- Email: [email protected]
Racial differences in the ECG — selected aspects
Available online at www.sciencedirect.com
ScienceDirect Journal of Electrocardiology 47 (2014) 809 – 814 www.jecgonline.com
Racial differences in the ECG — selected aspects
P.W. Macfarlane, DSc, FRCP, FRSE, a,⁎ I.A. Katibi, MD, FACC, b S.T. Hamde, PhD, c D. Singh, PhD, d E. Clark, MA, a B. Devine, MSc, a B.G. Francq, PhD, e S. Lloyd, MSc, e V. Kumar, PhD f a
Institute of Cardiovascular and Medical Sciences, University of Glasgow, Scotland b Faculty of Medicine, University of Ilorin, Nigeria c Institute of Engineering and Technology, Vishnupuri, India d National Institute of Technology, Jalandhar, India e Robertson Centre for Biostatistics, University of Glasgow, Scotland f Indian Institute of Technology, Roorkee, India
Abstract
Introduction: Racial differences in the ECG have been known about for many years but there has been no significant comparison of large population groups. This study set out to remedy this shortcoming. Methods: Digital ECG data were available for four population samples gathered in Scotland, Taiwan, Nigeria and India. All ECGs were recorded in the different countries and processed centrally by the University of Glasgow ECG Analysis Program. Measurements were analysed statistically to look for significant differences. Results: There were 4223 individuals in the study (2559 males and 1664 females). In general terms, findings such as QRS duration being longer in males than females applied to all four races. More specifically, QRS voltages were higher in young black males compared to others, while ST amplitudes, as in V2, were higher in Chinese and Nigerian males than in Caucasians. Conclusion: Race requires to be taken into account to enhance automated interpretation of the ECG. © 2014 Elsevier Inc. All rights reserved.
Keywords:
ECG; Race; Normal limits; Caucasian; Chinese; Indian; Black
Introduction There have been a number of studies assessing the normal limits of the adult ECG in different populations [e.g. 1–3]. However, although there have been a few reports involving blacks living in North America and Caucasians on the same continent assessing ethnic differences [4] to a limited extent, or using ECG criteria which were race dependent [5], there has been no substantial comparison of ECG measurements in multiple groups of individuals from different ethnic backgrounds. The University of Glasgow Electrocardiology Section has had a special interest in the normal limits of the ECG for many years and has published details of normal limits of the ECG in Chinese [6], Caucasians [7] and Nigerians [8]. More recently, a study on the normal limits of the ECG in Indians has been completed and a publication is in preparation. The opportunity therefore arose to look at a comparison of ECG measurements in four different populations. The Chinese ⁎ Corresponding author at: Electrocardiology Section, Level 1, New Lister Building, Royal Infirmary, Glasgow G31 2ER, Scotland. E-mail address: [email protected] http://dx.doi.org/10.1016/j.jelectrocard.2014.08.003 0022-0736/© 2014 Elsevier Inc. All rights reserved.
cohort and the Nigerian cohort clearly represent two different ethnic groups. On the other hand, it is debatable whether Scottish Caucasians and Indian Caucasians are of different ethnicity but given the fact that the population of India is the order of 1.25 billion and contains 17.5% of the world's population, it seemed of interest to separate ECGs from Indian and British individuals in this study. Methods ECGs were collected from the four populations as follows. a) White Caucasian ECGs collected from Scotland were obtained using methods described elsewhere [9]. In brief, using a locally developed microprocessor based electrocardiograph, all 12 leads were effectively recorded simultaneously and sampled at 500 samples per second. All individuals were examined by a physician to ensure that they were apparently healthy.
810
P.W. Macfarlane et al. / Journal of Electrocardiology 47 (2014) 809–814
b) Chinese Population A cohort of Chinese individuals was obtained from the Veterans General Hospital in Taipei, Taiwan. All individuals were screened for abnormalities of the cardiovascular system that might affect an ECG and all were free of such problems. Full details can be found elsewhere [6]. ECGs were recorded on a Siemens Mingorec, again with a sampling rate of 500 samples per second. Digital data were stored on a cassette which was sent to Glasgow for replay to a Siemens Mingocare system from which the data were extracted for further processing. c) Nigerian Population Full details of the data acquisition of ECGs in Nigeria can be found elsewhere [8]. In brief, a Burdick Atria 6100 was used to record the ECGs with a sampling rate of 500 samples per second. Data were transferred to a PC based ECG Management package and then written to CD for forwarding to Glasgow. All of the subjects in this group were assessed by medical staff from the University of Ilorin and were regarded as being apparently healthy. d) Indian Cohort Three centres in India were used for recording ECGs and in each case, the Burdick Atria 6100 electrocardiograph was used. The centres were at the Indian Institute of Technonlogy, Roorkee, Uttarakhand, the National Institute of Technology at Jalandhar, Punjab and the Institute of Engineering and Technology at Vishnupuri, Maharashtra. In these centres, volunteers completed a medical questionnaire to determine if they were apparently healthy. ECG analysis All ECGs were analysed by the 2010 version of the University of Glasgow ECG software [10,11]. All the standard ECG measurements were then passed to the Robertson Centre for Biostatistics at the University of Glasgow for further analysis. Statistical analysis For a number of measures, regression techniques were used to find upper and lower limits of normality deemed to be the 98th and 2nd percentiles of the distribution respectively. This was done separately for males and females and for each population sample. For data that were normally distributed, linear regression was used and normal limits determined from a knowledge of the mean regression line. Otherwise, quantile regression was used and upper and lower limits of normality had to be determined separately. The quantiles were also computed with a moving quantile window of 10 years. For example, the first window was 18–28 years with a centre of 22.5 years, while the second window was 19–29 years with a centre of 23.5 years, and so on.
Tests of significance were carried out for each sex on selected ECG measures to assess the effect of age, race and their interaction. Results The distribution of the populations in the four different countries is shown in Fig. 1 in the form of histograms. There were a total of 4223 individuals in the comparison (2559 males and 1664 females aged between 18 and 89 years) and a further breakdown is given in Table 1. In this short paper, only a few significant findings can be presented. Table 2 shows the p-values for the effect of race, age and their interaction on the selected measures for each sex. These p-values should be treated with caution. Significant p-values (b 0.05) suggest that there is indeed a relationship between the ECG measure and the variables investigated (age, race or their interaction). Wave durations QRS duration Mean QRS duration was found to be longer in males than females in all four groups (See Table 3). The highest QRS duration was in the Scottish Caucasian males and the lowest was in the Indian Caucasians. For women, the shortest QRS duration was in the Indian group and the longest in the Scottish Caucasians. From Table 2, it can be seen that for males, there was a significant effect of age and race on QRS duration and a significant interaction between them. For women, there was a significant effect due to race but not age, while there was no interaction between age and race. QT interval With respect to QT interval corrected using the Hodges equation [12], it was a consistent finding that females had a slightly longer QT than males of the order of 10 ms (See Table 3). There was a statistically significant effect of age for males and females but an effect of race was found in males only, giving a significant interaction between age and race in men. There were no ethnic differences of statistical significance in women noted though the Chinese women had a mean QT that was approximately 9 ms longer than the nearest value in Caucasians and 13.5 ms longer than in Indians. Table 3 shows the mean QT intervals corrected using three other well known equations, namely those of Bazett [13], Fridericia [14] and Sagie et al. — the Framingham method [15]. The striking feature is that the Hodges, Framingham and Fridericia formulae gave essentially similar mean values while the Bazett formula was always out of line with the others. This parallels the same findings in Scottish Caucasians shown in an earlier publication involving our lab[16]. Amplitudes Sokolow–Lyon Index. It is not possible to consider all the 12 lead wave amplitudes and so the classical Sokolow–Lyon
P.W. Macfarlane et al. / Journal of Electrocardiology 47 (2014) 809–814 350
CHINESE
300
250
250
200
200
150
150
100
100
50
50
0
0 18-29
30-39
40-49
50-59
60+
Age Group (Years)
18-29
Male Female
30-39
40-49
50-59
60+
Age Group (Years)
300
300
250
250
200
200
150
150
100
100
50
50
Male Female
CAUCASIAN
350
AFRICAN
350
0
INDIAN
350
300
811
0 18-29
30-39
40-49
50-59
Age Group (Years)
60+
Male Female
18-29
30-39
40-49
50-59
Age Group (Years)
60+
Male Female
Fig. 1. The age and sex distribution of the four cohorts involved in the study.
Index [17] has been chosen to illustrate some general points. In this study, the Index was taken to be SV1 þ maxðRV5; RV6Þ: Fig. 2 shows the upper and lower limits of normal based on the mean regression lines for a normally distributed variable. Table 3 shows the mean Index for the different groups. The dependence of the Index on age in males is clearly shown in Fig. 2, which also demonstrates that blacks have higher QRS voltage than Caucasians. Statistically, age indeed has a significant effect on the Index for males. Similarly, race has a significant effect and there is a significant interaction between age and race for males. In the younger age groups, the difference between the Chinese or Indian and the African upper limit of normal is the order of 1 mV. For women, the Index increases with age, markedly so in blacks. This trend is less clear in Indians and Chinese but is not the case in Caucasians. Hence, age is not significantly related to the Index in women. On the other hand, there are clear racial differences which are significant (p b 0.001), e.g. the values were 0.5 mV higher in blacks than in the other female groups at the younger end of the age spectrum increasing to over
Table 1 Sex distribution of total numbers in each cohort.
Caucasian Chinese Indian Black Total
Male
Female
Total
859 248 670 782 2559
637 255 293 479 1664
1496 503 963 1261 4223
1 mV at the other extreme. This leads to a significant interaction between age and race in women (Table 2). The Cornell Index SV3 + RaVL [18] is also listed for interest in Table 3. The gender differences are marked as is well known but differences due to ethnicity are much smaller. What can be seen is that the Chinese have the lowest values both for males and females. In addition, blacks do not have larger mean indices than Caucasian males. Table 2 provides data on significance of the differences. ST amplitude. With respect to ST amplitudes, the limb leads had upper limits of normal that did not exceed 100 μV in males and 75 μV in females. There was a statistically significant decrease in the upper limit of normal with increasing age in males in all limb leads except in lead aVR where the trend was reversed as expected, and in lead III where there was no link with age. There was a significant effect of race in males in limb leads except III and aVL. There was no interaction between age and race. There were similar statistically significant effects of age in women but less influence of race, again with no significant interaction between age and race. In the precordial leads, V1 showed a slightly higher upper limit of normal in young African males compared to the other groups. In females, the upper limit of normal increased very slightly with increasing age but was of the order of 100 μV in all cases. With respect to V2, African and Chinese males clearly had higher ST amplitudes than the Caucasians (Table 3, Fig. 3). In all races, there was a decrease in the upper normal limit with increasing age in males. The upper limit of normal in women in V2 was clearly significantly lower than in males
812
P.W. Macfarlane et al. / Journal of Electrocardiology 47 (2014) 809–814
Table 2 ECG data: P Values of regression parameters, by Gender ('Classical' regression). Measure
Age_M
Race_M
Age*Race_M
Age_F
Race_F
Age*Race_F
QD QTCH SOKLYON CORNELL STA_V1 STA_V2 STA_V5
b .001 b .001 b .001 0.002 b .001 b .001 b .001
b.001 b.001 b.001 0.003 b.001 b.001 0.019
0.040 0.016 b.001 0.198 0.001 0.098 0.624
0.362 b.001 0.089 b.001 b.001 0.079 b.001
0.002 0.167 b.001 b.001 b.001 b.001 0.194
0.328 0.365 b.001 0.001 0.041 0.150 0.296
M = Male; F = Female; QD = QRS duration; QTCH = QT corrected by Hodges equation; SOKLYON = Sokolow–Lyon Index; Cornell = Cornell Index; STA = ST amplitude. Age*Race denotes the interaction of age and race.
Caucasians, whether they be white or South Asian (Indian). This study also showed an unexpected increase in QRS voltage with increasing age in Nigerian females, the upper limit of normal for the Index being over 1 mV higher in the African group compared to the other groups in the over 60 age range. It has to be acknowledged that there were small numbers of women in the Nigerian and other cohorts but the voltage trend is marked. Clearly this is something which has to be accounted for in any diagnostic criteria used for automated ECG analysis. This applies particularly to the African males and females. The Sokolow–Lyon Index was not introduced as being age and race dependent but clearly it is. This study has underlined that fact. It also means that criteria for left ventricular hypertrophy in younger individuals should be adjusted for age and race as is the case in the University of Glasgow ECG analysis program. Another area of importance relates to ST amplitude because of its relevance for reporting of ST Elevation Myocardial Infarction (STEMI). The African and Chinese males had upper limits of normal which were approximately 50 μV higher than Caucasians in the over 50 age group and it would appear that STEMI criteria need to be adjusted for these two ethnic groups. This difference may seem small but it represents approximately a 20% change above the Caucasian upper limit of normal at age 50 years and could lead to false positive reports of STEMI in Chinese and blacks if criteria for Caucasians are used. Again, this applies
and in general terms, was highest in the African group and the Chinese group. However, the value remained fairly constant with age in women leading to a non-significant relationship with age although there was a significant difference due to ethnicity. Table 2 shows the significance values for ST amplitudes in V1, V2 and V5.
Discussion In a study such as this, it would be possible to write a book on the methodology and findings but in a summary of a conference presentation, only a few ECG measures of interest can be selected to highlight some important ethnic differences. In any study of apparently healthy individuals, it is impossible to exclude latent disease, even with advanced scanning techniques which could not realistically be implemented in such studies. It was not possible to standardise methods in different countries particularly as there were several years between the collection of the different cohorts, but the volunteers were included on the basis that reasonable methods were used to ensure that no-one had any obvious signs of disease liable to affect ECG appearances. Ethnic differences in QRS amplitude have been documented previously essentially between blacks and
Table 3 Means and standard deviations of selected ECG measures by gender. (See text for further discussion.) Caucasian QRS duration (ms) QT Interval corrected by Hodges (ms) QT Interval corrected by Bazett (ms) QT Interval corrected by Fridericia (ms) QT Interval corrected by Framingham (ms) Sokolow–Lyon Index (μV) Cornell Index (μV) ST amplitude in V2 (age 50 to 60 years) (μV)
Male Female Male Female Male Female Male Female Male Female Male Female Male Female Male Female
93.75 86.07 402.83 409.63 413.42 426.77 403.14 410.23 403.88 410.80 2899.13 2372.35 1577.74 1130.65 104.22 52.36
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
Chinese 9.82 7.68 16.90 15.18 19.81 17.88 17.38 16.56 16.70 15.76 794.49 636.74 597.40 454.75 61.13 38.64
92.92 85.76 401.28 418.55 409.90 431.84 401.68 420.32 402.45 420.39 2743.27 2238.40 1326.11 989.63 144.08 71.98
Indian ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
9.25 8.26 15.99 17.53 18.15 21.07 15.97 18.20 15.73 17.33 724.17 585.89 472.18 427.97 76.97 31.62
87.08 82.07 388.77 404.99 403.07 423.81 387.98 404.84 390.07 405.68 2708.55 2253.55 1475.43 1101.85 105.29 33.88
Black ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
8.11 7.30 15.60 14.07 19.62 17.56 16.90 15.68 15.89 14.73 795.16 705.02 625.01 480.11 64.02 49.24
87.91 83.44 392.81 406.19 406.45 424.91 392.25 405.43 393.67 406.04 3408.27 3089.47 1543.47 1239.18 120.01 62.36
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
9.37 7.62 15.57 16.49 20.99 20.15 16.40 18.62 15.73 17.57 906.85 788.74 584.92 478.53 54.89 35.65
P.W. Macfarlane et al. / Journal of Electrocardiology 47 (2014) 809–814
813
Fig. 2. The upper and lower limits of the Sokolow–Lyon Index based on linear regression in the four cohorts, both for males and females separately.
particularly to males. Indeed, ST amplitude in males in V2 was significantly related to age and race though there was no significant interaction between them. In females, race was the only variable that was significantly linked with ST amplitude in V2. It might be thought that the increased ST amplitude in Chinese compared to Caucasians was surprising but in fact, one of the authors had indirectly documented this 25 years ago [6] in the same population! While other measures such as QRS duration showed the well known difference between males and females, it appears that there is little that can be done to capitalise on such findings given that for all the years that automated ECG interpretation has been in existence, there appear to be few, if any, criteria which differentiate between males and females when considering abnormal values for QRS duration that could be used in the diagnosis of conduction abnormalities, for example. The mean differences of the order of a few milliseconds between ethnic groups are within the range of measurement error that might be expected in automated or manual measurement of QRS duration. A similar comment
can be made with respect to the male female differences in mean values. Furthermore, it has recently been shown that different computer programs may give different measurements of QRS durations and other durations and intervals for the same ECGs [19]. Thus, trying to refine QRS Duration criteria by ethnicity is unlikely to be meaningful. The study has shown, as previously [16], that the Bazett correction for QT interval duration is out of line with other corrections and is best avoided. As for QRS duration, automated measurement programs may give different QT values for the same ECGs [16] but the difference between the upper limit of normal QTc in men and women is large enough to allow different criteria for abnormality to be used. In addition, the present study suggests that corrected QT in black and Indian males and females is shorter than in the other ethnic groups and so some consideration should be given to making QTc dependent on ethnicity. A question which arises is why there are differences in ECG measurements between ethnic groups. Some QRS voltage differences might be explained by body mass index.
Fig. 3. The upper and lower limits of the ST amplitude in V2 based on linear regression in the four cohorts, both for males and females separately.
814
P.W. Macfarlane et al. / Journal of Electrocardiology 47 (2014) 809–814
For example, Chinese individuals have, on average, a lower body mass index than age and sex matched Caucasians. On the other hand, the increase in QRS voltage in Nigerian females with increasing age may be a cultural feature in that a more substantial index in older Nigerian ladies may be regarded as a sign of greater maturity and therefore desirable. Naturally, genetic factors which were not explored in this study could largely be responsible. This study shows that there are significant effects of race on ECG measures. The problem remains as to how these can be utilised in practice. The University of Glasgow 12 lead ECG analysis program [10] has incorporated a facility for the use of race almost since its inception. It can also make use of clinical diagnosis and drug therapy. However, experience shows that ECG technicians and nursing staff, who are increasingly recording ECGs with inadequate training, fail to record age, sex and particularly clinical details of a patient when obtaining an ECG. The use of race in diagnostic criteria will increase and hence it will become essential for staff to input such a variable to an ECG machine capable of making an automated ECG analysis. Manufacturers will also have to be alive to this situation and make race an input variable in the user interface. References [1] Simonson E. Differentiation between normal and abnormal in electrocardiography. St Louis, MI: Mosby; 1961. [2] Mason JW, Ramseth DJ, Chanter DO, Moon TE, Goodman DB, Mendzelevski B. Electrocardiographic reference ranges derived from 79,743 ambulatory subjects. J Electrocardiol 2007;40:228–34. [3] Wu J, Kors JA, Rijnbeek PR, van Herpen G, Lu Z, Xu C. Normal limits of the electrocardiogram in Chinese subjects. Int J Cardiol 2003;87:37–51. [4] Rautaharju PM, Zhou SH, Calhoun HP. Ethnic differences in ECG amplitudes in North American white, black and Hispanic men and women: effect of obesity and age. J Electrocardiol 1994;27(Suppl 1):20–31. [5] Jain A, Tandri H, Dalal D, Chahal D, Soliman EZ, Prineas RJ, et al. Diagnostic and prognostic utility of ECG for left ventricular hypertrophy defined by MRI in relationship to ethnicity: the Multi-Ethnic Study of Atherosclerosis (MESA). Am Heart J 2010;159:652–8.
[6] Chen CY, Chiang BN, Macfarlane PW. Normal limits of the electrocardiogram in a Chinese population. J Electrocardiol 1989;22:1–15. [7] Macfarlane PW, Lawrie TDV. Appendix 1. Adult normal limits. p2058. In: Macfarlane PW, van Oosterom A, Pahlm O, Kligfield P, Janse J, Camm J, editors. Comprehensive electrocardiology. 2nd ed. London: Springer-Verlag; 2011. [8] Katibi I, Clark EN, Devine B, Lloyd SM, Macfarlane P. Normal limits of the electrocardiogram in Nigerians. J Electrocardiol 2013;46:289–95. [9] Macfarlane PW, Lawrie TDV. The normal electrocardiogram and vectorcardiogram. p407. In: Macfarlane PW, Lawrie TDV, editors. Comprehensive electrocardiology, 1st ed., vol. 1. Oxford: Pergamon Press; 1989. [10] Macfarlane PW, Devine B, Latif S, McLaughlin S, Shoat DB, Watts MP. Methodology of ECG interpretation in the Glasgow program. Methods Inf Med 1990;29:354–61. [11] Macfarlane PW, Devine B, Clark E. The University of Glasgow (Uni-G) analysis program. Comput Cardiol 2005;32:451–4. [12] Hodges M, Salerno D, Erlien D. Bazett's QT correction reviewed. Evidence that a linear QT correction for heart is better. J Am Coll Cardiol 1983;1:694. [13] Bazett HC. An analysis of the time relations of electrocardiograms. Heart 1920;7:353–70. [14] Fridericia LS. Die systolendauer im electrokardiogramm bei normalenmenschen und bei herzkranken. Acta Med Scand 1920; 53:469–86. [15] Sagie A, Larson MG, Goldberg RJ, Bengtson JR, Levy D. An improved method for adjusting the QT interval for heart rate (the Framingham Study). Am J Cardiol 1992;70:797–801. [16] Luo S, Michler K, Johnston P, Macfarlane PW. A comparison of commonly used QT correction formulae: the effects of heart rate on the QTc of normal ECGs. J Electrocardiol 2004;37:81–90 [Suppl]. [17] Sokolow M, Lyon TP. The ventricular complex in left ventricular hypertrophy as obtained by unipolar precordial and limb leads. Am Heart J 1949;37:161–86. [18] Casale PN, Devereux RB, Alonso DR, Campo E, Kligfield P. Improved sex-specific criteria of left ventricular hypertrophy for clinical and computer interpretation of electrocardiograms: validation with autopsy findings. Circulation 1987;75:565–72. [19] Kligfield P, Badolini F, Rowlandsom I, Xue J, Park E, Derme B, et al. Comparison of automated measurements of electrocardiographic intervals and durations by computer-based algorithms of digital electrocardiographs. Am Heart J 2014;167, 150.e1–159.
ScienceDirect Journal of Electrocardiology 47 (2014) 809 – 814 www.jecgonline.com
Racial differences in the ECG — selected aspects
P.W. Macfarlane, DSc, FRCP, FRSE, a,⁎ I.A. Katibi, MD, FACC, b S.T. Hamde, PhD, c D. Singh, PhD, d E. Clark, MA, a B. Devine, MSc, a B.G. Francq, PhD, e S. Lloyd, MSc, e V. Kumar, PhD f a
Institute of Cardiovascular and Medical Sciences, University of Glasgow, Scotland b Faculty of Medicine, University of Ilorin, Nigeria c Institute of Engineering and Technology, Vishnupuri, India d National Institute of Technology, Jalandhar, India e Robertson Centre for Biostatistics, University of Glasgow, Scotland f Indian Institute of Technology, Roorkee, India
Abstract
Introduction: Racial differences in the ECG have been known about for many years but there has been no significant comparison of large population groups. This study set out to remedy this shortcoming. Methods: Digital ECG data were available for four population samples gathered in Scotland, Taiwan, Nigeria and India. All ECGs were recorded in the different countries and processed centrally by the University of Glasgow ECG Analysis Program. Measurements were analysed statistically to look for significant differences. Results: There were 4223 individuals in the study (2559 males and 1664 females). In general terms, findings such as QRS duration being longer in males than females applied to all four races. More specifically, QRS voltages were higher in young black males compared to others, while ST amplitudes, as in V2, were higher in Chinese and Nigerian males than in Caucasians. Conclusion: Race requires to be taken into account to enhance automated interpretation of the ECG. © 2014 Elsevier Inc. All rights reserved.
Keywords:
ECG; Race; Normal limits; Caucasian; Chinese; Indian; Black
Introduction There have been a number of studies assessing the normal limits of the adult ECG in different populations [e.g. 1–3]. However, although there have been a few reports involving blacks living in North America and Caucasians on the same continent assessing ethnic differences [4] to a limited extent, or using ECG criteria which were race dependent [5], there has been no substantial comparison of ECG measurements in multiple groups of individuals from different ethnic backgrounds. The University of Glasgow Electrocardiology Section has had a special interest in the normal limits of the ECG for many years and has published details of normal limits of the ECG in Chinese [6], Caucasians [7] and Nigerians [8]. More recently, a study on the normal limits of the ECG in Indians has been completed and a publication is in preparation. The opportunity therefore arose to look at a comparison of ECG measurements in four different populations. The Chinese ⁎ Corresponding author at: Electrocardiology Section, Level 1, New Lister Building, Royal Infirmary, Glasgow G31 2ER, Scotland. E-mail address: [email protected] http://dx.doi.org/10.1016/j.jelectrocard.2014.08.003 0022-0736/© 2014 Elsevier Inc. All rights reserved.
cohort and the Nigerian cohort clearly represent two different ethnic groups. On the other hand, it is debatable whether Scottish Caucasians and Indian Caucasians are of different ethnicity but given the fact that the population of India is the order of 1.25 billion and contains 17.5% of the world's population, it seemed of interest to separate ECGs from Indian and British individuals in this study. Methods ECGs were collected from the four populations as follows. a) White Caucasian ECGs collected from Scotland were obtained using methods described elsewhere [9]. In brief, using a locally developed microprocessor based electrocardiograph, all 12 leads were effectively recorded simultaneously and sampled at 500 samples per second. All individuals were examined by a physician to ensure that they were apparently healthy.
810
P.W. Macfarlane et al. / Journal of Electrocardiology 47 (2014) 809–814
b) Chinese Population A cohort of Chinese individuals was obtained from the Veterans General Hospital in Taipei, Taiwan. All individuals were screened for abnormalities of the cardiovascular system that might affect an ECG and all were free of such problems. Full details can be found elsewhere [6]. ECGs were recorded on a Siemens Mingorec, again with a sampling rate of 500 samples per second. Digital data were stored on a cassette which was sent to Glasgow for replay to a Siemens Mingocare system from which the data were extracted for further processing. c) Nigerian Population Full details of the data acquisition of ECGs in Nigeria can be found elsewhere [8]. In brief, a Burdick Atria 6100 was used to record the ECGs with a sampling rate of 500 samples per second. Data were transferred to a PC based ECG Management package and then written to CD for forwarding to Glasgow. All of the subjects in this group were assessed by medical staff from the University of Ilorin and were regarded as being apparently healthy. d) Indian Cohort Three centres in India were used for recording ECGs and in each case, the Burdick Atria 6100 electrocardiograph was used. The centres were at the Indian Institute of Technonlogy, Roorkee, Uttarakhand, the National Institute of Technology at Jalandhar, Punjab and the Institute of Engineering and Technology at Vishnupuri, Maharashtra. In these centres, volunteers completed a medical questionnaire to determine if they were apparently healthy. ECG analysis All ECGs were analysed by the 2010 version of the University of Glasgow ECG software [10,11]. All the standard ECG measurements were then passed to the Robertson Centre for Biostatistics at the University of Glasgow for further analysis. Statistical analysis For a number of measures, regression techniques were used to find upper and lower limits of normality deemed to be the 98th and 2nd percentiles of the distribution respectively. This was done separately for males and females and for each population sample. For data that were normally distributed, linear regression was used and normal limits determined from a knowledge of the mean regression line. Otherwise, quantile regression was used and upper and lower limits of normality had to be determined separately. The quantiles were also computed with a moving quantile window of 10 years. For example, the first window was 18–28 years with a centre of 22.5 years, while the second window was 19–29 years with a centre of 23.5 years, and so on.
Tests of significance were carried out for each sex on selected ECG measures to assess the effect of age, race and their interaction. Results The distribution of the populations in the four different countries is shown in Fig. 1 in the form of histograms. There were a total of 4223 individuals in the comparison (2559 males and 1664 females aged between 18 and 89 years) and a further breakdown is given in Table 1. In this short paper, only a few significant findings can be presented. Table 2 shows the p-values for the effect of race, age and their interaction on the selected measures for each sex. These p-values should be treated with caution. Significant p-values (b 0.05) suggest that there is indeed a relationship between the ECG measure and the variables investigated (age, race or their interaction). Wave durations QRS duration Mean QRS duration was found to be longer in males than females in all four groups (See Table 3). The highest QRS duration was in the Scottish Caucasian males and the lowest was in the Indian Caucasians. For women, the shortest QRS duration was in the Indian group and the longest in the Scottish Caucasians. From Table 2, it can be seen that for males, there was a significant effect of age and race on QRS duration and a significant interaction between them. For women, there was a significant effect due to race but not age, while there was no interaction between age and race. QT interval With respect to QT interval corrected using the Hodges equation [12], it was a consistent finding that females had a slightly longer QT than males of the order of 10 ms (See Table 3). There was a statistically significant effect of age for males and females but an effect of race was found in males only, giving a significant interaction between age and race in men. There were no ethnic differences of statistical significance in women noted though the Chinese women had a mean QT that was approximately 9 ms longer than the nearest value in Caucasians and 13.5 ms longer than in Indians. Table 3 shows the mean QT intervals corrected using three other well known equations, namely those of Bazett [13], Fridericia [14] and Sagie et al. — the Framingham method [15]. The striking feature is that the Hodges, Framingham and Fridericia formulae gave essentially similar mean values while the Bazett formula was always out of line with the others. This parallels the same findings in Scottish Caucasians shown in an earlier publication involving our lab[16]. Amplitudes Sokolow–Lyon Index. It is not possible to consider all the 12 lead wave amplitudes and so the classical Sokolow–Lyon
P.W. Macfarlane et al. / Journal of Electrocardiology 47 (2014) 809–814 350
CHINESE
300
250
250
200
200
150
150
100
100
50
50
0
0 18-29
30-39
40-49
50-59
60+
Age Group (Years)
18-29
Male Female
30-39
40-49
50-59
60+
Age Group (Years)
300
300
250
250
200
200
150
150
100
100
50
50
Male Female
CAUCASIAN
350
AFRICAN
350
0
INDIAN
350
300
811
0 18-29
30-39
40-49
50-59
Age Group (Years)
60+
Male Female
18-29
30-39
40-49
50-59
Age Group (Years)
60+
Male Female
Fig. 1. The age and sex distribution of the four cohorts involved in the study.
Index [17] has been chosen to illustrate some general points. In this study, the Index was taken to be SV1 þ maxðRV5; RV6Þ: Fig. 2 shows the upper and lower limits of normal based on the mean regression lines for a normally distributed variable. Table 3 shows the mean Index for the different groups. The dependence of the Index on age in males is clearly shown in Fig. 2, which also demonstrates that blacks have higher QRS voltage than Caucasians. Statistically, age indeed has a significant effect on the Index for males. Similarly, race has a significant effect and there is a significant interaction between age and race for males. In the younger age groups, the difference between the Chinese or Indian and the African upper limit of normal is the order of 1 mV. For women, the Index increases with age, markedly so in blacks. This trend is less clear in Indians and Chinese but is not the case in Caucasians. Hence, age is not significantly related to the Index in women. On the other hand, there are clear racial differences which are significant (p b 0.001), e.g. the values were 0.5 mV higher in blacks than in the other female groups at the younger end of the age spectrum increasing to over
Table 1 Sex distribution of total numbers in each cohort.
Caucasian Chinese Indian Black Total
Male
Female
Total
859 248 670 782 2559
637 255 293 479 1664
1496 503 963 1261 4223
1 mV at the other extreme. This leads to a significant interaction between age and race in women (Table 2). The Cornell Index SV3 + RaVL [18] is also listed for interest in Table 3. The gender differences are marked as is well known but differences due to ethnicity are much smaller. What can be seen is that the Chinese have the lowest values both for males and females. In addition, blacks do not have larger mean indices than Caucasian males. Table 2 provides data on significance of the differences. ST amplitude. With respect to ST amplitudes, the limb leads had upper limits of normal that did not exceed 100 μV in males and 75 μV in females. There was a statistically significant decrease in the upper limit of normal with increasing age in males in all limb leads except in lead aVR where the trend was reversed as expected, and in lead III where there was no link with age. There was a significant effect of race in males in limb leads except III and aVL. There was no interaction between age and race. There were similar statistically significant effects of age in women but less influence of race, again with no significant interaction between age and race. In the precordial leads, V1 showed a slightly higher upper limit of normal in young African males compared to the other groups. In females, the upper limit of normal increased very slightly with increasing age but was of the order of 100 μV in all cases. With respect to V2, African and Chinese males clearly had higher ST amplitudes than the Caucasians (Table 3, Fig. 3). In all races, there was a decrease in the upper normal limit with increasing age in males. The upper limit of normal in women in V2 was clearly significantly lower than in males
812
P.W. Macfarlane et al. / Journal of Electrocardiology 47 (2014) 809–814
Table 2 ECG data: P Values of regression parameters, by Gender ('Classical' regression). Measure
Age_M
Race_M
Age*Race_M
Age_F
Race_F
Age*Race_F
QD QTCH SOKLYON CORNELL STA_V1 STA_V2 STA_V5
b .001 b .001 b .001 0.002 b .001 b .001 b .001
b.001 b.001 b.001 0.003 b.001 b.001 0.019
0.040 0.016 b.001 0.198 0.001 0.098 0.624
0.362 b.001 0.089 b.001 b.001 0.079 b.001
0.002 0.167 b.001 b.001 b.001 b.001 0.194
0.328 0.365 b.001 0.001 0.041 0.150 0.296
M = Male; F = Female; QD = QRS duration; QTCH = QT corrected by Hodges equation; SOKLYON = Sokolow–Lyon Index; Cornell = Cornell Index; STA = ST amplitude. Age*Race denotes the interaction of age and race.
Caucasians, whether they be white or South Asian (Indian). This study also showed an unexpected increase in QRS voltage with increasing age in Nigerian females, the upper limit of normal for the Index being over 1 mV higher in the African group compared to the other groups in the over 60 age range. It has to be acknowledged that there were small numbers of women in the Nigerian and other cohorts but the voltage trend is marked. Clearly this is something which has to be accounted for in any diagnostic criteria used for automated ECG analysis. This applies particularly to the African males and females. The Sokolow–Lyon Index was not introduced as being age and race dependent but clearly it is. This study has underlined that fact. It also means that criteria for left ventricular hypertrophy in younger individuals should be adjusted for age and race as is the case in the University of Glasgow ECG analysis program. Another area of importance relates to ST amplitude because of its relevance for reporting of ST Elevation Myocardial Infarction (STEMI). The African and Chinese males had upper limits of normal which were approximately 50 μV higher than Caucasians in the over 50 age group and it would appear that STEMI criteria need to be adjusted for these two ethnic groups. This difference may seem small but it represents approximately a 20% change above the Caucasian upper limit of normal at age 50 years and could lead to false positive reports of STEMI in Chinese and blacks if criteria for Caucasians are used. Again, this applies
and in general terms, was highest in the African group and the Chinese group. However, the value remained fairly constant with age in women leading to a non-significant relationship with age although there was a significant difference due to ethnicity. Table 2 shows the significance values for ST amplitudes in V1, V2 and V5.
Discussion In a study such as this, it would be possible to write a book on the methodology and findings but in a summary of a conference presentation, only a few ECG measures of interest can be selected to highlight some important ethnic differences. In any study of apparently healthy individuals, it is impossible to exclude latent disease, even with advanced scanning techniques which could not realistically be implemented in such studies. It was not possible to standardise methods in different countries particularly as there were several years between the collection of the different cohorts, but the volunteers were included on the basis that reasonable methods were used to ensure that no-one had any obvious signs of disease liable to affect ECG appearances. Ethnic differences in QRS amplitude have been documented previously essentially between blacks and
Table 3 Means and standard deviations of selected ECG measures by gender. (See text for further discussion.) Caucasian QRS duration (ms) QT Interval corrected by Hodges (ms) QT Interval corrected by Bazett (ms) QT Interval corrected by Fridericia (ms) QT Interval corrected by Framingham (ms) Sokolow–Lyon Index (μV) Cornell Index (μV) ST amplitude in V2 (age 50 to 60 years) (μV)
Male Female Male Female Male Female Male Female Male Female Male Female Male Female Male Female
93.75 86.07 402.83 409.63 413.42 426.77 403.14 410.23 403.88 410.80 2899.13 2372.35 1577.74 1130.65 104.22 52.36
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
Chinese 9.82 7.68 16.90 15.18 19.81 17.88 17.38 16.56 16.70 15.76 794.49 636.74 597.40 454.75 61.13 38.64
92.92 85.76 401.28 418.55 409.90 431.84 401.68 420.32 402.45 420.39 2743.27 2238.40 1326.11 989.63 144.08 71.98
Indian ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
9.25 8.26 15.99 17.53 18.15 21.07 15.97 18.20 15.73 17.33 724.17 585.89 472.18 427.97 76.97 31.62
87.08 82.07 388.77 404.99 403.07 423.81 387.98 404.84 390.07 405.68 2708.55 2253.55 1475.43 1101.85 105.29 33.88
Black ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
8.11 7.30 15.60 14.07 19.62 17.56 16.90 15.68 15.89 14.73 795.16 705.02 625.01 480.11 64.02 49.24
87.91 83.44 392.81 406.19 406.45 424.91 392.25 405.43 393.67 406.04 3408.27 3089.47 1543.47 1239.18 120.01 62.36
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
9.37 7.62 15.57 16.49 20.99 20.15 16.40 18.62 15.73 17.57 906.85 788.74 584.92 478.53 54.89 35.65
P.W. Macfarlane et al. / Journal of Electrocardiology 47 (2014) 809–814
813
Fig. 2. The upper and lower limits of the Sokolow–Lyon Index based on linear regression in the four cohorts, both for males and females separately.
particularly to males. Indeed, ST amplitude in males in V2 was significantly related to age and race though there was no significant interaction between them. In females, race was the only variable that was significantly linked with ST amplitude in V2. It might be thought that the increased ST amplitude in Chinese compared to Caucasians was surprising but in fact, one of the authors had indirectly documented this 25 years ago [6] in the same population! While other measures such as QRS duration showed the well known difference between males and females, it appears that there is little that can be done to capitalise on such findings given that for all the years that automated ECG interpretation has been in existence, there appear to be few, if any, criteria which differentiate between males and females when considering abnormal values for QRS duration that could be used in the diagnosis of conduction abnormalities, for example. The mean differences of the order of a few milliseconds between ethnic groups are within the range of measurement error that might be expected in automated or manual measurement of QRS duration. A similar comment
can be made with respect to the male female differences in mean values. Furthermore, it has recently been shown that different computer programs may give different measurements of QRS durations and other durations and intervals for the same ECGs [19]. Thus, trying to refine QRS Duration criteria by ethnicity is unlikely to be meaningful. The study has shown, as previously [16], that the Bazett correction for QT interval duration is out of line with other corrections and is best avoided. As for QRS duration, automated measurement programs may give different QT values for the same ECGs [16] but the difference between the upper limit of normal QTc in men and women is large enough to allow different criteria for abnormality to be used. In addition, the present study suggests that corrected QT in black and Indian males and females is shorter than in the other ethnic groups and so some consideration should be given to making QTc dependent on ethnicity. A question which arises is why there are differences in ECG measurements between ethnic groups. Some QRS voltage differences might be explained by body mass index.
Fig. 3. The upper and lower limits of the ST amplitude in V2 based on linear regression in the four cohorts, both for males and females separately.
814
P.W. Macfarlane et al. / Journal of Electrocardiology 47 (2014) 809–814
For example, Chinese individuals have, on average, a lower body mass index than age and sex matched Caucasians. On the other hand, the increase in QRS voltage in Nigerian females with increasing age may be a cultural feature in that a more substantial index in older Nigerian ladies may be regarded as a sign of greater maturity and therefore desirable. Naturally, genetic factors which were not explored in this study could largely be responsible. This study shows that there are significant effects of race on ECG measures. The problem remains as to how these can be utilised in practice. The University of Glasgow 12 lead ECG analysis program [10] has incorporated a facility for the use of race almost since its inception. It can also make use of clinical diagnosis and drug therapy. However, experience shows that ECG technicians and nursing staff, who are increasingly recording ECGs with inadequate training, fail to record age, sex and particularly clinical details of a patient when obtaining an ECG. The use of race in diagnostic criteria will increase and hence it will become essential for staff to input such a variable to an ECG machine capable of making an automated ECG analysis. Manufacturers will also have to be alive to this situation and make race an input variable in the user interface. References [1] Simonson E. Differentiation between normal and abnormal in electrocardiography. St Louis, MI: Mosby; 1961. [2] Mason JW, Ramseth DJ, Chanter DO, Moon TE, Goodman DB, Mendzelevski B. Electrocardiographic reference ranges derived from 79,743 ambulatory subjects. J Electrocardiol 2007;40:228–34. [3] Wu J, Kors JA, Rijnbeek PR, van Herpen G, Lu Z, Xu C. Normal limits of the electrocardiogram in Chinese subjects. Int J Cardiol 2003;87:37–51. [4] Rautaharju PM, Zhou SH, Calhoun HP. Ethnic differences in ECG amplitudes in North American white, black and Hispanic men and women: effect of obesity and age. J Electrocardiol 1994;27(Suppl 1):20–31. [5] Jain A, Tandri H, Dalal D, Chahal D, Soliman EZ, Prineas RJ, et al. Diagnostic and prognostic utility of ECG for left ventricular hypertrophy defined by MRI in relationship to ethnicity: the Multi-Ethnic Study of Atherosclerosis (MESA). Am Heart J 2010;159:652–8.
[6] Chen CY, Chiang BN, Macfarlane PW. Normal limits of the electrocardiogram in a Chinese population. J Electrocardiol 1989;22:1–15. [7] Macfarlane PW, Lawrie TDV. Appendix 1. Adult normal limits. p2058. In: Macfarlane PW, van Oosterom A, Pahlm O, Kligfield P, Janse J, Camm J, editors. Comprehensive electrocardiology. 2nd ed. London: Springer-Verlag; 2011. [8] Katibi I, Clark EN, Devine B, Lloyd SM, Macfarlane P. Normal limits of the electrocardiogram in Nigerians. J Electrocardiol 2013;46:289–95. [9] Macfarlane PW, Lawrie TDV. The normal electrocardiogram and vectorcardiogram. p407. In: Macfarlane PW, Lawrie TDV, editors. Comprehensive electrocardiology, 1st ed., vol. 1. Oxford: Pergamon Press; 1989. [10] Macfarlane PW, Devine B, Latif S, McLaughlin S, Shoat DB, Watts MP. Methodology of ECG interpretation in the Glasgow program. Methods Inf Med 1990;29:354–61. [11] Macfarlane PW, Devine B, Clark E. The University of Glasgow (Uni-G) analysis program. Comput Cardiol 2005;32:451–4. [12] Hodges M, Salerno D, Erlien D. Bazett's QT correction reviewed. Evidence that a linear QT correction for heart is better. J Am Coll Cardiol 1983;1:694. [13] Bazett HC. An analysis of the time relations of electrocardiograms. Heart 1920;7:353–70. [14] Fridericia LS. Die systolendauer im electrokardiogramm bei normalenmenschen und bei herzkranken. Acta Med Scand 1920; 53:469–86. [15] Sagie A, Larson MG, Goldberg RJ, Bengtson JR, Levy D. An improved method for adjusting the QT interval for heart rate (the Framingham Study). Am J Cardiol 1992;70:797–801. [16] Luo S, Michler K, Johnston P, Macfarlane PW. A comparison of commonly used QT correction formulae: the effects of heart rate on the QTc of normal ECGs. J Electrocardiol 2004;37:81–90 [Suppl]. [17] Sokolow M, Lyon TP. The ventricular complex in left ventricular hypertrophy as obtained by unipolar precordial and limb leads. Am Heart J 1949;37:161–86. [18] Casale PN, Devereux RB, Alonso DR, Campo E, Kligfield P. Improved sex-specific criteria of left ventricular hypertrophy for clinical and computer interpretation of electrocardiograms: validation with autopsy findings. Circulation 1987;75:565–72. [19] Kligfield P, Badolini F, Rowlandsom I, Xue J, Park E, Derme B, et al. Comparison of automated measurements of electrocardiographic intervals and durations by computer-based algorithms of digital electrocardiographs. Am Heart J 2014;167, 150.e1–159.