ISSN 0326-646X





Sumario Vol. 42 - Nº 3 Julio - Septiembre 2013

Cardiovascular Risk and Arrhythmias Electrocardiographic Markers in Hypertensive Patients without
Coronary Artery Disease

Alberto Morales Salinas, Ebrey León Aliz, Raimundo Carmona Puerta,
Yisel Villanueva Ramos, Regla Poveda Rodríguez, Rafael López Machado, Carlos Vázquez Subit, Yaniel Castro Torres

Cardiocentro "Ernesto Che Guevara".
Colón 473 e/Estrada Palma y Misionero. (50100) Santa Clara, Villa Clara, Cuba.
E mail 1   E mail 2

Recibido el 29-ABR-2013 – ACEPTADO después de revisión el 25-MAYO-2013

The authors declare not having a conflict of interest.

Rev Fed Arg Cardiol. 2013; 42(3): 189-194

Print version Imprimir sólo la columna central




Los factores de riesgo (FR) cardiovascular se asocian con los marcadores electrocardiográficos predictivos de arritmias, sin embargo la relación entre estos marcadores y el riesgo cardiovascular global (RCVG) ha sido poco estudiada. El objetivo fue analizar la relación de los FR y el RCVG con marcadores electrocardiográficos, en hipertensos sin cardiopatía isquémica.
Métodos: Tipo de estudio: transversal. Se seleccionaron aleatoriamente 48 hipertensos de un consultorio médico de la provincia de Villa Clara, Cuba. Periodo: diciembre-2008 a marzo-2009. Se analizaron datos clínicos, de laboratorio y del electrocardiograma -dispersión de onda P (ΔP), intervalo QT, dispersión del QT (ΔQT), T pico-T final (Tp-e) y duración del QRS-; todos fueron corregidos por la frecuencia cardiaca. Se determinó el RCVG según el modelo cuanticualitativo de la Guía Europea de Hipertensión arterial. Criterio de exclusión: fibrilación auricular activa, bloqueo completo de rama izquierda, marcapasos, enfermedad pulmonar obstructiva crónica y/o asma bronquial.
Resultados: La media de edad fue de 59.4 años. El 52.1% de los hipertensos fueron mujeres. La prevalencia de tabaquismo, diabetes mellitus, hipercolesterolemia y sobrepeso-obesidad fue de 18.8%, 25%, 27.1% y 56.3% respectivamente. El 20.8%, 25%, 22.9%, 31.3% de los sujetos se clasificaron en los subgrupos de RCVG bajo, moderado, alto y muy alto en cada caso. El tabaquismo se asoció al ∆QTC, el QRSC y el Tp-e. El sobrepeso-obesidad tuvo relación con el QRSC y el Tp-e. La ΔP no se relacionó con ningún FR. El RCVG se asoció a la ΔP (p=0.013), el QT (p=0.010) y el Tp-Te (p=0.000).
Conclusiones: Hay relación entre el RCVG y varios marcadores electrocardiográficos. Puede haber marcadores con mayor asociación al RCVG, que a los FR.

Palabras clave: Marcadores electrocardiografícos. Factores de riesgo cardiovascular. Hipertensión arterial sistémica. Arritmias.

Cardiovascular risk factors are associated with electrocardiographic markers which are predictive of arrhythmias; however the relationship between these markers and the global cardiovascular risk has been little studied. We aimed to analyze the relationship between risk factors and global cardiovascular risk with electrocardiographic markers in hypertensive patients without ischemic heart disease.
Methods: A cross-sectional study was performed in 48 hypertensive patients who were randomly selected from a doctor’s office in the province of Villa Clara, Cuba, from December 2008 to March 2009. Clinical, laboratory and electrocardiogram data were analyzed, as well as P-wave dispersion (ΔP), QT interval, QT dispersion (ΔQT), T peak-T end (Tp-e) and QRS duration; all were corrected for heart rate. Global cardiovascular risk was determined by the quantitative-qualitative model of the European Guidelines on Hypertension. Exclusion criteria included active atrial fibrillation, left bundle branch block, pacemakers, chronic obstructive pulmonary disease and / or bronchial asthma.
Results: Mean age was 59.4 years and 52.1% of patients were women. The prevalence of smoking, diabetes mellitus, hypercholesterolemia, and overweight-obesity was 18.8%, 25%, 27.1% and 56.3% respectively. 20.8%, 25%, 22.9%, and 31.3% of subjects were classified into low, moderate, high and very high global cardiovascular risk subgroups in each case. Smoking was associated with QTc dispersion, QRSC and Tp-e. Overweight-obesity was related to QRSC and Tp-e. P dispersion was not associated with any risk factor. The global cardiovascular risk was associated with P dispersion (p = 0.013), QT (p = 0.010) and Tp-e (p = 0.000).
Conclusions: There is a relationship between global cardiovascular risk and several electrocardiographic markers. There may be markers with greater association with global cardiovascular risk than with risk factors.

Key words: Electrocardiographic markers. Cardiovascular risk factors. Systemic arterial hypertension. Arrhythmias.


Cardiovascular diseases (CVD) are the main cause of general and early mortality, as well as disability at world level. Within them, ischemic heart disease (IHD) is the one providing the greatest overall load to public health [1].

The estimation of the chance of suffering IHD by models or scores of overall cardiovascular risk (OCVR) is one of the basic strategies for cardiovascular prevention [2]. To predict OCVR, qualitative models can be used, such as Framingham [3,4,5] and SCORE [6] or quanti-qualitative such as those proposed in the HTN guidelines by the World Health Organization [7], the International Society of Hypertension [7] and the European Society of Hypertension [8].

Quantitative models [3-6] are characterized by using continuous variables and major risk factors (RF), being more accurate, with more evidence about them –even being validated in some populations [9]- and giving an absolute value to OCVR; while quanti-qualitative ones [7,8] are easier to use in medical practice, use more variables –generally dichotomous-, also including subclinical lesions of target organs and cardiovascular-renal co-morbidity (thus they are valid to stratify CVR in primary and secondary prevention) and last, they provide a range or category of OCVR instead of an absolute value.

Major RF have been related to several electrocardiographic markers of arrhythmias and sudden cardiac death (SCD) [10-13]; however, there are few data about the relation between OCVR and these markers. Other reasons encouraging the analysis of this relation are that arrhythmias and SCD frequently constitute the first manifestation of IHD [14]. As there are multiple interconnections in the genesis of arrhythmias and myocardial ischemia, that make the myocardium vulnerable to ischemia, so is arrhythmia [15,16]. In the USA, up to 90% of the cases of SCD, may be linked to IHD, although this association is lower in the Mediterranean area [15].

The only electrocardiographic marker that has been related with OCVR is left ventricular hypertrophy (LVH). Even LVH has been included between predictive variables of OCVR in some scores [3,8]. There is a lot of evidenceabout the relation of LVH with cardiovascular morbi-mortality, arrhythmias and SCD [15,16,17]. However, LVH more than an electrocardiographic marker, should be considered as an indicator of target organ impairment [8].

Recently, we showed in hypertensive patients without ischemic heart disease, that several electrocardiographic markers, such as P wave dispersions and corrected QT interval, as well as corrected QRS interval durations, corrected QT and T peak to T end were associated to age, average blood pressure, the time of evolution of HTN and LVH in electrocardiogram (ECG)[18]. This time, the goal was analyzing the relation of these electrocardiographic markers with some major RF (smoking, overweight-obesity, dyslipidemia and diabetes mellitus) and OCVR, in hypertensive patients without ischemic heart disease.



Type of study: analytic, cross-sectional.

Population and sample: The population was made up by all hypertensive patients (265) from a medical office of Villa Clara, Cuba.

A simple randomization sampling was made, and 60 hypertensive patients were selected (22.6% of the population). The patients selected were assessed in the office during the period from December 2008 to March 2009.

Inclusion criteria:A) Patients with at least one year of evolution of essential hypertension; B) consent to participate in the study, and C) with no personal history of IHD or another CVD.

Exclusion criteria: A) The presence of diseases or conditions that would not allow or would make it difficult to measure ECG markers such as atrial fibrillation (AF), complete left or right bundle branch block, and pacemaker rhythm; B) insufficient clinical and lab data or ECG in a poor state, preventing the required electrocardiographic measures. Finally, the sample was constituted by 48 patients, 23 males and 25 females.

Definition of variables:

  1. Corrected P wave dispersion (∆Pc). Difference between P of greater and lesser duration of the 12 leads, corrected by heart rate. Value in milliseconds.
  2. Corrected QRS duration (QRSc). Time of QRS interval in ECG. The largest of the 12 leads is corrected by heart rate. Value in milliseconds.
  3. Corrected QT interval (QTc): Time since the onset of Q (or R) wave until the end of the T wave. Conventionally, the largest between the D2 and V5 leads is taken, and corrected by heart rate. Value in milliseconds.
  4. Corrected QT interval dispersion (∆QTc): Difference between the QT interval of greatest and least duration of the 12 leads. Value in milliseconds.
  5. T peak to T end (Tp-e). Duration since the peak of the T wave until the end of it. Conventionally V5 is used. Value in milliseconds.
  6. Dyslipidemia. Presence of serum cholesterol 6.2 mmol/L and/or triglycerides >1.7 mmol/L.
  7. Overall cardiovascular risk (OCVR). Stratification in four categories: “additional low”, “additional moderate”, “additional high” and “additional very high” of risk at 10 years of suffering a deadly or non-deadly cardiovascular episode.

To estimate OCVR, the quanti-qualitative score by the European Guideline for Hypertension 2007 was used[8].

Procedures: The ECGs were digitized by optical scanner and the measurements were made using manual digital caliper with two observers, taking the average of the value obtained from both. The correction of the ECG parameters by heart rate by Bazzet’s formula (square root of RR interval) was taken into account to remove the influence of it on the parameter.

Statistical analysis: The SPSS 15.0 version for Windows was used. The Student’s t-test was applied for a sample and for independent samples, the test of ANOVA of a factor and linear regression was made estimating the regression coefficient. To estimate the statistical significance of these tests, significant was considered if p<0.05 and very significant if p<0.01.


In Table 1, the prevalence of smoking, hypercholesterolemia, overweight – obesity and diabetes mellitus was shown to be 18.8%, 27.1%, 56.3% and 25% respectively. 79.2% of hypertensive patients had a “moderate”, “high” or “very high” OCVR and only 20.8% presented a “low” OCVR.



Age (years, mean ± SD*)

59.4 ± 8.5

Women (n, %)

25 (52.1%)

Smoking –current or former smoker <1 year - (n, %)

9 (18.8%)

High Blood Pressure (BP** > 139/89 mmHg orhistory)

16 (33.3%)

Systolic blood pressure (mmHg, mean ± SD)

154.1 ± 24.1

Diastolic blood pressure (mmHg, mean ± SD)

87.3 ±  11.1

HDL ‡< 40 mg/dl in men /HDL ‡ <50 mg/dl in women


Total cholesterol >6.2 mmol/l (n, %)

13 (27.1%)

Total cholesterol (mmol/l)

5.6 ± 1.1

HDL cholesterol ‡ (mmol/l)

1.15 ±  0.26

Triglycerides (mmol/l)

2.05 ± 1.5

Diabetes (>6.9 mmol/l orhistory; [n, %])

12 (25.0%)

Glycaemiain a fasting state (mmol/l)

6.6 ± 3.6

Body mass index

30.9 ±  5.1

Overweight-obesity (body mass index > 25; [n, %])

27 (56.3%)

Overall cardiovascular risk***


  • Low additional risk

10 (20.8%)

  • Moderate additional risk

12 (25.0%)

  • High additional risk

11 (22.9%)

  • Very high additional risk

15 (31.3%)

*SD: Standard deviation; **BP: Blood pressure; †LDL: Low-density lipoproteins; ‡HDL: High-density lipoproteins; ***European Guidelines of Hypertension 2007.

Table 1: Cardiovascular risk profile of the hypertensive patients studied.


The behavior of the electrocardiographic markers studied according to the presence of risk factors is shown in Table 2, with smoking and mainly “overweight-obesity” being detected as related to one or more ECG variables.

Risk factor






x ± of


x ± of


x ± of


x ± of


x ± of




55 ± 16


63 ± 18


459 ± 31


112 ± 12


103 ± 13



59 ± 18

85 ± 29

469 ± 35

124 ± 27

121 ± 14



61 ± 20


78 ± 26


469 ± 34


129 ± 26


124 ± 12



54 ± 15

83 ± 31

463 ± 35

112 ± 21

110 ± 16



61 ± 13


84 ± 33


469 ± 38


127 ± 33


120 ± 15



56 ± 21

79 ± 25

465 ± 31

118 ± 18

116 ± 15



60 ± 12


84 ± 24


480 ± 38


135 ± 33


123 ± 16



58 ± 20

79 ± 30

462 ± 32

117 ± 21

116 ± 15

Table 2. Relation between electrocardiographic markers and risk factors.

Finally, in Figure 1, three of the five ECG markers analyzed are observed (ΔPc, QTc and Tp-e) to significantly relate to OCVR.

Figure 1. Relation between electrocardiographic markers and overall cardiovascular risk.


The prevalence of hypercholesterolemia, overweight-obesity and diabetes mellitus (Table 1) was superior to that reported in several of the main Cuban epidemiological studies [2]. These differences are influenced by our population not being the general population, but hypertensive patients, as well as the size of the sample.

To estimate OCVR, the quanti-qualitative score by the European Guideline of HTN[8] was used, since it is a simple method to stratify the cardiovascular risk of hypertensive patients in daily life.

It was remarkable that more than 50% of hypertensive patients were in the high and very high OCVR. This may have been influenced by the score used, which automatically classifies all hypertensive-diabetic patients as a subgroup in high risk, as well as this score including more variables than quantitative methods. For instance, the Framingham-Anderson [3] and Framingham-Wilson scores [4] do not include obesity and family history of premature CVD between its predictive variables.

In the previous study led by Carmona-Puerta, we showed the relation of these electrocardiographic markers (∆PC, QRSC, QTC, ∆QTC andTp-e) with age, the figures of mean blood pressure and the time of evolution of HTN, as well as these markers being more altered in hypertensive patients with LVH in ECG[18]. While in the current study we detected that the electrocardiographic markers can be linked both to risk factors, smoking and overweight-obesity, and OCVR. We did not find in literature papers analyzing the relation between these markers and OCVR. So we consider that this is the main contribution of our study.

About the association between these markers and RF, there are conflicting data [10-13,18], because of the scant comparability of the papers that analyze this issue. For instance, there is a great variability in the methodological designs used (different population and inclusion criteria, etc.), the number and type of ECG leads analyzed, as well as biases-variations existing in the measurements-interpretations of ECG and the classification of results [19].

Starting by these elements, we can indicate that other studies have also detected a relation between smoking and some of the ECG markers analyzed. For instance, Singh evaluated the effect of smoking in variables of depolarization and ventricular repolarization in 25 smokers, that he compared with 25 non-smokers of similar sex and gender, with the finding that smoking was associated to QT interval duration and dispersion [10]. Likewise, Mozos et al, detected smoking relating to Tpe prolongation [20]. However, Leotta et al, when analyzing young healthy individuals (n=170, age 22-25 y.o., 84 men), did not find a relation between smoking and QT [21]. In a representative sample of the general population of USA, which included 7795 participants in the Third National Survey of Health and Nutrition, QT prolongation was associated to alcohol ingestion, but not to other modifiable risk factors, such as the habit of smoking [22]. In spite of these contradictory results, it is necessary to warn that the Framingham study showed that smokers have 2 to 3 times a greater risk of SCD [23]. Moreover, smoking relates to recurrence and the long-term prognosis of SCD [24,25].

In regard to overweight-obesity, in young healthy Koreans (127 men and 149 women of 25 years of age), QTc related to obesity, contrary to what happened in our study [11]. Other works have also detected that in obese patients, QT interval, QTc interval durations and/or QT dispersion may be altered [12,20,26-28], as well as P wave dispersion [29]. P wave duration and dispersion should not be seen only as a predictor of persistent atrial fibrillation, but also of cardiovascular and all-cause mortality [30,31]. While in the Framingham study, obesity was also associated to the risk of SCD [23]. The individuals with morbid obesity have between 40 and 60 times a greater risk of SCD than individuals with a similar age in the general population [32]. A favorable message for cardiovascular prevention is that in obese people, the loss of body weight reduces P wave dispersion [33].

In our study, dyslipidemia was not related to the ECG markers analyzed, which coincides with other authors. From major RF, dyslipidemia is where less evidence we have about its link to these markers. However, it is important to warn that cholesterol is another of the variables associated to SCD [34]. While another good preventive message is that pharmacological treatment vs. dyslipidemia is capable of reducing SCD both in primary and secondary prevention [35].

In our work, we did not detect a relation between diabetes and ECG markers either. However, in diabetic patients there can be not just P wave and QT dispersion, but also this last ECG alteration may be predictive of microalbuminuria [13,36]. Likewise, Leotta et al, found a relation between QTc and DM (in women); however, not between QTc, obesity and smoking [21]. Even in the patients with metabolic syndrome, a greater P wave, QT and QTc dispersion was identified [37].

In spite of multiple studies having been designed to analyze the relation between RF and electrocardiographic markers, there are few data on the link between these markers with the prediction of OCVR. A finding that also stimulated the analysis of this relation in future studies, is that there could be markers – as in this case P wave dispersion (Figure 1) – that are associated to OCVR in spite of not being linked to RF separately (Table 2).

In what subgroup of patients it seems more appropriate to supplement the estimation of OCVR with the measurement of ECG markers for SCD? In the answer, we should take into account that the number of SCD linked to ischemic heart disease increases with age, while the cases of cardiomyopathies and primary electrical diseases of the heart are more common at earlier ages. However, we have to consider the paradox that the proportion of SCD decreases with age, due to the increase of co-morbidity and other causes of death. For example, the proportion of SCD in the subgroup of 50 to 59 years is close to 35%, while in the intervals of 60 to 69 years and more than 70 years it drops below 25%[38]. So, we suggest supplementing the estimation of OCVR with the measurement of electrocardiographic markers, mainly in the subgroup of 60 to 69 years.

Finally, the usefulness of adding more variables to the traditional ones used in OCVR scores in primary prevention, is a controversial topic[39]. However, before the patient, the ECG analysis of arrhythmic risk should supplement the estimation of ischemic risk obtained with the OCVR scores, mainly in patients with greater OCVR.

Limitations of the study: The size of the sample (however, because the measurements are bothersome, there are many works on the topic with similar sizes of samples), is not a cohort study and there may be non-quantified variables that influence on the relation between these ECG markers and overall cardiovascular risk.


In hypertensive patients without ischemic heart disease, there is a relation between the overall cardiovascular risk and the risk of arrhythmia. There may be ECG markers with a greater association with overall cardiovascular risk, than with the cardiovascular risk factors. We suggest supplementing the estimation of OCVR with the measurement of ECG markers of SCD, mainly in the subgroup of 60 to 69 years of age.



  1. Mendis S, Puska P, Norrving B. Global Atlas on Cardiovascular Disease Prevention and Control. WHO 2011. Disponible:
  2. Elosua R, Morales-Salinas A. Determinación del riesgo cardiovascular global. Caracterización, modelización y objetivos de la prevención cardiovascular según el contexto socio-geográfico. Rev Esp Cardiol 2011; 11 Supl (E): 2-12.
  3. Anderson KM, Wilson PWF, Odell PM, et al. An updated coronary risk profile: a statement for health professionals. Circulation 1991; 83: 356-62.
  4. Wilson PWF, D’Agostino RB, Levy D, et al. Prediction of coronary heart disease using risk factor categories. Circulation 1998; 97: 1837-47.
  5. Pencina MJ, D'Agostino RB Sr, Larson MG, et al. Predicting the 30-year risk of cardiovascular disease: the Framingham heart study. Circulation 2009; 119: 3078-84.
  6. Conroy RM, Pyorala K, Fitzgerald AP, et al et al. Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. Eur Heart J. 2003; 24: 987-1003.
  7. Guidelines Sub-Committee. 1999 World Health Organization/International Society of Hypertension Guidelines for the management of hypertension. J Hypertens 1999; 17: 151-83.
  8. Mancia G, De Backer G, Dominiczak A, et al., Management of Arterial Hypertension of the European Society of Hypertension; European Society of Cardiology. 2007 Guidelines for the Management of Arterial Hypertension: The Task Force for the Management of Arterial Hypertension of the European Society of Hypertension (ESH) and of the European Society of Cardiology (ESC). J Hypertens 2007; 25: 1105-87.
  9. Marrugat J, Subirana I, Comín E, et al. Validity of an adaptation of the Framingham cardiovascular risk function: the VERIFICA study. J Epidemiol Community Health 2007; 61: 4-47.
  10. Singh K. Effect of smoking on QT interval, QT dispersion and rate pressure product. Indian Heart J 2004; 56 (2): 140-2.
  11. Ahn SV, Kim HC, Hur NW, et al. Relationship between corrected QT interval and cardiovascular risk factors in young healthy adults: the Kangwha study. J Prev Med Public Health 2006; 39 (6): 455-61.
  12. Mshui ME, Saikawa T, Ito K, et al. QT interval and QT dispersion before and after diet therapy in patients with simple obesity. Proc Soc Exp Biol Med 1999; 220: 133-8.
  13. Yazici M, Ozdemir K, Altunkeser BB, et al. The Effect of Diabetes Mellitus on the P-Wave Dispersion. Circ J 2007; 71: 880-3
  14. Joep Perk J, De Backer G, Gohlke H, et al. Guía europea sobre prevención de la enfermedad cardiovascular en la práctica clínica (versión 2012). Rev Esp Cardiol 2012; 65: 869-73.
  15. Bayés de Luna A, Elosua R. Muerte súbita. Rev Esp Cardiol 2012; 65 (11): 1039-52.
  16. Gaztañaga L, Marchlinski FE, Betensky BP. Mecanismos de las arritmias cardiacas. Rev Esp Cardiol 2012; 65 (2): 174-85.
  17. Levy D, Salomon M, D’Agostino RB, et al. Prognostic implications of baseline electrocardiographic features and their serial changes in subjects with left ventricular hypertrophy. Circulation 1994; 90: 1786-93.
  18. Carmona Puerta R, León Aliz E, Morales Salinas A. Vulnerabilidad arrítmica incrementada y su relación con la hipertensión arterial en una población rural de Cuba. Relampa 2011; 24 (2): 96-105.
  19. De Bacquer D, De Backer G. Electrocardiographic findings and global coronary risk assessment.Eur. Heart J 2002; 23 (4): 268-70.
  20. Mozos I, Filimon L. QT and Tpeak-Tend intervals in shift workers. J Electrocardiol. 2013; 46 (1): 60-5.
  21. Leotta G, Maule S, Rabbia F, et al. Relationship between QT interval and cardiovascular risk factors in healthy young subjects. J Hum Hypertens 2005; 19(8): 623-7.
  22. Zhang Y, Post WS, Dalal D, Blasco-Colmenares E, et al. Coffee, alcohol, smoking, physical activity and QT interval duration: Results from the Third National Health and Nutrition Examination Survey. PLoS ONE 2011; 6 (2): e17584.
  23. Kannel WB, Thomas HE Jr. Sudden coronary death: the Framingham Study. Ann N Y Acad Sci 1982; 382: 3-21.
  24. Hallstrom AP, Cobb LA, Ray R. Smoking as a risk factor for recurrence of sudden cardiac arrest. N Engl J Med 1986; 314: 271-5.
  25. Cupples LA, Gagnon DR, Kannel WB. Long- and short-term risk of sudden coronary death. Circulation 1992; 85:11-8.
  26. Frank S, Colliver JA, Frank A. The electrocardiogram in obesity: statistical analysis of 1,029 patients. J Am Coll Cardiol 1986; 7: 295-9.
  27. Braschi A, Abrignani MG, Francavilla VC, et al. Novel electrocardiographic parameters of altered repolarization in uncomplicated overweight and obesity. Obesity (Silver Spring). 2011; 19 (4): 875-81.
  28.  Akintunde AA,  Oyedeji AT,  Familoni OB, et al QT Interval prolongation and dispersion: Epidemiology and clinical correlates in subjects with newly diagnosed systemic hypertension in Nigeria. J Cardiovasc Dis Res 2012; 3 (4): 290-5.
  29. Seyfeli E, Duru M, Kuvandık G, et al. Effect of obesity on P-wave dispersion and QT dispersion in women. Intern J Obes 2006; 30: 957-61.
  30. Koide Y, Yotsukura M, Ando H, et al. Usefulness of P-wave dispersion in standard twelve-lead electrocardiography to predict transition from paroxysmal to persistent atrial fibrillation. Am J Cardiol 2008; 102 (5): 573-7.
  31. Magnani JW, Gorodeski EZ, Johnson VM, et al. P wave duration is associated with cardiovascular and all-cause mortality outcomes: the National Health and Nutrition Examination Survey. Heart Rhythm 2011; 8 (1): 93-100.
  32. Kannel WB, Schatzkin A. Sudden death: lessons from subsets in population studies. J Am Coll Cardiol 1985; 5 Suppl 6: B141.
  33. Sjostrom LV. Mortality of severely obese subjects. Am J Clin Nutr 1992; 55: 516S-23S.
  34. Russo V, Ammendola E, De Crescenzo I, et al. Severe obesity and P-wave dispersion: the effect of surgically induced weight loss. Obes Surg 2008; 18 (1): 90-6.
  35. De Sutter J, Tavernier R, De Buyzere M, et al. Lipid lowering drugs and recurrences of life-threatening ventricular arrhythmias in high-risk patients. J Am Coll Cardiol 2000; 36: 766-72.
  36. Yeo CK, Hapizah MN, Khalid BA, et al. The comparison of QT dispersion and 24 hour ambulatory blood pressure monitoring amongst diabetic patients with and without microalbuminuria. Med J Malaysia 2004; 59 (2): 185-9.
  37. Hanci V, Yurtlu S, Aydin M, et al. Preoperative abnormal P and QTc dispersion intervals in patients with metabolic syndrome. Anesth Analg 2011; 112 (4): 824-7.
  38. Tung P, Albert CM. Causes and prevention of sudden cardiac death in the elderly. Nat Rev Cardiol 2013; 10: 135-42.
  39. Baena-Díez J, Ramos R, Marrugat J. Capacidad predictiva de las funciones de riesgo cardiovascular: limitaciones y oportunidades. Rev Esp Cardiol 2009; 9: (Supl) 4B-13B.


Publication: September 2013

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