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European Heart Journal Advance Access originally published online on June 13, 2008
European Heart Journal 2008 29(15):1896-1902; doi:10.1093/eurheartj/ehn269
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Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2008. For permissions please email: journals.permissions@oxfordjournals.org

Chronotropic incompetence and mortality in middle-aged men with known or suspected coronary heart disease

Kai P. Savonen1,2,*, Vesa Kiviniemi3, Jari A. Laukkanen1,4,5, Timo A. Lakka1,6, Tuomas H. Rauramaa1, Jukka T. Salonen4,7 and Rainer Rauramaa1,2

1 Kuopio Research Institute of Exercise Medicine, Haapaniementie 16, 70100 Kuopio, Finland
2 Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland
3 Information Technology Center, University of Kuopio, Kuopio, Finland
4 School of Public Health and Clinical Nutrition, University of Kuopio, Kuopio, Finland
5 Lapland Central Hospital, Rovaniemi, Finland
6 Department of Physiology, Institute of Biomedicine, University of Kuopio, Kuopio, Finland
7 Oy Jurilab Ltd, Kuopio, Finland

Received 31 January 2008; revised 25 April 2008; accepted 29 May 2008; online publish-ahead-of-print 13 June 2008.

* Corresponding author. Tel: +358 17 2884422, Fax: +358 17 2884488, Email: savonen{at}hytti.uku.fi


   Abstract
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 Abstract
Introduction
 Methods
 Results
 Discussion
 Funding
 References
 
Aims: The objective is to study whether a heart rate (HR) increase from 40 to 100% of maximal work capacity in the exercise test (HR40-100) independently predicts mortality in men with known or suspected coronary heart disease (CHD).

Methods and results: The subjects were 294 men, 42–61 years of age, from eastern Finland with known or suspected CHD but without use of HR-lowering medication at baseline. HR was measured at rest and during a maximal, symptom-limited exercise test. During an average follow-up of 11.0 years, there were 61 all-cause deaths. In Cox-multivariable model, mortality increased by 41% (95% confidence interval, 12–79%) with a 1-SD (15 b.p.m.) decrement in HR40-100. HR increase from rest to 40% of maximal work capacity was not associated with an increased risk of death. Synergism was observed between HR40-100 and workload achieved at HR of 100 b.p.m. so that men having low values for both these HR variables had a particularly adverse prognosis compared with men with high values for these variables.

Conclusion: An attenuated HR increase particularly during the latter half of a maximal exercise test is an independent predictor of death in men with known or suspected CHD.

Key Words: Exercise testing • Heart rate • Mortality


   Introduction
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 Abstract
 Introduction
Methods
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 Discussion
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An attenuated heart rate (HR) response to exercise, or chronotropic incompetence (ChI), has been shown to predict adverse cardiac events among persons with known or suspected cardiovascular disease (CVD).1,2 It is not known, however, whether blunted HR rise at a delimited phase of the exercise test better predicts mortality than overall HR increase from rest to the highest HR during the exercise test (HRpeak). This delineation has a solid physiological basis because the HR rise at values below 100 b.p.m. is controlled by gradual withdrawal of parasympathetic nervous activity, whereas from 100 b.p.m. until maximum the HR rise is governed by continuously increasing sympathetic nervous activity.3 In line with this reasoning, we have found that in men without overt coronary heart disease (CHD) an HR increase from 40 to 100% of maximal work capacity in the exercise test (HR40-100) predicts mortality4 and acute myocardial infarction5 better than HRpeak or overall HR increase from rest to HRpeak (HR reserve). We investigated whether HR40-100 measured during the exercise test on a cycle ergometer independently predicts mortality in middle-aged men with known or suspected CHD and not using HR-lowering medication. Second, we compared the prognostic significance of HR40-100 with previously used ChI variables, as well as with a workload which a subject can attain at the HR of 100 b.p.m. during an exercise test (WL100), the variable previously shown to predict mortality in this patient group.6


   Methods
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 Abstract
 Introduction
 Methods
Results
 Discussion
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 References
 
Subjects
We studied participants in the Kuopio Ischaemic Heart Disease Risk Factor Study (KIHD), an ongoing population study designed to investigate risk factors for CVD and related outcomes. The study involves men from East Finland, an area known for its high prevalence and incidence of CVD. The study population constitutes a representative sample of men who lived in the town of Kuopio or neighbouring rural communities and who were 42, 48, 54, or 60 years of age at baseline between March 1984 and December 1989. Of 3235 eligible men, 2682 (83%) participated in the study. Complete data on exercise test variables were available for 2240 men. Of these men, 1946 men who had cancer at baseline (n = 36), used HR-lowering medication (n = 372) or had no prevalent CHD (n = 1719) were excluded. A prevalent CHD was defined as either a history of myocardial infarction, angina pectoris diagnosed by a doctor, angina pectoris on effort based on the London School of Hygiene Cardiovascular Questionnaire7 or the use of nitroglycerine for chest pain at least once a week. After these exclusions, the final study sample included 294 men with known or suspected CHD and not using HR-lowering medication. The study protocol was approved by the Research Ethics Committee of the University of Kuopio and complies with the Declaration of Helsinki. Each participant gave a written informed consent.

Assessment of ChI and other exercise test variables
A maximal, symptom-limited exercise test was performed at baseline using an electrically braked cycle ergometer as described previously.8 For 137 (47%) men examined before June 1986, the testing protocol is comprised of a 3 min warm-up at 50 Watts (W) followed by a step-by-step increase in the workload by 20 W/min. The remaining 157 (53%) men were tested with a ramp protocol which involved a linear increase in the workload by 20 W/min. HR was recorded from electrocardiogram (ECG) at rest, at the end of each 60 s interval during the exercise test, and at peak exercise. Resting HR was expressed as the lowest HR value, whether measured in lying position before the test or while sitting on bicycle at the initiation of the test. HR40-100 was calculated as HRpeak – HR at the workload of 40% of maximal workload.4,5 The other ChI variables were defined as presented in Table 1. WL100 was recorded directly at HR of 100 b.p.m. or interpolated linearly as a function of HR using resting HR and the nearest HR value above 100 b.p.m.6,9


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  		Table 1 The definition of variables quantifying chronotropic incompetence in the current study

 
Peak oxygen uptake (VO2peak) was defined as the highest value recorded over a 30 s interval. The existence of ischaemic changes in ECG during the exercise test, defined as ST depression of >1.0 mm at 80 ms after the J-point, was considered as an abnormal exercise test ECG.

Assessment of other risk factors
A history of arrhythmias, myocardial infarction, or cardiac insufficiency diagnosed by a doctor was assessed using a self-administered questionnaire. Diabetes was defined as a history of taking medication for diabetes or fasting blood glucose 6.1 mmol/L or over. The collection of blood specimens and the assessment of cigarette smoking, alcohol consumption, blood biochemistry, body mass index (BMI), and systolic (SBP) and diastolic blood pressure (DBP) at rest have been described elsewhere.8,10

Ascertainment of follow-up events
Deaths were ascertained by computer linkage to the national death registry using the Finnish social security number. There were no losses to follow-up. All deaths that occurred between study enrollment (from 20 March 1984 to 5 December 1989) and 31 December 1998 were included.

The average follow-up time to any death or the end of follow-up was 11.0 years (range 0.8–14.8 years). In the present study sample of 294 men with known or suspected CHD, a total of 61 (20.7%) deaths occurred during the follow-up period.

Statistical analysis
The skewed distribution of serum triglycerids was corrected via logarithmic transformation. Differences in baseline characteristics between halves above and below median of HR40-100 were tested with age-adjusted logistic and linear regression analyses, and Mann–Whitney U test. Serum LDL cholesterol and apolipoprotein B as well as SBP and DBP at rest correlated strongly with each other. Of these pairs, apolipoprotein B and SBP were included in the survival analyses because of their stronger association with the risk of death.

Cox proportional hazards’ regression models were used to study the associations of HR40-100 and other variables with the risk of death. The selection of variables for entrance into the multivariable model was based on the univariate analyses. We deliberately decided to restrict the number of variables in the final Cox model to six, according to the 10:1 events:degrees of freedom rule.11 We first chose five variables with highest Wald statistic from the univariate models together with HR40-100 into the multivariable model. We then optimized the prognostic value of the multivariable model by replacing the variable having the lowest Wald statistic in this model with the variable having the highest Wald statistic among the variables left outside the multivariable model. We repeated this procedure until the total likelihood ratio {chi}2 statistic for the multivariable model was not found to improve any more, and the model built was called the final model. The bootstrap-corrected relative risks (RRs) and their confidence intervals (CIs) were estimated as the median and the 0.025 and 0.975 percentiles of RRs observed in 1000 resamplings with replacement, respectively.12 To validate our final model, we also performed 1000 resamplings with forward selection (P = 0.10 for entry) in each separate set for all variables considered in univariate analyses. The aim of the analysis was to investigate whether variables included in our final model are those which are most frequently chosen in 1000 resamplings as well.13

To allow for the complex interplay between HR40-100 and other risk factors, propensity score was calculated for each subject by logistic regression analysis in which HR40-100 below or above 46 b.p.m. (median value) was the dependent variable and all risk factors in the univariate survival analyses were considered as independent variables.14 The propensity score calculated was then entered with HR40-100 into the Cox model.

In supplementary analyses, the possibly premature termination of the test was addressed and the predictive value of other ChI variables listed in Table 1 was investigated by replacing HR40-100 with each of them in turns in the final model. Finally, the independent predictive values of two HR variables, HR40-100 and WL100, were explored by entering WL100 into the final model. The interaction and synergistic effects between HR40-100 and WL100 were investigated as well. When exploring synergism, HR40-100 and WL100 were broken down at the half of each variable, and values at the higher and lower halves were called as high and low, respectively.

All results from Cox survival analyses presented in Results section and Tables are bootstrap-corrected values, except those in Table 3, and in Model A in Table 4. The proportional-hazards assumption was confirmed by the inspection of the plots of Schoenfeld residuals for covariates. Martingale residuals were used to assess the linearity assumption by plotting against the covariate to indicate the form of the relationship between log-hazard and function and the covariate. No violations were observed. In all analyses P < 0.05 was considered statistically significant. Statistical analyses were performed by using SPSS 11.5 for Windows (SPSS, Inc., Chicago, IL) and SAS, version 9.1 (SAS Institute, Inc., Cary, NC).


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  		Table 3 Risk factors for death in 294 men with known or suspected coronary heart disease at baseline and not using heart rate-lowering medication, univariate modelsa

 

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  		Table 4 Risk factors for death in 294 men with known or suspected coronary heart disease at baseline and not using heart-rate-lowering medication, multivariable modelsa

 

   Results
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 Abstract
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 Methods
 Results
Discussion
 Funding
 References
 
The median (range) age was 54 years (42–61 years). The mean (SD) HR40-100 was 45 (15) b.p.m. Baseline characteristics according to halves of HR40-100 are shown in Table 2.


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  		Table 2 Baseline characteristics in 294 men with known or suspected coronary heart disease at baseline and not using heart-rate-lowering medication

 
HR increase from 40 to 100% of maximal work capacity, other risk factors, and mortality
The six variables with the highest Wald statistics in univariate models were history of MI at baseline, HR40-100, cigarette smoking, age, VO2peak, and history of arrhythmias (Table 3). When building the final model, VO2peak was first replaced by alcohol consumption (Table 4, Models A and B). Further replacing of alcohol consumption by any other variable considered in univariate analyses was not found to improve the final model. In the final model (Table 4, Model B), the risk of death increased 41% for a 1-SD (15 b.p.m.) decrement in HR40-100. After 1000 bootstrapping resamplings with forward selection in each separate set, the five strongest variables in our final model (age, HR40-100, cigarette smoking, history of MI at baseline, and history of arrhythmias) were each found to be included in over 51% of 1000 models constructed (Table 4, Model C). This suggests that the variables selected to our final are those which stand out from the rest of the candidate predictors irrespective of selection method used. HR increase from rest to 40% of maximal work capacity did not predict death when it replaced HR40-100 in the final model: the risk of death increased 1.1-fold (95% CI 0.8–1.3) for each 1-SD (12 b.p.m.) decrement.

HR increase from 40 to 100% of maximal work capacity and mortality, propensity analysis
The receiver-operating characteristics (ROCs) curve was computed to quantify the performance of propensity score to correctly classify subjects into those with HR40-100 above or below the median. The area under ROC curve was 0.81, indicating strong discrimination. HR40-100 <46 b.p.m. (i.e. below median) was associated with a 2.9-fold (95% CI 2.0–5.0) higher risk of death compared with HR40-100 ≥46 b.p.m. After entering propensity score into the model, the risk decreased to 2.0-fold but the association was still statistically significant (Table 4, Model D).

The effect of dyspnoea or ischaemic changes in ECG
It might be argued that ChI solely reflects the premature termination of the test because of symptoms or findings attributed to a worse prognosis because of more severe underlying disease. Of reasons for terminating the test in the current study sample, the combination of either dyspnoea or ischaemic changes in ECG was the most predictive for death. When the combination of either dyspnoea or ischaemic changes in ECG was entered into the final model, however, the prognostic value of HR40-100 remained the same (Table 4, Model E).

The predictive value of other ChI variables and workload at HR of 100 b.p.m.
The predictive value of other ChI variables listed in Table 1 was explored by entering them in turns to the final model to substitute for HR40-100 (Table 5). The strongest predictors were HR reserve and HR impairment but the association was statistically significant only for HR reserve.


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  		Table 5 Variables quantifying chronotropic incompetence and the risk of deatha

 
The prognostic value of HR40-100 weakened slightly when WL100 was entered into the final model while WL100 was a strong predictor of death as well (Table 4, Model F). No interaction was observed between HR40-100 and WL100, but a synergistic effect was found. In the final model, the risk of death was not increased in 107 men who had either a low HR40-100 or a low WL100 (RR 1.1, 95% CI 0.6–2.3) but the risk was markedly increased in 93 men who had both a low HR40-100 and a low WL100 (RR 2.5, 1.4–5.4) when compared with 94 men having both a high HR40-100 and a high WL100.


   Discussion
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 Abstract
 Introduction
 Methods
 Results
 Discussion
Funding
 References
 
The main finding of the present study is that a low HR40-100 was an independent predictor of death in a population-based sample of men with known or suspected CHD at baseline. The prognostic value of HR40-100 was larger than that of other previously established ChI variables.

The present findings are in line with the results of previous studies in which ChI has predicted premature death in patients with known or suspected CHD.1,2 The observations in the present study add to current knowledge; in that a new ChI variable, HR40-100, was a stronger predictor of death than previously established variables quantifying ChI. We have previously shown the predictive superiority of HR40-100 over other ChI variables in subjects without overt CHD,4,5 but the present study confirmed these findings in the separate group of subjects with known or suspected CHD as well.

So far the predictive value of HR40-100 has not been confirmed in studies other than KIHD. Nonetheless, in the recent study a blunted HR increase from rest to 33% of maximal work capacity in the exercise test was not as strong predictor of death as a low HR reserve in patients referred for exercise testing.15 This finding suggests that a blunted HR increase from 33 to 100% of maximal work capacity (which by definition is close to HR40-100) might have been even stronger predictor than a low HR reserve if it had been computed in that study. When our results and those from the previous study15 are considered together, it can be deduced that a blunted HR increase particularly at the latter half of the exercise test, even more than an overall attenuated HR increase from rest to HRpeak, is a prognostically adverse finding. The predictive value of the early HR increase in the test is obscure, however, because in our study a blunted HR increase from rest to 40% of maximal work capacity was not associated with an increased mortality unlike in the previous study.15

The specific mechanisms leading to an increased incidence of mortality in patients with ChI have not been elucidated, although several underlying physiological disorders have been suggested.16–19 Numerous proposed mechanisms may imply that the prognostic value of ChI is based on several overlapping mechanisms rather than a solitary one. Furthermore, it is quite possible that ChI is not itself a cause of increased risk, but it is a marker of several risk factors accumulated in a given person with increased risk. Further studies are certainly needed to clarify the issue. In the current study, the association between a low HR40-100 and increased mortality weakened after adjustment for propensity score but a low HR40-100 was still a strong predictor of death. Therefore, a large proportion of the predictive value of a low HR40-100 was still left which could not be explained by its association with several risk factors.

The underlying severe CHD may cause the blunted HR response to exercise (i.e. ChI) by interrupting the test prematurely because of symptoms or findings related to underlying severe disease. In our study, the combination of either dyspnoea or ischaemic changes in ECG as a reason for terminating the test was slightly more prevalent among men with a low HR40-100, but the prognostic value of HR40-100 was unchanged when this combination was entered to the Cox model. Actually, the patients with ChI and exercise test performance limited by angina have been shown to have a better prognosis assessed by radionuclide imaging than patients with ChI but not limited by angina.20 These findings together suggest that the adverse prognosis in patients with ChI is not simply explained by the premature termination of the test before their actual maximal HR is achieved. An interesting finding in the present study was that both HR40-100 and WL100 were independent predictors of death when entered concurrently into the Cox model and synergistic effect was observed between them. This underlines the fact that the prognostic value of the exercise testing is optimized when various parameters measured at different time points during the test are taken into consideration when the risk assessment is done.21

The strength of the present study is that the participation rate was high and there were no losses to follow-up. Second, we have reliable data on mortality, because deaths were ascertained by the National Death Registry using a social security number. Finally, the comprehensive assessment of health habits and cardiovascular risk factors allowed us to investigate the independent association of HR40-100 with mortality.

The subjects of the current study were from population-based sample, whereas most of the previous studies exploring the predictive value of ChI have involved patients referred for testing in clinical setting. The inclusion criterion in the present study was based on participants’ self-reported history of MI or angina pectoris, or using of anti-anginal medication. The self-reporting unavoidably involves inaccuracy and a risk of misclassification. In the current study, however, the history of myocardial infarction or angina pectoris was based on a diagnosis made by a physician. Furthermore, the London School of Hygiene Cardiovascular Questionnaire7 is widely used and well validated tool for an unbiased assessment of chest pain in epidemiological studies. In our opinion the subjects included in the present study represent the wide spectrum of patients with findings or symptoms related to CHD, and consequently the results can be possibly generalized to even larger group of patients than the results from studies using more stringent inclusion criteria.

One limitation of the present study is that only men were enrolled. The extent to which age, gender, underlying diseases, and regular physical activity influence HR40-100, or modify its association with mortality, deserves further studies. Subjects using HR-lowering medication were excluded from the current study as in most of the previous studies. We acknowledge that beta-blockers are an essential part of the pharmacotherapy for diagnosed CHD, but the independent prognostic value of ChI is very difficult to assess under the influence of HR-lowering medication. A recent study, however, showed that ChI is associated with increased mortality even in patients using metoprolol or atenolol.22 Additionally, in two studies ChI predicted an adverse outcome in clinical cohorts that involved also subjects using ß-blocking medication while ß-blocker use was included as a covariate in survival analyses.15,23 If ChI is used for prognostic assessment in patients using HR-lowering medication, the cut-off points for determination of abnormal result should be different to those used with patients without HR-lowering medication.22 It is impossible to know whether HR40-100 changed during the long follow-up period, and how the possible changes could have affected our results. Finally, men who underwent coronary artery bypass surgery or percutaneous transluminal coronary angioplasty during the follow-up were not censored at the time of intervention because we did not have a possibility to monitor those events during the long follow-up.

Conflict of interest: none declared.


   Funding
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Funding
References
 
The Ministry of Education in Finland (74/722/2003); the Maire Taponen Foundation; the Mehiläinen Research Foundation; the Paulo Foundation; the University of Kuopio; the Finnish Medical Foundation; and Orion-Farmos Research Foundation.


   References
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 Abstract
 Introduction
 Methods
 Results
 Discussion
 Funding
 References
 

  1. Lauer MS. Exercise electrocardiogram testing and prognosis. Novel markers and predictive instruments. Cardiol Clin (2001) 19:401–414.[CrossRef][Medline]
  2. Kligfield P, Lauer MS. Exercise electrocardiogram testing: beyond the ST segment. Circulation (2006) 114:2070–2082.[Free Full Text]
  3. Lauer MS. Heart rate response in stress testing: clinical implications. ACC Curr J Rev (2001) 10:16–19.
  4. Savonen KP, Lakka TA, Laukkanen JA, Halonen PM, Rauramaa TH, Salonen JT, Rauramaa R. Heart rate response during exercise test and cardiovascular mortality in middle-aged men. Eur Heart J (2006) 27:582–588.[Abstract/Free Full Text]
  5. Savonen KP, Lakka TA, Laukkanen JA, Rauramaa TH, Salonen JT, Rauramaa R. Usefulness of chronotropic incompetence in response to exercise as a predictor of myocardial infarction in middle-aged men without cardiovascular disease. Am J Cardiol (2008) 101:992–998.[CrossRef][Web of Science][Medline]
  6. Savonen KP, Lakka TA, Laukkanen JA, Rauramaa TH, Salonen JT, Rauramaa R. Workload at the heart rate of 100 beats/min and mortality in middle-aged men with known or suspected coronary heart disease. Heart (2008) 94:e14.[Abstract/Free Full Text]
  7. Rose GA, Blackburn H, Gillum RF, Prineas RJ. Cardiovascular Survey Methods World Health Organization Monograph, Series No. 56 (1982) Geneva: World Health Organization.
  8. Lakka TA, Venalainen JM, Rauramaa R, Salonen R, Tuomilehto J, Salonen JT. Relation of leisure-time physical activity and cardiorespiratory fitness to the risk of acute myocardial infarction. N Engl J Med (1994) 330:1549–1554.[Abstract/Free Full Text]
  9. Savonen KP, Lakka TA, Laukkanen JA, Rauramaa TH, Salonen JT, Rauramaa R. Effectiveness of workload at the heart rate of 100 beats/min in predicting cardiovascular mortality in men aged 42, 48, 54, or 60 years at baseline. Am J Cardiol (2007) 100:563–568.[CrossRef][Web of Science][Medline]
  10. Salonen JT, Salonen R, Seppanen K, Rauramaa R, Tuomilehto J. HDL HDL2 HDL3 subfractions, the risk of acute myocardial infarction. A prospective population study in eastern Finnish men. Circulation (1991) 84:129–139.[Abstract/Free Full Text]
  11. Harrell FE Jr, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med (1996) 15:361–387.[CrossRef][Web of Science][Medline]
  12. Harrell FE Jr. Regression Modeling Strategies with Applications to Linear Models, Logistic Regression, and Survival Analysis (2001) New York, NY: Springer-Verlag. p88.
  13. Blackstone EH. Breaking down barriers: helpful breakthrough statistical methods you need to understand better. J Thorac Cardiovasc Surg (2001) 122:430–439.[Free Full Text]
  14. Blackstone EH. Comparing apples and oranges. J Thorac Cardiovasc Surg (2002) 123:8–15.[Free Full Text]
  15. Leeper NJ, Dewey FE, Ashley EA, Sandri M, Tan SY, Hadley D, Myers J, Froelicher V. Prognostic value of heart rate increase at onset of exercise testing. Circulation (2007) 115:468–474.
  16. Ellestad MH. Chronotropic incompetence. The implications of heart rate response to exercise (compensatory parasympathetic hyperactivity?). Circulation (1996) 93:1485–1487.[Free Full Text]
  17. Chaitman BR. Abnormal heart rate responses to exercise predict increased long-term mortality regardless of coronary disease extent: the question is why? J Am Coll Cardiol (2003) 42:839–841.[Free Full Text]
  18. De Sutter J, Van de Veire N, Elegeert I. Chronotropic incompetence: are the carotid arteries to blame? Eur Heart J (2006) 27:897–898.[Free Full Text]
  19. Routledge HC, Townend JN. Why does the heart rate response to exercise predict adverse cardiac events? Heart (2006) 92:577–578.[Abstract/Free Full Text]
  20. Hammond HK, Kelly TL, Froelicher V. Radionuclide imaging correlatives of heart rate impairment during maximal exercise testing. J Am Coll Cardiol (1983) 2:826–833.[Abstract]
  21. Froelicher VF, Myers J. Exercise and the Heart (2006) 5th ed. Philadelphia, PA: Elsevier. 270–271.
  22. Khan MN, Pothier CE, Lauer MS. Chronotropic incompetence as a predictor of death among patients with normal electrograms taking beta blockers (metoprolol or atenolol). Am J Cardiol (2005) 96:1328–1333.[CrossRef][Web of Science][Medline]
  23. Myers J, Tan SY, Abella J, Aleti V, Froelicher VF. Comparison of the chronotropic response to exercise and heart rate recovery in predicting cardiovascular mortality. Eur J Cardiovasc Prev Rehabil (2007) 14:215–221.[CrossRef][Web of Science][Medline]
    The above article uses a new reference style being piloted by the EHJ that shall soon be used for all articles.

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