in table 1. Cornell criteria

are commonly used and have shown to have a better diagnostic and prognostic performance

compared to other ECG-LVH criteria [21]. Also Cornell criteria with lower cutoff values are

used because in a study in a Korean population, this showed improved sensitivity [30]. The

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Sokolow-Lyon criteria [31] and Minnesota criteria [32] are also commonly used for detecting

LVH. In addition, a group is added in the analysis, called LVH total which includes participants

who had LVH following any of the criteria.

Name LVH criteria Criteria

Sokolow-Lyon criteria SV1 + RV5/V6 ≥ 35 mm and/or RaVL ≥ 11

mm

Minnesota criteria 3.1 RV5/V6 > 26 mm or RI/II/III/aVF > 20 mm

or RaVL > 12 mm

Cornell voltage criteria RaVL + SV3 > 28 mm for men and

RaVL + SV3 > 20 mm for women

Cornell criteria “low” RaVL + SV3 > 20 mm for men and

RaVL + SV3 > 16 mm for women Table 1: LVH criteria

The outcome data are assessed by the research team of the EGAT study and were already

available. The outcome data is available till December 2015, so there is a total follow up of

18.5 years. CVD stands for cardiovascular disease and includes four major areas:

- CHD; MI and sudden cardiac death.

- Cerebrovascular disease; stroke and transient ischemic attack.

- Peripheral artery disease; intermittent claudication.

- Aortic atherosclerosis and thoracic or abdominal aortic aneurysm.

While CHD and CVA are also included in CVD, we also use them as individual outcomes.

The cause of death is determined by researchers of the EGAT study, among who two

cardiologists, one neurologist, one gastroenterologist and one internist. They examined the

cause of death based on death certificates, records of hospital admissions or medical notes from

insurance companies. If this information was not available, the researchers contacted relatives

or colleagues.

The causes of death are classified in the following groups:

- Coronary heart disease (fatal myocardial infarction or sudden unexplained death)

- Stroke (including subarachnoid haemorrhage)

- Other vascular death (heart failure, valvular heart disease or peripheral arterial disease)

- Respiratory disease (excluding malignancy)

- Gastrointestinal or hepato-biliary disease (excluding malignancy)

- Malignancy

- Injuries (including suicide)

- Other

For defining the final model, many multivariate regressions are made to consider the best

fitting model. The diagram used to perform these multivariate regressions is seen in appendix

2. The following variables are considered in these regressions:

- LVH, defined as mentioned above.

- Age: continuous and in groups. The groups are defined as following; group 1= 45-49 years

old, group 2=50-54 years old, group 3= 55-59 years old, group 4= 60-64 years old and group

5=65-69 years old.

- Sex.

- BMI: continuous and in groups. BMI groups are defined following the BMI cutoff points for

Asians and Asian Americans performed by the Asian American Diabetes Initiative. This

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shows that BMI <18.5 is below the healthy range. BMI between 18.5 and 22.9 is within the

healthy range, between 23 and 26.9 is above healthy range and above 27 is further above

healthy range [33].

- Diabetes mellitus: DM is diagnosed when FBS is higher than 126 mg/dl. FBS is also used as

a continuous variable. FBS is the glucose value in the blood after fasting for eight hours.

- Lipids: the variables used for lipids are triglycerides, total cholesterol, LDL and non-HDL.

non-HDL is calculated as the variable total cholesterol minus the variable HDL.

- Blood pressure: for blood pressure the following variables are used: systolic blood pressure

(SBP), diastolic blood pressure (DBP) and hypertension as a binary variable (yes/no). This

variable is defined as yes if the participant had a SBP of 140 mmHg or higher in 1997.

- Smoking: for smoking three different variables are used: yes/no/quit, current/not current,

ever/never.

- Alcohol: yes/no/quit, current/not current, ever/never.

Statistical analysis

Histograms and Q-Q plots are made to see if the variables are normally distributed. When

normally distributed baseline variables are listed as mean with standard deviation. When not

normally distributed the baseline variables are listed as median with interquartile range. The

continuous variables are compared with t-tests and the categorical variables with Pearson’s chi-

square tests. The differences between ECG measurements of the two researchers is evaluated

with Bland-Altman plots. Kaplan-Meier survival curves are made to distinguish the survival

curves of participants with and without LVH and after that log-rank tests were done for the

different outcomes to look for the equality of survivor functions between participants with and

without LVH. The null-hypothesis for this test is that both groups have equal risks for the

outcome.

To see which variables have a significant association with the different outcomes univariate

Cox regressions are done. For this regression p-values lower than 0.25 are considered

potentially useful in the multivariate model. If the variable in the univariate regression has a p-

value greater than 0.25 it is highly unlikely it will contribute to the multivariate model.

Multivariable Cox regressions are performed to get a final model and AUC curves of these

models are made. The best fitting models with LVH variable included are tested on their

incremental predictive value with a test for equality of ROC area between the model with LVH

included and the model LVH excluded. The test for the equality of the area under the curves

uses an algorithm suggested by DeLong, DeLong and Clarke-Pearson [34]. This test for

equality of ROC area is also used to explore the equality of predictive value for a model with

BMI and LVH, and the model with FBS and non-HDL that requires blood samples.

A p-value ≤ 0.05 was considered statistically significant. All statistics are executed in STATA

version 14.1.

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RESULTS

Patient characteristics

This study includes 2350 participants, whereof 1855 males and 495 females. All participants

were Thai inhabitants and working at EGAT. Median follow up time is 222 months (18.5 years)

and mean follow-up time for CHD is 199 months (16.6 years), for CVD 194 months (16.2

years), for CVA 198 months (16.5 year) and for all-cause mortality 202 months (16.8 years).

Variable Total (n=2350) No LVH

(n=2153)

LVH (n=197) p-value

Age ≥55 (%) 46 46 45 0.04

Sex (% male) 79 78 86 0.005

BMI (mean, STD) 24.7 (3.3) 24.7 (3.3) 25.1 (3.3) 0.06

Hypertension (%) 47 46 59 <0.001

Systolic blood pressure (mean,

STD)

136 (21.2) 135 (20.9) 142 (23.3) <0.001

Diastolic blood pressure

(mean, STD)

81 (13.2) 81 (12.9) 86 (15.0) <0.001

Fasting blood sugar (median,

IQR)

90 (81, 100) 89 (81, 99) 91 (81, 102) 0.25

Diabetes mellitus (%) 16 15 19 0.16

Total cholesterol (mean, STD) 238 (41.3) 237 (41.7) 239 (37.6) 0.36

Non-HDL (mean, STD) 186 (42.6) 185 (42.9) 188 (39.4) 0.19

Smoking, current (%) 24 24 24 0.94

Alcohol, current (%) 38 37 43 0.09 Table 2: Baseline table. Abbreviations: STD= standard deviation, IQR= interquartile range

At baseline 197 participants (8.4%) had LVH defined by any of the four criteria, seen in table

3. The highest prevalence of LVH (4.3%) was observed with the Sokolow-Lyon criteria, while

the Cornell criteria only showed a prevalence of 0.6%.

Variable Total

(n=2350)

Male

(n=1855)

Female

(n=495)

p-value

LVH following the Sokolow-Lyon

criteria

4.3% 5.1% 1.4% <0.001

LVH following the Cornell criteria 0.6% 0.2% 2.2% <0.001

LVH following the lower Cornell

criteria

3.4% 3.3% 3.6% 0.66

LVH following the Minnesota criteria 3.5% 4.2% 1.0% <0.001

LVH total 8.4% 9.2% 5.3% 0.004 Table 3: Distribution of different LVH criteria with difference between men and women.

The frequencies of the outcome variables are shown in table 4. 11.5% of our population had a

CVD event, fatal or non-fatal.

Outcome Frequency Percentage in %

CVD 269 11.5

CHD 152 6.4

CVA 101 4.3

All-cause mortality 413 17.6 Table 4: Frequencies of outcome variables

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Association ECG-LVH and outcome

Log rank tests

With log rank tests the equality of survivor functions is measured between the groups with LVH

and without LVH. For CHD, there only is a significant inequality in survivor functions for

Sokolow-Lyon LVH criteria (p-value= 0.021), Minnesota LVH criteria (p-value= <0.001) and

LVH total (p-value= 0.029) (see table 5).

LVH criteria CHD events observed CHD events expected

Sokolow-Lyon No 140 145.7

Yes 12 6.3

Chi2 5.29

p-value 0.021

Minnesota No 139 147.0

Yes 13 5.0

Chi2 13.09

p-value <0.001

Total No 132 139.4

Yes 20 12.6

Chi2 4.76

p-value 0.029

Total 152 152 Table 5: Log-rank test CHD for LVH criteria

For CVD the same applies as for CHD. Sokolow-Lyon LVH criteria has a p-value of 0.002,

Minnesota LVH criteria <0.001 and a p-value for LVH total of 0.008. The results are shown

in table 6.

LVH criteria CVD events observed CVD events expected


Yes 21 10.9

Chi2 9.76

p-value 0.002

Minnesota No 250 260.1

Yes 19 8.9

Chi2 12.01

p-value <0.001

Total No 235 247

Yes 34 22

Chi2 7.07

p-value 0.008

Total 269 269 Table 6: Log-rank test for CVD for LVH criteria

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For CVA, there is a significant p-value for the log rank test for LVH following the Sokolow-

Lyon criteria (0.014) and for LVH total (0.017), as seen in table 7.

LVH criteria CVA events observed CVA events expected


Yes 9 4.1

Chi2 6.06

p-value 0.014

Total No 86 92.6

Yes 15 8.4

Chi2 5.68

p-value 0.017

Total 101 101 Table 7: Log-rank test for CVA for LVH criteria

For the outcome all-cause mortality, no significant p-values are seen. Therefore, we can not

reject the hypothesis that there is an equal risk for all-cause mortality for participants with and

without LVH.

Cox regression

Univariate Cox regressions are performed for the outcomes CVD, CHD, CVA and all-cause

mortality. All the variables that had a p-value ≤0.25 were considered valuable for the

multivariate Cox regression. For all outcomes, all categories (age, sex, BMI, blood pressure,

diabetes, lipids, smoking and alcohol) had one or more significant variables in the univariate

regression. Many multivariate Cox regressions are performed whereof the diagram is shown in

appendix 2. In this regressions, it is seen that for the outcomes CVA and all-cause mortality no

LVH criteria had significant p-values. Finally, four models came out with significant p-values

for LVH criteria and with risk variables that gave the best significance. BMI is left out of the

multivariate models because it did not give significant p-values. Alcohol was included in the

model even though it is a protective value. Non-HDL is also included in the model even though

it is not significant because literature concludes that non-HDL is a good marker for lipids.

Finally, LVH, age, sex, smoking, FBS, hypertension, non-HDL and alcohol are included

variables in the models. For outcome CHD the model with Sokolow-Lyon LVH criteria, LVH

had a hazard ratio of 1.92 with a p-value of 0.03 (see table 8). The Minnesota criteria for LVH

in the model for outcome CHD, had a hazard ratio of 2.63 with a p-value of <0.001 (see table

9). Furthermore, the outcome CVD had two models with significant LVH criteria; Sokolow-

Lyon criteria gave a hazard ratio of 1.86 with a p-value of 0.01 (see table 10) and Minnesota

criteria gave a hazard ratio of 1.98 with a p-value of 0.01 (see table 11).

Variable Hazard ratio p-value 95% confidence interval

Sokolow-Lyon LVH (yes) 1.92 0.03 1.06 3.47

Age (in years) 1.08 <0.001 1.05 1.12

Sex (female) 0.60 0.06 0.36 1.01

Smoking (current) 1.56 0.02 1.07 2.26

FBS (10 unit increase) 1.10 <0.001 1.10 1.10

Hypertension (yes) 2.02 <0.001 1.42 2.90

Non-HDL (1 unit increase) 1.00 0.41 0.99 1.01

Alcohol (current) 0.46 <0.001 0.32 0.68 Table 8: Multivariate Cox regression CHD model with Sokolow-Lyon LVH criteria

14


Minnesota LVH (yes) 2.63 <0.001 1.48 4.67

Age (in years) 1.08 <0.001 1.05 1.12

Sex (female) 0.62 0.07 0.37 1.04

Smoking (current) 1.58 0.02 1.08 2.29

FBS (10 units increase) 1.10 <0.001 1.10 1.10



Alcohol (current) 0.46 <0.001 0.32 0.67 Table 9: Multivariate Cox regression CHD model with Minnesota LVH criteria


Sokolow-Lyon LVH (yes) 1.86 0.01 1.18 2.94

Age (in years) 1.07 <0.001 1.04 1.10

Sex (female) 0.80 0.24 0.56 1.16

Smoking (current) 1.77 <0.001 1.34 2.34




Alcohol (current) 0.61 <0.001 0.46 0.80 Table 10: Multivariate Cox regression CVD model with Sokolow-Lyon LVH criteria


Minnesota LVH (yes) 1.98 0.01 1.22 3.21

Age (in years) 1.07 <0.001 1.04 1.10

Sex (female) 0.81 0.26 0.56 1.17

Smoking (current) 1.78 <0.001 1.35 2.35




Alcohol (current) 0.60 <0.001 0.46 0.80 Table 11: Multivariate Cox regression CVD model with Minnesota LVH criteria

Figures 1-4 display Kaplan-Meier survival curves of LVH versus no LVH for developing CHD

and CVD events. Seen is that the LVH group had lower survival estimates than the no LVH

group.

Figure 1: Kaplan-Meier survival graph CHD/ Sokolow-Lyon Figure 2: Kaplan-Meier survival graph CHD/Minnesota

15

Figure 3: Kaplan-Meier survival graph CVD/Sokolow-Lyon Figure 4: Kaplan-Meier survival graph CVD/Minnesota

Incremental predictive value LVH on traditional model

For predicting CHD both Sokolow-Lyon- as Minnesota LVH criteria gave no incremental

predictive value to the traditional model (p-value: 0.30 and 0.19, respectively), whereof results

are shown in table 12 and 13. All ROC areas had a value of 0.73. Furthermore, no incremental

predictive value was found for the CVD prediction model when LVH was added (see table 14

and 15), Sokolow-Lyon LVH criteria gave p-value 0.20 and Minnesota LVH criteria 0.26.

These models had a ROC area of 0.70 and 0.71.

ROC area Std. Err. p-value

Model without LVH 0.73 0.02

Model with Sokolow-Lyon LVH 0.73 0.02 0.30 Table 12: ROC area CHD model with and without Sokolow-Lyon LVH criteria



Model with Minnesota LVH 0.73 0.02 0.19 Table 13: ROC area CHD model with and without Minnesota LVH criteria



Model with Sokolow-Lyon LVH 0.71 0.02 0.20 Table 14: ROC area CVD model with and without Sokolow-Lyon LVH criteria



Model with Minnesota LVH 0.71 0.02 0.26 Table 15: ROC area CVD model with and without Minnesota LVH criteria

16

Simplified prediction model using non-laboratory variables

Although no incremental predictive value is found for LVH, we explored the replacement value

of LVH. Therefore, three different models were made. Model 1 contains variables age, sex,

smoking, hypertension, alcohol, BMI and LVH. Model 2 contains the same variables but FBS

and non-HDL instead of LVH and BMI. Model 3, which we call standard model, only encloses

age, sex, smoking, hypertension and alcohol.

Figure 5: Variables included in the three different models used for testing predictive value of a non-laboratory

model with LVH and BMI instead of FBS and non-HDL.

For CHD, no significant difference in predictive value is seen between model 1 with Sokolow-

Lyon LVH criteria and model 2. This also applies for Minnesota LVH criteria and model 2.

Furthermore, no significant difference is seen between model 1 with Sokolow-Lyon criteria and

model 3, as for the Minnesota criteria. The difference in predictive value between model 2 and

model 3 is significant (p-value=0.02). Results can be seen in table 16.

ROC areas (p-value) Model 2 Model 3

Model 1: SL criteria 0.71 and 0.72 (0.20) 0.71 and 0.70 (0.19)

Model 1: M criteria 0.71 and 0.72 (0.32) 0.71 and 0.70 (0.17)

Model 2 X 0.73 and 0.70 (0.02) Table 16: ROC area three different models for CHD (ROC area left column mentioned first, then ROC area of

first row, and p-value

For CVD, no significant difference is seen between the models with LVH (both Sokolow-Lyon

as Minnesota) and model 2. Between model 1 with Sokolow-Lyon LVH criteria and model 3 a

significant difference in predictive value is seen (p-value=0.04). Between model 1 with

Minnesota criteria and model 3, no significant difference is seen. Between model 2 and model

3 a significant difference is found (p-value: 0.02) (see table 17).

ROC areas (p-value) Model 2 Model 3

Model 1: SL criteria 0.69 and 0.70 (0.40) 0.69 and 0.68 (0.04)

Model 1: M criteria 0.69 and 0.70 (0.26) 0.69 and 0.68 (0.12)

Model 2 X 0.70 and 0.68 (0.02) Table 17: ROC area three different models for CVD (ROC area left column mentioned first, then ROC area of

first row, and p-value

17

DISCUSSION

The main finding of this study is the significant association between Sokolow-Lyon LVH

criteria and Minnesota LVH criteria and cardiovascular outcomes CVD and CHD. However,

no significant benefit of putting the LVH variable in the model with traditional risk factors to

predict CVD or CHD is found. A model with traditional risk factors and LVH and BMI gave

the same predictive value as a model with the traditional risk factors and FBS and non-HDL.

But only for the Sokolow-Lyon LVH criteria for predicting CVD, this model had superior

predictive value over the standard model.

The analysis of this research shows that LVH on ECG defined by the Sokolow-Lyon criteria,

the Minnesota criteria and LVH total, gives significant associations with CHD, CVD and CVA

in a log-rank test and univariate Cox regression. In the multivariate Cox regression with

additional risk factors only significant hazard ratios are seen for the Sokolow-Lyon LVH

criteria and the Minnesota LVH criteria in predicting CHD and CVD. We did expect that LVH

on ECG had a significant association with cardiovascular outcome, because many other studies

concluded this association [17-24]. Although, research about this subject lacks in the Thai

population, we expected that this association between LVH and cardiovascular outcome was

also present in our population. The hazard ratios of Sokolow-Lyon and Minnesota LVH criteria

in multivariate regressions for predicting CVD and CHD were around 2. In a study comparing

different ethnicities, for Asians a hazard ratio is seen of 2.66 (95% CI: 1.83-3.88) for LVH

defined by the Cornell criteria in predicting mortality [26]. Our study showed no significant

association between Cornell LVH criteria and any of the outcomes. For LVH defined by the

Sokolow-Lyon criteria, in a Western population a hazard ratio is seen of 1.39 (95% CI: 1.12-

1.73) in a multivariate model for predicting CVD [18]. The hazard ratio in our study for this

circumstances was 1.86 (95% CI: 1.18-2.94). Therefore, it seems like Sokolow-Lyon criteria

have a stronger association with CVD in the Thai population than in the Western population.

For the Minnesota LVH criteria, a relative risk is seen of 2.04 (95% CI: 1.05-3.95) for predicting

CHD in Western hypertensive patients [35]. Our study showed a hazard ratio of 2.63 (95% CI:

1.48-4.67) for Minnesota LVH criteria in predicting CHD. Hence, also for the Minnesota

criteria it seems that there is a stronger risk prediction in the Thai population than in the Western

population.

As regards the LVH criteria, it was not expected that the Cornell criteria for LVH were useless

in our research. They did not predict cardiovascular outcome in our population and did not even

had a significant hazard ratio in the univariate regression. In other studies, the Cornell criteria

is shown to be a good predictor of cardiovascular outcome and total mortality [22, 36]. In the

univariate regression LVH criteria are significantly association with many outcomes, but this

reduces in our multivariate regression. This occurrence is also seen in Jain et al. This study

found significant hazard ratios for all their LVH criteria in univariate regressions, however,

most LVH criteria lost their significant hazard ratio in a multivariate regression [21]. In contrary

to our study, the Sokolow-Lyon criteria and Minnesota LVH criteria lost their prognostic value

whereas the Cornell criteria maintain their significant association. Apparently, many LVH

criteria are not strong enough to be of significant value for a multivariate model. All LVH

criteria used, are derived from Western populations. Some studies reported that these criteria

do not fit the Asian population [30, 37] but did not give criteria based on an Asian population.

Park et al. showed that lower cutoff points for the Cornell criteria improved the sensitivity for

an Asian population [30]. Unfortunately, in our population, this criterion gave no significant

18

results. Therefore, we believe in the importance of developing LVH criteria specific for the

Thai population. This can be executed with a cross-sectional research with ECG and

echocardiogram data.

The multivariate model of traditional risk factors in this research exists of the variables age

(continuous), sex, smoking (current/not current), FBS, hypertension (yes/no), non-HDL and

alcohol (current/not current). These variables had the most significant hazard ratios in this

research. These risk factors are commonly used and in this research we used these variables

instead of using completely different ones. We only explored which variable inside its own

group was the best to use (see appendix 2 for the diagram). Adding more risk factors could

potentially create a better predictive model. In some studies, the models were also adjusted for

antihypertensive therapy [21, 26] and physical activity [21]. Antihypertensive therapy

influences the risk for cardiovascular events. Therefore, in future research it could be useful to

implement medication data in the analysis. Instead of a risk factor, alcohol use is a protective

factor, this was as expected because other studies showed that mild alcohol consumption can

help avert CVD [38].

In this research it is explored whether ECG-LVH had incremental predictive value in the model

with traditional risk factors. Okwuosa et al. is the first research exploring the possibility of

improving CVD or CHD risk prediction with LVH. In this study Caucasians and Blacks were

included and no incremental predictive value was seen when LVH was added to the model with

traditional risk factors [20]. Also in a multi-ethnic study no significant improvement of the

traditional predictive model is seen when LVH is added [28]. We expected that LVH criteria

could have an incremental predictive value to the model for our population because in

Santhanakrishan et al. is seen that LVH is more prevalent and had a greater effect on all-cause

mortality in Asians than in Caucasians [26]. Even though this appearance, LVH criteria in our

population still had no incremental predictive effect when added to the traditional model. This

may be explained by the high predictive value of the model with the traditional variables or the

inaccurate criteria for LVH in this research. Newly developed LVH criteria for the Thai

population could differ in incremental value. Another possibility is that the risk determined by

the variable hypertension and the risk of LVH coincide. In this research no significant

interactions are found between LVH criteria and hypertension. Still, literature concluded that

LVH is often caused by hypertension, therefore the variable hypertension may be enough to

include the risk LVH entails.

Furthermore, there is explored if a predictive model for CVD and CHD with LVH and BMI has

the same predictive value as a model with FBS and non-HDL. We expected that even though

LVH is not of incremental value, it can still function as a replacement for variables requiring

blood samples. No research is found that performed this analysis for the Thai population. Only

in the Western Framingham study, there is explored if a non-laboratory prediction model could

be developed by replacing total and HDL cholesterol by BMI [29]. Building on this idea, we

tried to implement LVH in this model as replacement for laboratory variables. For CHD, we

found that the models with LVH criteria, both Sokolow-Lyon as Minnesota, have the same

predictive value as the models with FBS and non-HDL. Though, the models with LVH also had

the same predictive value as the standard model. The model with FBS and non-HDL did have

a significantly better predictive value than the standard model. The laboratory model is as good

as the non-laboratory model but this model has the same predictive value as the standard model.

Replacing laboratory variables with LVH is not useful for predicting CHD.

19

For CVD, the models with both LVH criteria also had the same predictive value as the model

with FBS and non-HDL. The model with Minnesota LVH criteria has the same predictive value

as the standard model, but the model with Sokolow-Lyon criteria has better predictive value

than the standard model. The model with FBS and non-HDL also has better predictive value

than the standard model. This could mean that in practical use for predicting CVD, we can use

a model with Sokolow-Lyon LVH criteria and BMI as well as a model with FBS and non-HDL

which leads to less blood tests in the future. The difference in predictive values is very small.

This can be explained by the fact that sex, age, hypertension and smoking already have strong

predictive value which makes the other variables less essential. Considering the fact that the

experiment of the Framingham to create a non-laboratory model also worked reasonably well

[29], we consider it valuable to continue the analysis to create a non-laboratory model with

LVH included.

Strengths and limitations

A strength of this research is that we explored the possible incremental predictive value of LVH

in a model with traditional risk factors for predicting cardiovascular outcome. Most studies on

LVH and cardiovascular outcome only looked at the hazard ratios in the Cox regression. This

result gives information about the association between LVH and cardiovascular outcomes, but

gives no information about the possibility that the predictive model for CVD gets stronger when

LVH is included. Another strength is the exploration about having an alternative risk prediction

model with LVH and BMI instead of FBS and non-HDL which require blood samples, is not

done before and could be useful for daily practice. Further, it should be mentioned the EGAT

study is the first large cohort study in the Thai population. Therefore, all research done in this

study is of great contribution to knowledge about cardiovascular disease in Thailand.

There are some limitations of this research that need to be reported. First limitation is the loss

of many ECGs because the ink is faded in time. Therefore, we could not use all the participants

from EGAT 1. The second limitation is the distribution of men and women which is not equal.

Because more males are included, the results of this research could not be representative for

females. Future research should try to include as many females as males. A third limitation is

the confounding factors used, which are the conventional risk factors. Maybe different risk

factors like sodium intake, would also fit in de model, but these factors were not available.

Medication use and socioeconomic status are also factors that can influence the risk on having

a CVD event. The fourth limitation of this research is that ECGs were only read by one

researcher, whereof 10% is reviewed by another researcher. Seen in the Bland-Altman plots of

three amplitude measurements (appendix 3) is the small difference between the measurements.

We do not regard them as clinically relevant in the diagnosis of LVH. Still, data would be more

accurate if the ECGs were read by a machine as the Marquette, used in many other studies. This

limitation negatively affected the objectivity of our data used. The fifth limitation is the fact

that we did not put death as a competing risk in our analysis because only Cox proportional

hazard regressions are used. Furthermore, in this study a large population with different

occupations from the Thai population is used in order to avoid including people from only one

socioeconomic group. Nevertheless, it should be mentioned that all participants were

employees at EGAT so they were healthy enough to have a job.

For further research, we suggest that LVH criteria specifically for the Thai population should

be developed. As mentioned before, there is no general criterion for LVH in the Thai

20

population. Existing criteria are derived from Western studies and not many studies have been

done to develop LVH criteria for the Asian, or Thai, population. We used four different criteria

hoping that one of these criteria or the criteria all together, would give a good indication of the

participants with LVH, but it would be more precise if LVH criteria are developed for the Thai

population. When the most accurate criteria are used, this will affect the accuracy of the analysis

with cardiovascular outcome. With new LVH criteria more research could be executed to

explore its practical use in predicting CVD.

CONCLUSION

LVH, following the Sokolow-Lyon criteria and the Minnesota criteria, is associated with CVD

and CHD in multivariate Cox regressions. People with LVH have circa twice the chance to get

a CVD or CHD event as people without LVH. The final models with risk factors strongest

associated with all outcomes, contain the variables age, sex, smoking, hypertension, FBS, non-

HDL and alcohol. There is no incremental predictive value seen for LVH criteria added to the

model. Therefore, using ECG-LVH criteria for more accurate prediction is ineffectual. Though,

for predicting CVD, there seems to be an option for replacing variables that require blood

samples (FBS and non-HDL) by Sokolow-Lyon LVH criteria and BMI, which can lead to less

blood tests.

21

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24

APPENDICES

Appendix 1: Case report form ECG measurement

CASE REPORT FORM

Patient information

Patient ID

Sex Male Female

Date of birth (dd-mm-yyyy)

Age

ECG

Lead I : R wave (mm)

Lead II : R wave (mm)

Lead III : R wave (mm)

Lead VL : R wave (mm)

Lead VF : R wave (mm)

Lead V1 : S wave (mm)

Lead V3 : S wave (mm)

Lead V5 : R wave (mm)

Lead V6 : R wave (mm)

Name investigator: Date:

25

Appendix 2: Diagram multivariate Cox regressions

Figure 6: Diagram options multivariate Cox regressions, used for

all outcomes.

Abbreviations:

BMI: body mass index

SBP: systolic blood pressure

DBP: diastolic blood pressure

Hyperten.: hypertension

FBS: fasting blood sugar

HDL: high-density-lipoprotein

26

Appendix 3: Bland-Altman plots three ECG measurements

Figure 7: Bland-Altman plot for S

wave amplitude in V1. The mean

difference is close to zero with a

maximum difference of 2.

Figure 8: Bland-Altman plot for R


difference is around 0.3 with a


Figure 9: Bland-Altman plot for R


difference is around 0.3 with a


the predictive value of left ventricular hypertrophy on...

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