Article

Non-invasive Risk Stratification for Implantable Cardioverter-Defibrillator Placement-Heart Rate Variability

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Abstract

Heart rate variability (HRV) is a beat-to-beat variation in cardiac cycle length resulting from autonomic influence on the sinus node of patients in sinus rhythm. The importance of HRV as a risk stratifier has been well accepted, particularly in survivors of myocardial infarction. Large clinical trials are still needed to clarify the role of HRV in patients with non-ischemic cardiomyopathy. Given the significant association between HRV and the development of fatal arrhythmias/sudden cardiac death, HRV has been used in some clinical trials as one of the screening tests to select optimal candidates for implantable cardioverter–defibrillator placement, although its role in this area has not been fully established. Additional large prospective clinical trials are needed to further clarify the predictive value of existing or novel HRV parameters, on their own or in combination with other risk stratifiers, for assessing the risk of sudden cardiac death in a variety of clinical settings.

DOI:https://doi.org/10.15420/ahhj.2009.7.1.39

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Despite significant advances in medical and device therapy, sudden cardiac death (SCD) remains a major issue in public health, with an estimated annual rate of up to 450,000 in the US.1 Implantable cardioverter–defibrillator (ICD) therapy is effective for both primary2,3 and secondary prevention of SCD3 in patients with prior myocardial infarction (MI) and poor left ventricular (LV) function. In patients with non-ischemic cardiomyopathy, ICD therapy also significantly reduces all-cause mortality4 and potentially improves long-term prognosis in selected patients.5,6

However, if ICDs were implanted in all Multicenter Automatic Defibrillator Implantation Trial (MADIT)-II-eligible patients, the number needed to treat to save one life would be too high,7 and existing healthcare systems cannot afford increased ICD usage.

Given the invasive nature and possible complications6,8 of ICD implantation and the fact that not all patients experience recurrent malignant ventricular tachyarrhythmias after ICD implantation,9 it is imperative to identify those patients who are at the highest risk for life-threatening arrhythmias and who would benefit most from ICD therapy in addition to optimal medical treatment.

Risk Stratification for Implantable Cardioverter–Defibrillators

The most commonly used and well-defined tests for predicting major arrhythmic events include measurement of LV function, evaluation of autonomic modulation, and detection of arrhythmic markers.

LV function is usually assessed by measuring LV ejection fraction (LVEF) using echocardiography or ventriculography. Reduced LVEF has been commonly used as the first selection criterion for ICD implantation,2 but there has not been an easy algorithm derived from LVEF and other clinical characteristics that can predict those patients who will benefit most from ICD therapy.

Markers for arrhythmia substrate include, but are not limited to, the frequency of ventricular ectopic beats ≥10 per hour (VE10), non-sustained ventricular tachycardia (VT) on Holter monitoring electrocardiogram (ECG), and signal-averaged ECG (SAECG). These tests have been frequently applied to screen patients for further invasive testing before an ICD is implanted.10

Cardiac autonomic modulation may be measured quantitatively by assessing heart rate variability (HRV) and baroreflex sensitivity (BRS). The BRS test quantitatively evaluates the ability of the autonomic nervous system to react to various acute stimuli involving primarily vagal reflexes. Early studies have proved its usefulness in the evaluation of SCD risk.11–13 However, stimulation, usually via a pharmaceutical approach, is required to obtain specific ECG as well as blood pressure recordings for deriving BRS measurements. HRV appears to be more practical and promising in predicting arrhythmic risk and SCD, particularly in post-MI patients.14 Both BRS test and HRV analysis have been widely studied and well-defined over last two decades. More recently, heart rate turbulence (HRT)15 and the deceleration capacity of heart rate16 have been introduced to evaluate autonomic modulation.

Cardiac programmed electrical stimulation (PES) is often reserved for second-step risk stratification after non-invasive assessment.10,17 It has been widely demonstrated that PES is helpful in selecting a subgroup of MI survivors without spontaneous ventricular arrhythmias who benefit from prophylactic ICD implantation,10,17 while in patients with dilated cardiomyopathy (DCM) PES may not be so helpful.18

Despite encouraging achievements in risk stratification, it has not been fully established which set of risk stratification tests should be performed and how a decision should be made to select optimal candidates for ICD placement.19 Improved approaches are needed to identify patients who will benefit most from ICD therapy so that ICDs can be implanted in a cost-effective manner.20

Association Between Heart Rate Variability and Arrhythmic Events

HRV is a beat-to-beat variation in cardiac cycle length resulting from autonomic influence on the sinus node of patients in sinus rhythm. HRV is commonly measured by calculating time domain indices or performing spectral (frequency) analysis of an array of R-R intervals on short- or long-term ECG recordings.14 A significant association between depressed HRV and poor clinical outcomes has been confirmed by numerous studies in various clinical settings (see Table 1).

Heart Rate Variability in Acute Myocardial Infarction Survivors. In 1987, Kleiger et al. reported that reduced HRV was associated with increased mortality in patients after acute MI, and the relative risk (RR) of mortality was 5.3 times higher in the group with an HRV measure of standard deviation of all normal-to-normal intervals (SDNN) <50ms compared with those with an SDNN >100ms. HRV remained a significant predictor of long-term survival even after adjustment for clinical characteristics, other Holter features, and LVEF.21 This report drew great attention to the area of HRV analysis in risk stratification. Since then, numerous studies have been published and the association between HRV and prognosis in post-MI has been confirmed by the vast majority of clinical studies.22–27 A prospective study in 280 consecutive MI survivors confirmed that SDNN independently predicted patients at risk for future death or arrhythmic complications in the era of acute coronary revascularization of MI.26 Further clinical data from the Autonomic Tone and Reflexes After Myocardial Infarction (ATRAMI) study support that HRV stratifies post-MI patients for cardiac mortality. Patients with an SDNN <70ms carried a significant multivariate risk of cardiac mortality (3.2, 95% confidence interval [CI] 1.42–7.36).13 St George’s group reported that an HRV triangular index <20 units predicted arrhythmic mortality (RR 4.0, CI 1.4–11.3; p=0.008) independently in 334 survivors of acute MI during a follow-up of 41±20 months.28 HRV predicted cardiac death with a sensitivity of 40%, specificity of 86%, and positive predictive accuracy of 20% in 579 acute MI survivors over a follow-up of at least two years when the HRV triangular index was dichotomized at <17 units.29 In the fibrinolytic era, the results of the Italian Group for the Study of the Survival of Myocardial Infarction (GISSI)-2 demonstrated that the prognostic value of HRV remained significant in post-MI patients of all ages.30 There is a general consensus that depressed HRV is a strong predictor of mortality and arrhythmic complications that is independent of other recognized risk factors in MI survivors.

HRV evaluation using other approaches has proved its predictive value in post-MI patients as well.25,31,32 Katz et al. showed that one-minute HRV in deep breathing was predictive for cardiac death with an RR of 16.6 and a negative predictive value of 99.2% in 185 consecutive patients 5.1±2.5 days after a first MI.31 Such a simple short-term bedside test of HRV may be more practical from the clinical point of view; however, further studies in larger populations are needed to confirm its value in clinical settings.

Using a non-linear approach, Huikuri et al. found that reduced α1 (<0.75) independently predicted arrhythmic death (n=75) with an adjusted RR of 1.4 (95% CI 1.1–1.7; p<0.05) in a substudy of the DIAMOND trial that consisted of 446 survivors of acute MI with an LVEF ≤35%.32 HRT is also considered a measure of variability in heart rate. It represents a biphasic chronotropic response of the sinus node to a single ventricular premature beat, and quantitatively evaluates how heart rate returns to baseline rate after a premature ventricular beat. HRT has been shown to successfully predict mortality after MI,15,20,33 as has turbulence dynamics (a new measure of HRT).34 More recently, Bauer et al. demonstrated that impaired heart rate deceleration capacity was a powerful predictor of mortality after MI and could be more accurate than LVEF and conventional measures of HRV.16

Heart Rate Variability in Other Clinical Settings. Filipecki et al. investigated the value of 10 HRV parameters from 24-hour ambulatory ECGs to predict significant arrhythmic events in 56 patients with a history of VT and/or ventricular fibrillation (VF) of different etiologies but not due to acute MI. During a mean 24-month follow-up there were eight SCDs and 12 recurrences of malignant ventricular arrhythmias or ICD discharges. The subgroup with low LVEF and the mean of all five-minute standard deviation of R-R intervals <43ms had a significantly reduced survival rate (27 versus 83% at two years; p<0.01).35 Fauchier et al. evaluated the prognostic value of HRV in 116 patients (91 men, age 51±12 years, LVEF 34±12%) with idiopathic DCM by assessing HRV using time and frequency domain analysis on 24-hour ambulatory ECGs. Ten arrhythmic events (resuscitated VF or sustained VT) and seven SCDs were found during a mean follow-up of 53±39 months. This study demonstrated that reduced SDNN was an independent predictor of arrhythmic events and SCD.36 Their findings are supported by La Rovere’s study in 444 patients with DCM and chronic heart failure (CHF) showing that reduced low-frequency (LF) power was an independent predictor of sudden death.37 Makikallio reported significant prognostic power of non-linear HRV parameters for SCD in a general population.38 Reduced α1 (<1.0) predicted SCD (n=29) with an adjusted RR of 4.3 (95% CI 2.0–9.2; p<0.001) independent of other predictors in 325 subjects ≥65 years of age.38 Non-linear dynamic analysis is the latest tool to be shown to have an even higher predictive value than other traditional parameters in a number of studies. However, standardization of this new technique and more supporting evidence are still lacking.

Heart Rate Variability and Pending Life-threatening Arrhythmic Events. Recent advances in ICD technology provide opportunities to evaluate and compare the variation patterns of heart rate before the onset of arrhythmic events with those seen under control conditions by analyzing ICD-stored ECG recordings. Meyerfeldt et al. reported that the onset of slow VT (cycle length >270ms) was characterised by a significant increase in heart rate, while fast VT (cycle length ≤270ms) was triggered during decreased heart rates compared with control series.39 Baumert et al. found significant changes in the mean N-N interval and non-linear HRV parameters before the onset of VT.40 Pruvot et al. observed peculiar heart rate dynamics before the onset of ventricular tachyarrhythmias independent of antiarrhythmic drugs.41 Lombardi et al. investigated HRV patterns before VT and under control conditions on extracted ECGs from ICD memory in patients with ischemic heart disease or DCM and implanted ICDs for VF or VT. They performed time and frequency domain analysis as well as the power–law behavior of R-R interval time series at rest, at 15–30 minutes, and immediately before the onset of VT. They found significantly altered HRV patterns before VT onset, which suggested a shift of sympathovagal balance toward sympathetic predominance and reduced vagal tone.42 These findings support the value of HRV assessment in predicting fatal arrhythmic events.

Heart Rate Variability as a Risk Stratifier in Combination with Other Risk Factors. Although HRV is of significant value for risk stratification, its predictive accuracy remains low if used alone. Improvement of the predictive values was found when HRV was combined with late potential: the positive predictive accuracy was up to 33% with a sensitivity of 58% and an RR of 18.5 for SCDs or life-threatening ventricular arrhythmias.23 The results of the ATRAMI trial demonstrated convincingly that the combination of HRV and a BRS test provided significant prognostic value in patients post-MI. Two-year mortality was 17% when SDNN <70ms was associated with BRS <3.0ms/mmHg, in comparison with 2% (p<0.0001) when both HRV (SDNN >105ms) and BRS (BRS >6.1ms/mmHg) were well preserved.13 It was possible to stratify as many as 90% of post-MI patients into ‘high-risk’ (risk >30%) and ‘low-risk’ (risk <3%) groups when several non-invasive risk stratifiers were used in combination and the invasive PES was reserved for cases where the non-invasive tests were inconclusive.43

Evaluation of Heart Rate Variability for Implantable Cardioverter–Defibrillator Implantation

Schmitt et al. used HRV analysis as one of the non-invasive screening tests (LVEF, VE10, SDNN, and SAECG) to pre-select patients for PES study from a consecutive series of 1,436 patients with acute MI. In this prospective study, all tests were performed before hospital discharge. Patients with a pre-defined risk score ≥3 were considered at high risk. Of 248 high-risk patients (17.3%) scheduled for invasive study, PES was performed in 98 eligible and consented patients. Of 20 patients with abnormal PES results, a prophylactic ICD was implanted and survival benefit was observed.44 This study supports the use of HRV as a first-step risk stratifier to identify candidates for ICD placement. Similarly, in the multicentre, prospective, randomised BEta-blocker STrategy plus ICD trial (BEST), HRV was used as one of the pre-selection tests followed by PES to evaluate whether ICD implantation would be beneficial in 143 survivors of acute MI. Patients were included in the study if they had LVEF ≤35% and VE10, SDNN <70ms, or abnormal SAECG. However, they did not find such PES-guided ICD implantation beneficial during a mean follow-up of 540±378 days in this study population at an early stage (<1 month) after an acute MI. The actuarial overall mortality for the conventional strategy and PES-guided ICD strategy arms was 18 versus 14% after one year (p=0.3) and 29.5 versus 20% after two years (p= 0.2).45

In the Defibrillator in Acute Myocardial Infarction Trial (DINAMIT)—a randomized, open-label comparison of ICD therapy and no ICD therapy—patients were selected by measures of LVEF (≤0.35) and cardiac autonomic function (dichotomised at SDNN ≤70 or mean heart rate ≥80bpm on 24-hour Holter monitoring ECG). All-cause mortality was pre-defined as the primary outcome, while arrhythmic death was the secondary outcome. A total of 332 patients in the ICD group and 342 patients in the no-ICD group were studied at six to 40 days after an MI. Prophylactic ICD therapy was associated with a reduced rate of arrhythmic death, but did not reduce all-cause mortality.46 The differences in patient inclusion criteria, study time after MI, and technical specifications may all contribute to the discrepancy in findings between these studies.

Although the positive predictive accuracy of HRV alone is not high enough to identify individuals at high risk, it could be helpful to eliminate those at very low risk for death by evaluating HRV.47 Rashba et al. explored this possibility in 274 participants in DEFINITE, which evaluated the role of prophylactic ICD placement in patients with non-ischemic DCM. In this study population there were no deaths in patients with preserved HRV (SDNN >113ms), in contrast to a 10% mortality rate in patients with depressed HRV (SDNN <81ms) over three-year follow-up.47 Normal HRV reduced the proportion of high-risk patients from 20.4 to 9.2% and increased that of low-risk patients from 40.5 to 51.6% in those with low LVEF without optimal beta-blocker therapy (prior risk 20.3%).48

In idiopathic DCM there are findings that do not support the use of HRV (SDNN) for selection of patients for ICD implantation.49,50 No significant differences were found in HRV measurements between patients with and without appropriate ICD interventions for VT or VF during a mean follow up of 43±26 months in idiopathic DCM patients who had prophylactic ICD implantation for asymptomatic non-sustained VT on Holter and LVEF ≤0.30 (n=51) or unexplained syncope (n=19).49 Similarly, the results of the Marburg Cardiomyopathy Study (MACAS) failed to show HRV (SDNN) as a good predictor of major arrhythmic events (VT/VF/SCD) in 340 patients with non-ischemic cardiomyopathy during 52±21 months of follow-up.50

Summary

HRV has become one of the most popular tests to evaluate autonomic function and risk-stratify cardiac patients, particularly MI survivors. It independently predicts risk for SCD and total mortality in patients after MI30 with or without LV dysfunction.13,27,51 Observational studies also suggest the usefulness of HRV in patients with non-ischemic cardiomyopathy, but some controversies exist.50 There are many different ways to assess HRV, some of which—such as HRT—may be more productive than others.52 It has not been fully established whether using HRV as a screening tool may refine current risk assessment algorithms to select ICD implantation candidates for prevention of SCD. Although HRV has been used as one of the risk stratification tests to identify high-risk groups for ICD implantation in a few prospective trials, there are no large prospective trials with ICD intervention in which HRV was used as the main risk-stratification technique and showed remarkable survival benefit. Additional large prospective studies and trials are needed to further clarify the predictive value of existing or novel HRV parameters, by themselves or in combination with other risk factors, for assessing the risk for SCD in various clinical settings.

References

  1. Zipes DP, Camm AJ, Borggrefe M, et al., ACC/AHA/ESC 2006 Guidelines for Management of Patients With Ventricular Arrhythmias and the Prevention of Sudden Cardiac Death: a report of the American College of Cardiology/American Heart Association Task Force and the European Society of Cardiology Committee for Practice Guidelines (writing committee to develop Guidelines for Management of Patients With Ventricular Arrhythmias and the Prevention of Sudden Cardiac Death): developed in collaboration with the European Heart Rhythm Association and the Heart Rhythm Society, Circulation, 2006;114(10):e385–e484.
  2. Moss AJ, Zareba W, Hall WJ, et al., Prophylactic implantation of a defibrillator in patients with myocardial infarction and reduced ejection fraction, N Engl J Med, 2002;346(12):877–83.
  3. Ezekowitz JA, Armstrong PW, McAlister FA, Implantable cardioverter defibrillators in primary and secondary prevention: a systematic review of randomized, controlled trials, Ann Intern Med, 2003;138(6):445–52.
  4. Desai AS, Fang JC, Maisel WH, Baughman KL, Implantable defibrillators for the prevention of mortality in patients with nonischemic cardiomyopathy: a meta-analysis of randomized controlled trials, JAMA, 2004;292(23):2874–9.
  5. Wichter T, Paul M, Wollmann C, et al., Implantable cardioverter/ defibrillator therapy in arrhythmogenic right ventricular cardiomyopathy: single-center experience of long-term follow-up and complications in 60 patients, Circulation, 2004;109(12):1503–8.
  6. Corrado D, Leoni L, Link MS, et al., Implantable cardioverterdefibrillator therapy for prevention of sudden death in patients with arrhythmogenic right ventricular cardiomyopathy/dysplasia, Circulation, 2003;108(25):3084–91.
  7. Chan PS, Stein K, Chow T, et al., Cost-effectiveness of a microvolt T-wave alternans screening strategy for implantable cardioverterdefibrillator placement in the MADIT-II-eligible population, J Am Coll Cardiol, 2006;48(1):112–21.
  8. Gradaus R, Block M, Brachmann J, et al., Mortality, morbidity, and complications in 3344 patients with implantable cardioverter defibrillators: results from the German ICD Registry EURID, Pacing Clin Electrophysiol, 2003;26(7 Pt 1):1511–18.
  9. Gillis AM, Sheldon RS, Wyse DG, et al., Clinical and electrophysiologic predictors of ventricular tachyarrhythmia recurrence in patients with implantable cardioverter defibrillators, J Cardiovasc Electrophysiol, 2003;14(5):492–8.
  10. Schmitt C, Barthel P, Ndrepepa G, et al., Value of programmed ventricular stimulation for prophylactic internal cardioverterdefibrillator implantation in postinfarction patients preselected by noninvasive risk stratifiers, J Am Coll Cardiol, 2001;37(7):1901–7.
  11. Farrell TG, Paul V, Cripps TR, et al., Baroreflex sensitivity and electrophysiological correlates in patients after acute myocardial infarction, Circulation, 1991;83(3):945–52.
  12. Farrell TG, Odemuyiwa O, Bashir Y, et al., Prognostic value of baroreflex sensitivity testing after acute myocardial infarction, Br Heart J, 1992;67(2):129–37.
  13. La Rovere MT, Bigger JT Jr, Marcus FI, et al., Baroreflex sensitivity and heart-rate variability in prediction of total cardiac mortality after myocardial infarction. ATRAMI (Autonomic Tone and Reflexes After Myocardial Infarction) Investigators, Lancet, 1998;351(9101):478–84.
  14. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology, Heart rate variability: standards of measurement, physiological interpretation and clinical use, Circulation, 1996;93(5):1043–65.
  15. Schmidt G, Malik M, Barthel P, et al., Heart-rate turbulence after ventricular premature beats as a predictor of mortality after acute myocardial infarction, Lancet, 1999;353(9162):1390–96.
  16. Bauer A, Kantelhardt JW, Barthel P, et al., Deceleration capacity of heart rate as a predictor of mortality after myocardial infarction: cohort study, Lancet, 2006;367(9523):1674–81.
  17. Andresen D, Steinbeck G, Bruggemann T, et al., Risk stratification following myocardial infarction in the thrombolytic era: a two-step strategy using noninvasive and invasive methods, J Am Coll Cardiol, 1999;33(1):131–8.
  18. Rinaldi CA, Simon RD, Baszko A, et al., Can we predict which patients with implantable cardioverter defibrillators receive appropriate shock therapy? A study of 155 patients, Int J Cardiol, 2003;88(1):69–75.
  19. Freedberg NA, Hill JN, Fogel RI, Prystowsky EN, Recurrence of symptomatic ventricular arrhythmias in patients with implantable cardioverter defibrillator after the first device therapy: implications for antiarrhythmic therapy and driving restrictions. CARE Group, J Am Coll Cardiol, 2001;37(7):1910–15.
  20. Exner DV, Kavanagh KM, Slawnych MP, et al., Noninvasive risk assessment early after a myocardial infarction the REFINE study, J Am Coll Cardiol, 2007;50(24):2275–84.
  21. Kleiger RE, Miller JP, Bigger JT Jr, Moss AJ, Decreased heart rate variability and its association with increased mortality after acute myocardial infarction, Am J Cardiol, 1987;59(4):256–62.
  22. Malik M, Farrell T, Cripps T, Camm AJ, Heart rate variability in relation to prognosis after myocardial infarction: selection of optimal processing techniques, Eur Heart J, 1989;10(12):1060–74.
  23. Farrell TG, Bashir Y, Cripps T, et al., Risk stratification for arrhythmic events in postinfarction patients based on heart rate variability, ambulatory electrocardiographic variables and the signalaveraged electrocardiogram, J Am Coll Cardiol, 1991;18(3):687–97.
  24. Bigger JT, Fleiss JL, Rolnitzky LM, Steinman RC, The ability of several short-term measures of RR variability to predict mortality after myocardial infarction, Circulation, 1993;88(3):927–34.
  25. Bigger JT Jr, Steinman RC, Rolnitzky LM, et al., Power law behavior of RR-interval variability in healthy middle-aged persons, patients with recent acute myocardial infarction, and patients with heart transplants, Circulation, 1996;93(12):2142–51.
  26. Zabel M, Klingenheben T, Franz MR, Hohnloser SH, Assessment of QT dispersion for prediction of mortality or arrhythmic events after myocardial infarction: results of a prospective, long-term follow-up study, Circulation, 1998;97(25):2543–50.
  27. Malik M, Camm AJ, Janse MJ, et al., Depressed heart rate variability identifies postinfarction patients who might benefit from prophylactic treatment with amiodarone: a substudy of EMIAT (The European Myocardial Infarct Amiodarone Trial), J Am Coll Cardiol, 2000;35(5):1263–75.
  28. Batchvarov VN, Hnatkova K, Poloniecki J, et al., Prognostic value of heterogeneity of ventricular repolarization in survivors of acute myocardial infarction, Clin Cardiol, 2004;27(11):653–9.
  29. Copie X, Hnatkova K, Staunton A, et al., Predictive power of increased heart rate versus depressed left ventricular ejection fraction and heart rate variability for risk stratification after myocardial infarction. Results of a two-year follow-up study, J Am Coll Cardiol, 1996;27(2):270–76.
  30. Zuanetti G, Neilson JM, Latini R, et al., Prognostic significance of heart rate variability in post-myocardial infarction patients in the fibrinolytic era. The GISSI-2 results. Gruppo Italiano per lo Studio della Sopravvivenza nell' Infarto Miocardico, Circulation, 1996;94(3):432–6.
  31. Katz A, Liberty IF, Porath A, et al., A simple bedside test of 1-minute heart rate variability during deep breathing as a prognostic index after myocardial infarction, Am Heart J, 1999;138(1 Pt 1):32–8.
  32. Huikuri HV, Makikallio TH, Peng CK, et al., Fractal correlation properties of R-R interval dynamics and mortality in patients with depressed left ventricular function after an acute myocardial infarction, Circulation, 2000;101(1):47–53.
  33. Hallstrom AP, Stein PK, Schneider R, et al., Characteristics of heart beat intervals and prediction of death, Int J Cardiol, 2005;100(1): 37–45.
  34. Bauer A, Malik M, Barthel P, et al., Turbulence dynamics: an independent predictor of late mortality after acute myocardial infarction, Int J Cardiol, 2006;107(1):42–7.
  35. Filipecki A, Trusz-Gluza M, Szydlo K, Giec L, Value of heart rate variability parameters for prediction of serious arrhythmic events in patients with malignant ventricular arrhythmias, Pacing Clin Electrophysiol, 1996;19(11 Pt 2):1852–6.
  36. Fauchier L, Babuty D, Cosnay P, Fauchier JP, Prognostic value of heart rate variability for sudden death and major arrhythmic events in patients with idiopathic dilated cardiomyopathy, J Am Coll Cardiol, 1999;33(5):1203–7.
  37. La Rovere MT, Pinna GD, Maestri R, et al., Short-term heart rate variability strongly predicts sudden cardiac death in chronic heart failure patients, Circulation, 2003;107(4):565–70.
  38. Makikallio TH, Huikuri HV, Makikallio A, et al., Prediction of sudden cardiac death by fractal analysis of heart rate variability in elderly subjects, J Am Coll Cardiol, 2001;37(5):1395–1402.
  39. Meyerfeldt U, Wessel N, Schutt H, et al., Heart rate variability before the onset of ventricular tachycardia: differences between slow and fast arrhythmias, Int J Cardiol, 2002;84(2–3):141–51.
  40. Baumert M, Baier V, Haueisen J, et al., Forecasting of life threatening arrhythmias using the compression entropy of heart rate, Methods Inf Med, 2004;43(2):202–6.
  41. Pruvot E, Thonet G, Vesin JM, et al., Heart rate dynamics at the onset of ventricular tachyarrhythmias as retrieved from implantable cardioverter-defibrillators in patients with coronary artery disease, Circulation, 2000;101(20):2398–2404.
  42. Lombardi F, Porta A, Marzegalli M, et al., Heart rate variability patterns before ventricular tachycardia onset in patients with an implantable cardioverter defibrillator. Participating Investigators of ICD-HRV Italian Study Group, Am J Cardiol, 2000;86(9):959–63.
  43. Bailey JJ, Berson AS, Handelsman H, Hodges M, Utility of current risk stratification tests for predicting major arrhythmic events after myocardial infarction, J Am Coll Cardiol, 2001;38(7):1902–11.
  44. Schmitt C, Barthel P, Ndrepepa G, et al., Value of programmed ventricular stimulation for prophylactic internal cardioverterdefibrillator implantation in postinfarction patients preselected by noninvasive risk stratifiers, J Am Coll Cardiol, 2001;37(7):1901–7.
  45. Raviele A, Bongiorni MG, Brignole M, et al., Early EPS/ICD strategy in survivors of acute myocardial infarction with severe left ventricular dysfunction on optimal beta-blocker treatment. The BEta-blocker STrategy plus ICD trial, Europace, 2005;7(4):327–37.
  46. Hohnloser SH, Kuck KH, Dorian P, et al., Prophylactic use of an implantable cardioverter-defibrillator after acute myocardial infarction, N Engl J Med, 2004;351(24):2481–8.
  47. Rashba EJ, Estes NA, Wang P, et al., Preserved heart rate variability identifies low-risk patients with nonischemic dilated cardiomyopathy: results from the DEFINITE trial, Heart Rhythm, 2006;3(3):281–6.
  48. Bailey JJ, Hodges M, Church TR, Decision to implant a cardioverter defibrillator after myocardial infarction: the role of ejection fraction v. other risk factor markers, Med Decis Making, 2007;27(2):151–60.
  49. Grimm W, Herzum I, Muller HH, Christ M, Value of heart rate variability to predict ventricular arrhythmias in recipients of prophylactic defibrillators with idiopathic dilated cardiomyopathy, Pacing Clin Electrophysiol, 2003;26(1 Pt 2):411–15.
  50. Grimm W, Christ M, Bach J, et al., Noninvasive arrhythmia risk stratification in idiopathic dilated cardiomyopathy: results of the Marburg Cardiomyopathy Study, Circulation, 2003;108(23):2883–91.
  51. Camm AJ, Pratt CM, Schwartz PJ, et al., Mortality in patients after a recent myocardial infarction: a randomized, placebo-controlled trial of azimilide using heart rate variability for risk stratification, Circulation, 2004;109(8):990–96.
  52. Zipes DP, Camm AJ, Borggrefe M, et al., ACC/AHA/ESC 2006 guidelines for management of patients with ventricular arrhythmias and the prevention of sudden cardiac death: a report of the American College of Cardiology/American Heart Association Task Force and the European Society of Cardiology Committee for Practice Guidelines (Writing Committee to Develop Guidelines for Management of Patients With Ventricular Arrhythmias and the Prevention of Sudden Cardiac Death), J Am Coll Cardiol, 2006;48(5):e247–e346.
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