Measurement of Autonomic Tone in Cardiac Implantable Electronic Devices
Larisa G. Tereshchenko
KEY POINTS
Autonomic tone is continuously monitored on modern cardiac implantable electronic devices (CIEDs).
Standard deviation of 5-minute median atrial-to-atrial intrinsic intervals (SDAAM) is a reliable measure of long-term heart rate variability (HRV), equivalent to standard deviation of 5-minute average normal-to-normal (NN) intervals (SDANN).
Continuously monitored HRV metrics are easily available in any patient implanted with implantable cardioverter-defibrillators (ICD) or cardiac resynchronization therapy (CRT) device. Use of monitored HRV metrics helps to manage heart failure (HF) and can improve clinical outcomes.
Two risk scores are available to predict 1-year all-cause mortality and 30-day hospitalization rate for CRT patients.
Novel metrics of autonomic tone monitoring are under development. Implementation of novel metrics of autonomic tone will expand the eligible population for autonomic tone monitoring and will improve the accuracy of prediction.
Implementation of autonomic tone monitoring in clinical practice is needed.
The autonomic nervous system mediates the dynamic response of heart rate (HR) to physiologic impulses via vagal and sympathetic nerves.1,2 HR oscillates cyclically. Respiration causes high frequency HR oscillations exclusively via parasympathetic/vagal nerve discharge.3,4 Day-night cycle causes circadian changes in HR, mediated by neurohormones.5 Exercise and emotions exert a strong effect on HR via the sympathetic nervous system. However, the unique “signature” of the sympathetic nervous
system effect on HR dynamics has not yet been found. Interpretation of low frequency (LF) HR oscillations is complex. Baroreflex and thermoregulation contribute to LF HR fluctuations.5,6 In general, characteristics of HR dynamics reflect autonomic modulation and carry prognostic value for clinically important outcomes.7,8,9,10 Moreover, a growing body of evidence suggests that continuous monitoring of autonomic tone reveals subclinical changes long before HF symptoms worsen, even on β-blockers.11,12 Therefore, benefit from implanted devices can be greatly enhanced if continuous monitoring of autonomic tone data can contribute to patient management.
system effect on HR dynamics has not yet been found. Interpretation of low frequency (LF) HR oscillations is complex. Baroreflex and thermoregulation contribute to LF HR fluctuations.5,6 In general, characteristics of HR dynamics reflect autonomic modulation and carry prognostic value for clinically important outcomes.7,8,9,10 Moreover, a growing body of evidence suggests that continuous monitoring of autonomic tone reveals subclinical changes long before HF symptoms worsen, even on β-blockers.11,12 Therefore, benefit from implanted devices can be greatly enhanced if continuous monitoring of autonomic tone data can contribute to patient management.
Remote monitoring of patients with cardiac implantable electronic devices (CIEDs) is associated with improved survival13 and reduction in health care utilization.14 Modern CIEDs are equipped with tools for continuous monitoring and longitudinal assessment of heart rate variability (HRV). In routine clinical settings, HRV data can be obtained by either direct interrogation of the device or by review of home monitoring/Internet-based system data. Monitoring of HRV in CRT devices can help to identify early CRT nonresponders and timely adjust HF medical management. In addition to the assessment of CRT effect on autonomic nervous system, HRV monitoring can assess the effectiveness of a used β-blockers dosage and help to titrate β-blockers.15 Constantly improving CIEDs’ capabilities to collect, store, and process electrical signals provides opportunities for a larger number of potentially useful biomarkers that can be monitored in future CIEDs. In the future, CIEDs can become artificial intelligence devices that may be able to predict and prevent all types of terminal arrhythmias: ventricular tachyarrhythmias, asystole, and pulseless electrical activity. Therefore, this chapter has two separate parts. The first part discusses currently available markers for continuous monitoring of autonomic tone , implemented in commercially available CIEDs. The second part discusses novel markers that were studied in the analysis of intracardiac device electrograms (EGMs) but are not yet routinely offered in commercially available CIEDs.
MONITORING AUTONOMIC TONE IN COMMERCIALLY AVAILABLE CRT AND ICD DEVICES
Table 13.1 summarizes currently available HRV measurements.
Monitoring Autonomic Tone in Cardiac Resynchronization Therapy Devices
In patients with New York Heart Association (NYHA) Class I to IV heart failure (HF), CRT improves cardiac function and reduces morbidity and mortality. In NYHA Class III to IV HF patients, CRT also improves quality of life. In addition to direct therapeutic effect, modern CRT devices monitor a large number of physiologic parameters, which can be used to fine-tune medical management (Table 13.1). Monitoring of HRV is readily available on most CRT devices; it is used for HF management.
Device-Based Automatic Monitoring of SDAAM/SDANN
Modern implantable cardioverter-defibrillators (ICD) and CRT devices of major device manufacturers are capable of automatic assessment of HRV. The standard deviation of 5 minutes median atrial-to-atrial intrinsic intervals (SDAAM) is calculated automatically. The device excludes from analysis atrial paced beats, atrial and ventricular premature complexes, and arrhythmic episodes.16 The percentage of time during the 24-hour collection period in which intrinsic atrial beats were eligible for analysis is reported, as well. In Boston Scientific devices, if there were less than 67% eligible intrinsic atrial intervals, SDAAM is not displayed for that period.16 In addition to a 24-hour period, average SDAAM is reported weekly. Device-measured SDAAM is an equivalent of Holter-measured standard deviation of 5-minute
average normal-to-normal (NN) intervals (SDANN). SDANN or SDAAM measures long-term HR fluctuations. SDAAM/SDANN is an optimal long-term HRV metric for automated analysis, because averaging several hundred atrial-to-atrial intrinsic intervals is less affected by inaccuracies in device diagnosis of atrial premature complexes and abnormal supraventricular rhythms and provides reasonably reliable HRV estimate. For automated analysis, SDANN is a more accurate long-term HRV measure than standard deviation of NN intervals (SDNN).2
average normal-to-normal (NN) intervals (SDANN). SDANN or SDAAM measures long-term HR fluctuations. SDAAM/SDANN is an optimal long-term HRV metric for automated analysis, because averaging several hundred atrial-to-atrial intrinsic intervals is less affected by inaccuracies in device diagnosis of atrial premature complexes and abnormal supraventricular rhythms and provides reasonably reliable HRV estimate. For automated analysis, SDANN is a more accurate long-term HRV measure than standard deviation of NN intervals (SDNN).2
TABLE 13.1 Currently Available Measures of Autonomic Tone in CRT and ICD Devices | |||||||||||||||||||||||||||
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Braunschweig et al17 monitored CRT-device-based SDANN in the InSyncIII prospective study participants and reported improvement of SDANN 2 weeks post-CRT implant, especially among the NYHA Class III HF patients. In the NYHA Class II and IV HF patients, SDANN improvement plateaued at 4 weeks and did not improve further. In Class III HF patients, improvement of SDANN continued until 3-month post-CRT implantation.
Adamson et al showed that decreased SDAAM (<50 ms when averaged over 4 weeks) was associated with a greater than 3-fold mortality.18 SDAAM decreased median 16 days before HF hospitalization and returned to baseline after treatment. Sensitivity for prediction of HF hospitalizations was 70%, while the rate of false-positives was high (2.4 per 1 patient-year of follow-up).
Fantoni et al16 showed that CRT-responders demonstrated a significant increase of SDANN 3 months after starting CRT. SDANN increase was consistent in patients with ischemic and nonischemic cardiomyopathy. Fantoni et al16 defined CRT nonresponders as patients in whom SDANN did not improve or even worsened 4 weeks after CRT implantation. SDANN-defined CRT nonresponders had a significantly higher rate of combined clinical outcome events (HF hospitalizations, appropriate ICD therapies, or cardiovascular mortality). SDANN-defined CRT nonresponders showed no significant improvement in exercise capacity and left ventricular (LV) function 1 year post CRT. SDANN was the earliest parameter, indicating functional improvement. Close monitoring of SDANN within the first months after CRT implantation can identify potential nonresponders very early, just 1 month post-CRT implant. Early recognition of CRT nonresponse provides a chance to tailor patient management19 and improve clinical outcomes. SDANN improvement post CRT is
consistent with a significant reduction of sympathetic neural activity after biventricular pacing in systolic HF,20 suggesting that the long-term HRV measure SDANN does reflect sympathetic tone.
consistent with a significant reduction of sympathetic neural activity after biventricular pacing in systolic HF,20 suggesting that the long-term HRV measure SDANN does reflect sympathetic tone.
The Heart Failure-Heart Rate Variability Registry (HF-HRV)21 prospectively evaluated the relationships between device-measured HRV parameters and clinical characteristics of HF patients.22,23 When adjusted by demographics, body mass index, and diastolic blood pressure, Cox regression analyses showed significant association of SDANN and HRV footprint percentage with all-cause mortality in CRT patients.22 Changes in SDANN and HRV footprint percentage significantly correlated with clinical improvement, measured by quality of life and activity level.23
Footprint is a graphical rendering of the likelihood of a particular beat-to-beat HR change occurring at each intrinsic sinus rate during a 24-hour period,24 implemented in Boston Scientific devices. The footprint area is the normalized size of the two-dimensional plot of RR interval variability versus HR; it is reported as a percentage of graph area used. HRV footprint provides a graphical representation of several variables, including frequency; day-to-day rate variability; and maximum, minimum, and mean HR. Molon et al25 used HRV footprint graph to derive two nonlinear HRV measures: HR complexity and HR entropy. Nonlinear indices represent more structured information than footprint area. HF patients with higher HR-related complexity, representing a less compromised autonomic function, had better clinical response.25 A significant increase in HR complexity was associated with clinical improvement 1 year post-CRT implantation.25 However, HR complexity and entropy strongly correlated with traditional time-domain HRV metrics (SDANN). Further study of HR complexity and entropy is needed, to determine whether their addition to traditional HRV metrics carries additional predictive value. Any additional computational feature in implantable devices utilizes device memory and can potentially shorten device battery life. Therefore, complementary predictive value and benefit of any new HRV parameter for continuous monitoring should be documented first.
Risk Scores Using Monitoring of Autonomic Tone for Prediction of HF Outcomes
It is well known that combination of several risk factors into a risk score can improve the accuracy of the prediction. Two up-to-date risk models incorporate data obtained by CRT devices during continuous monitoring of autonomic tone. One, developed and validated by Singh et al,21 predicts all-cause mortality in HF patients. The other, developed and validated by Cowie et al,26 predicts HF hospitalizations.
The risk model by Singh et al21 was developed in The Cardiac Resynchronization Therapy Registry Evaluating Patient Response with RENEWAL Family Devices (CRT RENEWAL) study and was validated in the HF-HRV study of CRT patients. SDANN, HRV footprint, mean 24-hour HR, and activity log data were collected and included in the predictive model. All-cause mortality served as the primary outcome. Monitored HRV parameters were dichotomized with higher risk of death assigned on the basis of device diagnostics obtained at the 2-week visit: SDANN less than 43 ms, mean HR greater than 74 beats/min, HRV footprint less than 29%, and activity level less than 5%. The developed risk model predicted 1-year all-cause mortality with moderate accuracy (C-statistic was 0.678 in the development cohort and 0.704 in the validation cohort). The risk model was well calibrated: mortality in the high-risk group was 13.8% in the development cohort and 13.6% in the validation cohort. The risk score model by Singh et al21 can be easily implemented in clinical practice.
The risk score by Cowie et al26 was developed in the combined patient population of OFISSER,27 Italian ClinicalService Project,28 and CONNECT29 studies. The validation cohort included PARTNERS-HF,30 FAST, PRECEDE-HF, and SENSE-HF31 studies population. Patient data were included if 90 days of device diagnostic data
were available. Adjudicated HF hospitalization event served as a primary outcome. Intrathoracic impedance, activity level, night HR, HRV, atrial fibrillation burden and characteristics, as well as ventricular arrhythmia burden and treatment were included in the model. A Bayesian Belief Network framework was used to combine the evidence from each diagnostic parameter and generate the risk score. In a high-risk group 2.3% of patients were hospitalized within the next 30 days in the development cohort and 6.8% in the validation cohort. In the validation cohort, the high-risk group was identified with 46% sensitivity (ie, 46% of the months with HF hospitalizations were preceded by a high-risk score) and 90% specificity (ie, 10% of the months with no HF hospitalizations were also preceded by a high-risk score). Patients who achieve a high-risk state on any day in the last 30 days were 10 times more likely to be hospitalized for HF in the next 30 days as compared to patients who had a low risk on each of the last 30 days. The advantage of this risk score is that it is time varying. Each patient can change risk category and transition from low to high risk or from high to low risk at any given time. Two risk scores are compared in Table 13.2. To simplify implementation in clinical practice, it would be more convenient if authors provided the Excel calculator on a website, or as a supplementary to the published manuscript Excel file. Unfortunately, neither of these two scores provided a user-friendly tool, slowing down the implementation of the risk stratification tool in clinical practice.
were available. Adjudicated HF hospitalization event served as a primary outcome. Intrathoracic impedance, activity level, night HR, HRV, atrial fibrillation burden and characteristics, as well as ventricular arrhythmia burden and treatment were included in the model. A Bayesian Belief Network framework was used to combine the evidence from each diagnostic parameter and generate the risk score. In a high-risk group 2.3% of patients were hospitalized within the next 30 days in the development cohort and 6.8% in the validation cohort. In the validation cohort, the high-risk group was identified with 46% sensitivity (ie, 46% of the months with HF hospitalizations were preceded by a high-risk score) and 90% specificity (ie, 10% of the months with no HF hospitalizations were also preceded by a high-risk score). Patients who achieve a high-risk state on any day in the last 30 days were 10 times more likely to be hospitalized for HF in the next 30 days as compared to patients who had a low risk on each of the last 30 days. The advantage of this risk score is that it is time varying. Each patient can change risk category and transition from low to high risk or from high to low risk at any given time. Two risk scores are compared in Table 13.2. To simplify implementation in clinical practice, it would be more convenient if authors provided the Excel calculator on a website, or as a supplementary to the published manuscript Excel file. Unfortunately, neither of these two scores provided a user-friendly tool, slowing down the implementation of the risk stratification tool in clinical practice.
TABLE 13.2 Comparison of Risk Scores Using CIED-Monitored Parameters | ||||||||||||||||||||||||||||||||||||||
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Monitoring Autonomic Tone in Implantable Cardioverter Defibrillator Devices
Similarly to HF patients with implanted CRT devices, HRV monitoring on ICD devices can be used for the improvement of HF management and prevention of HF
hospitalizations. However, another important goal of autonomic tone monitoring in ICD devices is prediction of imminent ventricular tachycardia events. A large number of novel therapeutic interventions that can potentially prevent ventricular tachycardia events are currently under development.32,33
hospitalizations. However, another important goal of autonomic tone monitoring in ICD devices is prediction of imminent ventricular tachycardia events. A large number of novel therapeutic interventions that can potentially prevent ventricular tachycardia events are currently under development.32,33
Decreased HRV is associated with increased risk of ventricular tachyarrhythmias and sudden cardiac death (SCD).34 As appropriate and inappropriate ICD shocks are associated with increased mortality,35,36 prediction and prevention of impending arrhythmic events in patients with implanted ICD become increasingly important.37,38 HRV is depressed in HF, including HF patients with implanted ICD.39 However, the role of long-term HRV parameters in the prediction of ventricular arrhythmia events in ICD patients is less clear. Ten Sande et al39 did not find a significant association between time-updated long-term HRV metric SDANN, automatically measured by the ICD device, with appropriate ICD therapies.
In contrast, analysis of circadian rhythm was prognostic. The Biotronik SUMMIT registry study in China (Study of Home Monitoring System Safety and Efficacy in Cardiac Implantable Electronic Device [CIED] implanted patients [SUMMIT]—ICD and cardiac resynchronization therapy-defibrillator) showed that the standard deviation of 30-day mean night-time heart rate (SD MNHR) was associated with appropriate ICD therapies and all-cause mortality.40 SD MNHR greater than or equal to 3.685 beats/min threshold provided the best accuracy for the prediction of ventricular tachyarrhythmias and all-cause mortality. Zhao et al40 also showed strong negative correlation (−0.7) between SD MNHR and SDANN. Zhao et al40 studied ICD patients in their first month post ICD implantation and included only patients with ventricular pacing percentage less than 10%. Further studies are needed.
Limitations of Device-Based Monitoring of Autonomic Tone
Several limitations of currently available tools for continuous monitoring of autonomic tone in commercially available CIEDs should be considered. Currently, not all patients with implanted CIEDs benefit from monitoring. SDANN measurements are not possible or meaningful in patients with persistent atrial fibrillation or if atrial pacing is required for greater than 80% of the 24-hour interval.18 A suggested method to increase the value of continuous monitoring of autonomic tone is to program CRT device as outlined in the Multicenter InSync Randomized Clinical Evaluation (MIRACLE) trial41 and lower the rate limit up to 40 beats/min, if possible. Similarly, primary prevention ICD programming with a lower rate limit of 40 or 50 beats/min can decrease the likelihood of pacing, maximize device clinical benefit, and increase the availability of continuous HRV monitoring diagnostic and prognostic data. The number of novel measures of autonomic tone are currently under investigation. Further improvement of continuous monitoring of autonomic tone will be able to improve the accuracy of prediction and enable timely prevention of sustained ventricular tachyarrhythmias and HF exacerbation events.
NOVEL APPROACHES TO MEASUREMENT OF AUTONOMIC TONE ON
INTRACARDIAC DEVICE ELECTROGRAMS A substantial number of potentially useful measures of autonomic tone have been developed and studied on intracardiac device electrograms (EGMs) but have not yet been implemented in commercially available CIEDs. Table 13.3 summarizes novel metrics of autonomic tone. It is important to emphasize that additional studies are needed before the following novel risk markers are implemented in clinical practice.
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