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Europace Advance Access originally published online on May 3, 2007
Europace 2007 9(8):687-693; doi:10.1093/europace/eum066
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© The European Society of Cardiology 2007. All rights reserved. For Permissions, please e-mail: journals.permissions@oxfordjournals.org

ICD AND MONITORING

Physiological approach to monitor patients in congestive heart failure: application of a new implantable device-based system to monitor daily life activity and ventilation

Eric Page1,*, Serge Cazeau2, Philippe Ritter2, Daniel Galley3 and Cyrille Casset4

1 UCPX, Laboratoire de physiopathologie de l'exercice, Grenoble, France; 2 InParys, Saint-Cloud, France; 3 Centre Hospitalier Général, Albi, France; 4 ELA Medical, Sorin group, Le Plessis-Robinson, France

Manuscript submitted 9 February 2007. Accepted after revision 19 March 2007.

* Corresponding author. Tel: +33 476 29 1773; Fax: +33 476 33 8469. E-mail address: ericpagecardio{at}wanadoo.fr


   Abstract
Top
 Abstract
Introduction
 Methods
 Results
 Discussion
 Conclusions
 Acknowledgements
 References
 
Aims We examine an expert system designed to permanently monitor patients with congestive heart failure (CHF) using data of a dual-sensor pacemaker and to allow warning of significant changes in physiological indices.

Methods and results This study included 67 implanted patients divided into two groups: a control group without history of CHF (n = 19) who had received DDDR pacemakers (DDD group) and a test group (n = 48) who had received cardiac resynchronization therapy systems (CRT group) for severe CHF (NYHA III or IV, LVEF <40%). The embedded monitoring system measures minute ventilation (MV) and activity (ACT) at rest and at exercise. All devices collect data, and all adverse medical events were recorded. Data are stored daily for up to 3 months. The mean ACT was similar for both groups. Mean rest and exercise MV were significantly higher in CRT group. On 195 periods of 1-month follow-up in the CRT group, 31 events were suspected, 22 were true positive, 9 were false-positive, and 3 clinical adverse events were not predicted (sensitivity: 88%, specificity: 94.7%, positive predictive value: 71%, negative predictive value: 98.2%)

Conclusion A new diagnostic expert system that holds promise for the long-term ambulatory monitoring of CHF was developed.

Key Words: Heart failure, Cardiac pacing, Exercise


   Introduction
Top
 Abstract
 Introduction
Methods
 Results
 Discussion
 Conclusions
 Acknowledgements
 References
 
Rate responsive cardiac pacing based on physical activity is programmable in the majority of currently available pacemakers. Among several systems developed in the last 20 years,1Go,2Go most manufacturers have preferentially built systems that incorporate two sensors, one related to activity (ACT) by means of an accelerometer, and the other related to minute ventilation (MV), by monitoring of respiratory rate and amplitude.3Go,4Go The combined data acquired by both sensors provide a rate responsiveness that is adapted to the metabolic demand and very similar to the physiological response of an age-matched healthy individual.5Go,6Go This rate responsiveness considerably increases the functional capacity of patients who suffer from chronotropic incompetence during effort,7Go,8Go whether it is associated with left ventricular dysfunction. The data storage capacity of devices allows the retrieval of information related to the response time of the sensors at rest and during exercise.9Go

More recently, these sensors are incorporated in cardiac resynchronization therapy (CRT) system in order to improve the haemodynamic function of patients in congestive heart failure (CHF).10Go–12Go

With this objective in mind, we have designed and tested a monitoring system to be embedded in upcoming DDD pacemakers and CRT systems, based on information provided by the combined assessments of MV and ACT with a view to (i) continuously monitor the activity of these pacemakers, (ii) build and test an expert system capable of warning the primary health-care provider of significant changes in physiological indices, and (iii) anticipate the development of acute cardiac decompensation.13Go

The parameters ultimately entered into the expert system included (i) mean daily resting and activity MV, expressed in litre per minute equivalent and (ii) mean daily workload, expressed in g per day equivalent. We hypothesized that (i) a stable MV at rest and at exercise combined with a stable activity level reflects a stable clinical status, (ii) an increase in MV, particularly at rest, combined with a decrease in activity level announces a deteriorating functional status, and (iii) an increase in activity level without change in MV indicates a clinical improvement.


   Methods
Top
 Abstract
 Introduction
 Methods
Results
 Discussion
 Conclusions
 Acknowledgements
 References
 
To develop and test the new remote automatic warning system, stored data were retrieved from a population of DDD pacing systems recipients (DDD group) without signs or symptoms of CHF and a group of CRT systems recipients (CRT group) with CHF: the study was conducted according to protocols that followed the rules of ‘Good Clinical Practice’, and according to the human research regulations of France, UK, Germany, and Italy.

The DDD group included 19 patients (mean age = 71 ± 8 years, 11 men) who had no history or manifestations of CHF, and who had received TalentTM 3 DR pacemakers (ELA Medical Montrouge, France) for conventional indications, including atrioventricular block or advanced intraventricular conduction disorders in 12, sinus node dysfunction in 4, and bradycardia–tachycardia syndrome in 3 patients. The underlying heart disease in this group included coronary artery disease in 3, obstructive hypertrophic cardiomyopathy in 3, trivial aortic or mitral valve disease in 3, and no apparent structural heart disease in 10 patients. The CRT group included 48 recipients of Talent 3 CRT systems (ELA Medical). The indications for CRT were (i) New York Heart Association (NYHA) functional class III or IV CHF despite optimal medical regimen, (ii) a left ventricular ejection fraction (LVEF) ≤40%, and (iii) a QRS duration between 120 and 150 ms during sinus rhythm. The baseline characteristics of the CRT group are presented in Table 1. Their clinical status was observed over a 1-year follow-up, and all adverse health events and medical interventions were documented from a review of all available medical records.


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  		Table 1 Baseline characteristics of 48 patients included in the CRT group

 
Monitoring system
This new function is based on the monitoring resources offered by the Talent 3 DR or MSP (CRT) pulse generators (ELA Medical). These devices use transthoracic current injections to measure MV as a signal for rate responsiveness,5Go and an embedded ACT sensor that cross-checks the presence of activity vs. resting state, thus monitoring MV at rest and during activity, as well as total acceleration, interpreted as the ‘workload’ developed by the patient. These physiological variables are updated and stored daily in the pulse generator's memory for up to 3 months, and can be retrieved and displayed by the standard programmer.

The MV and ACT sensing were turned on, and all stored data were retrieved from the pulse generators, collecting data at 3 months (M3) in the DDD group, and at 3, 6 (M6), 9 (M9), and 12 (M12) months in the CRT group.

The data were downloaded to a Labview version 7.1 simulation software (National Instruments Corp., Austin, TX, USA) to simulate the routines of the expert system. The data retrieved from the CRT devices, divided into 1-month periods, were matched to the clinical status to define the sensitivity and specificity of the system in the warning of the caregiver within the prior 30 days.

Definitions


Formula

where TP is an adverse clinical event correctly predicted by the expert system within a delay of 3 days to 1 month prior to the event and FN is an adverse clinical event not predicted by the system within the month preceding the event.


Formula

where TN is a 1-month follow-up period without adverse clinical event and without alarm, and FP is a follow-up without adverse clinical event within 1 month after an alarm delivered by the expert system.


Formula

Warnings occurring over several days during a 30-day period preceding a documented adverse event were counted as a single warning.

Statistical analyses
Quantitative variables are presented as means ± standard deviation (range). All statistical tests are two-sided. A P value of 0.05 was considered statistically significant. Continuous variables were compared using Student's t-test, according to model hypothesis (normality of residuals and homogeneity of variance). Assumptions that were not confirmed were tested using the non-parametric Wilcoxon test.


   Results
Top
 Abstract
 Introduction
 Methods
 Results
Discussion
 Conclusions
 Acknowledgements
 References
 
Between-groups comparisons
Marked intra- and inter-individual daily variations in stored data were observed in all patients of both groups. Although the measurements of MV were generally stable, with up to 5% day-to-day variations present in 50% of follow-ups, the measurements of workload varied widely, with >10% day-to-day variations present in >50% of follow-ups. Because of these marked variations in daily ambulatory activities which may interfere with an embedded system, Fourier transformation analysis was performed, revealing a distinct weekly periodicity. Therefore, the three curves (MV at rest, MV during activity, and workload) were smoothed over 7-day periods before the data were entered in the simulation system for calibration. The mean workload, in g equivalent per day, and mean MV at rest and during activity, in litre per minute equivalent, retrieved from the DDD group and from the CRT group over a 3-month period, are shown in Figure 1A–C, respectively. Prominent inter-individual variations (20–30%) in all measurements were observed, corresponding to distinct mean MV and activity in each patient, in the absence of CHF, depending on height, weight, gender, and variability in device-related characteristics. The mean workload was similar in the DDD and the CRT groups, whereas both the mean rest and activity-related MV were significantly higher in the CRT group (Table 2).


Figure 1
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  		Figure 1 (A) Evolution of MV at rest in the DDD group vs. the CRT group. (B) MV during activity in the DDD group vs. the CRT group. (C) Evolution of workload in the DDD group vs. the CRT group. Each graph presents the mean values ± SD in each study group. The early post-discharge period (first 4 weeks) is clearly apparent on workload and rest MV curves in both study groups. The curves of the DDD and CRT groups have similar appearances (for same data), although rest and activity MV are higher in the CRT than in the DDD group. The wide standard deviations are due to marked inter-individual variations in patients' characteristics.

 


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  		Table 2 Mean workload and rest and activity-related ventilation in the DDD group vs. the CRT group after 3 months (mean values ± SD at week 12)

 
In addition, in both groups, the workload curves became flat at approximately 1 month in the absence of CHF event (and the MV curves at rest remained parallel), representing the time needed for patients with or without CHF to return to stable daily activities after implantation of the pacing system. This time period was not included in the definition and testing of the expert system.

Events and predictive value of the expert system
A total of 195 1-month periods of follow-up were documented by interrogation of the pacemaker memories. The patients' medical records revealed the occurrence of 25 CHF events in 16 patients, 22 of which were predicted by the system. A representative example of 1 among 22 accurate warnings by the expert system is shown in Figure 2. This patient experienced several adverse changes in clinical status without seeking medical attention before the routine follow-up visit at day 89. The patient was then hospitalized for management of CHF, after a >1-month period between onset of symptoms and onset of therapy.


Figure 2
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  		Figure 2 TP event. The patient was hospitalized at the time of a routine ambulatory visit for management of CHF, at the end of the third month of follow-up (day 89). Note that an alarm was delivered 1 month earlier, on days 58–62 (top mark), suggesting the occurrence of repetitive episodes of cardiac decompensation unreported by the patient. Five periods are shown: from post-implantation (day 0) to day 30 (period A), representing the early post-discharge period with stabilization of rest and activity MV curves. The workload curve rises reaching the daily life activity for this particular patient, with stable curves between days 30 and 50 (period B), indicating a stable clinical status. Between days 50 and 58 (period C), the curves show a marked increase in MV both at rest and during activity. The next period (days 58–74, period D) shows a decrease in both MV curves without important changes in workload. It is noteworthy that MV does not return to previous values, indicating a worsening of the clinical status. The last period (days 75–88, period E) shows the last episode of cardiac decompensation associated with a decrease in workload, consistent with marked adverse change in clinical status.

 
Because of the 7-day smoothing delay necessary for data collection and warning, there were three episodes of CHF not predicted by the expert system that occurred abruptly and required urgent hospitalization for management of acute cardiac decompensation. In these three cases, all curves retrospectively showed the acute episode and returned towards baseline shortly after intensive treatment of CHF. A representative example is shown in Figure 3. All curves were stable (period B) after the first month post-implant (period A) until the hospitalization (period C) for cardiac decompensation. The hospitalization and post-hospitalization periods (periods D and E) show the effects on workload and activity MV only.


Figure 3
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  		Figure 3 FN event. The patient was hospitalized for 1 week for management of CHF (between the two vertical lines, period B) 2 months after pacemaker implantation. The variations in the three curves before hospitalization (period A) were not marked enough to trigger an alarm. During hospitalization, a rearrangement of the drug regimen resulted in a decrease in both the workload and activity-induced MV (period B). After hospital discharge (period C), the variables tended to return to the pre-hospitalization levels, indicative of a rapid clinical deterioration after the patient was discharged from the hospital.

 
The expert system suspected 31 events, of which 9 were classified as FP, as there was no identifiable change in the patients' clinical conditions, corresponding to 0.55 unnecessary medical visits, per patient, per year. The mean time between warnings and actual events was 13 ± 9 days (range: 3–30, median = 12). The numbers of TN, TP, FP, and FN events observed at each study time point are listed in Table 3. The overall sensitivity, specificity, PPV, and NPV were 88, 94.7, 71, and 98.2%, respectively.


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  		Table 3 Numbers of TN, TP, FP, and FN events observed at each study time point

 

   Discussion
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
Conclusions
 Acknowledgements
 References
 
We evaluated the performance of a new monitoring system based on MV and ACT, both available in standard DDDR or CRT devices. We used the information provided by these sensors to analyse and report MV at rest and during activity as well as the workload corresponding to periods of activity, with a view to anticipate the development of cardiac decompensation. The testing of this new function in 48 recipients of CRT systems confirmed a high sensitivity, specificity, PPV, and NPV of the system, and a low number of inappropriate alarms per patient-year.

When developing this expert system, we assumed that, in healthy individuals as well as patients suffering from a stable chronic illness, energetic expenditures remain stable from 1 week to the next. In healthy individuals, including the elderly, a close correlation exists between aerobic capacity expressed as peak oxygen consumption and daily energetic expenditure.14Go,15Go The latter can be estimated by a questionnaire covering retrospectively all activities over a 1-week period, including the weekend, according to the minimal period of investigations defined by Passmore and Durnin,16Go divided by 7. More recent questionnaires adapted to patients suffering from CHF have also shown a close correlation between daily physical activity and peak oxygen consumption, particularly when the activity level corresponded to a ≥3 MET daily energy expenditure.17Go,18Go As observed in healthy subjects, optimally treated patients presenting with stable CHF maintain relatively constant energy expenditures from one week to the next, by adapting their activities to their aerobic capacity. During effort, patients suffering from CHF are most often limited by dyspnoea and muscle fatigue.19Go The pathophysiological mechanisms behind the development of these disease manifestations are complex and are not closely related to LV dysfunction. Several authors have found no correlation between central haemodynamics measured at rest and at exercise capacity, on the one hand,20Go and between pulmonary pressures and ventilatory response, on the other hand.21Go In contrast, fatigue can be explained by early dysfunction of peripheral muscles, which progresses over time and loss of endurance and overall muscle mass.22Go–24Go These alterations in muscle metabolism can be partially corrected by regular physical exercise.22Go,24Go

Exertional dyspnoea, manifest as a steep VE/VCO2 slope is, to a great extent, due to an enhanced exercise-induced reflex in the peripheral muscles, which increases ventilation at any given level of exercise. In this respect, physical re-training can improve the ventilatory response to exercise.25Go Conversely, progression of CHF is often associated with an increase in ventilation, and a steep VE/VCO2 slope is considered a predictor of poor prognosis, more sensitive than peak oxygen consumption.26Go Therefore, the stability of CHF depends on the efficacy of medical therapy, as well as factors such as sedentary life style or, conversely, physical fitness, variations in body mass,27Go,28Go and superimposed complications, including broncho-pulmonary infection, sudden salt and water overload, or an intercurrent illness. This is the source of variations in functional status, which can be ascertained by evaluations of rest and exercise ventilation, and activity level. Furthermore, when CHF remains stable, the measurements of rest and exercise ventilation remain similarly stable. In the case of improvement by physical training, or a weight reduction programme, or both, one should observe an increase in the amount of activity, without increase in ventilation at rest, and with an increase in exercise ventilation commensurate with the increase in activity. In case of clinical deterioration, activity decreases and ventilation increases both at rest and during exercise.

Continuous ambulatory monitoring of the patient's physiological status with implantable sensors is expected to prevent hospitalizations for CHF by anticipating the need for therapeutic adjustments. DDDR and CRT systems can also store physiological variables. These data can be reviewed at the time of scheduled visits, or be immediately transmitted to expert centres with a view to obviate hospitalizations,29Go and be used to continuously monitor the evolution of heart disease, assess the efficacy of a new therapy, or simply follow the patient's activity status.

Various sensors have been evaluated, or are currently available in implantable devices. Measurement of heart rate variability is a promising tool, which does not require a specific sensor.30Go,31Go In a large study of patients in NYHA functional classes III or IV, its sensitivity in the prediction of hospitalizations for cardiovascular disorders was 70%, with 2.4 FP events per patient-year.31Go The ventricular evoked-response recorded by permanent pacemakers was closely correlated with echocardiographic indices in a small group of patients.32Go Another simple method, using changes in right ventricular (RV) pacing impedance,33Go may reliably follow changes in LVEF and NYHA functional class, although its performance depends on the lead position. Transvenous RV pressure measurements, available for several years,34Go have been incorporated in the dedicated Medtronic Chronicle® (Medtronic, INC., Minneapolis, MN, USA) device.35Go–39Go This limited monitoring of haemodynamic function is, however, unlikely to provide enough information to reliably follow the complex clinical evolution of CRT recipients. Oxygen saturation sensors, introduced several years ago,40Go,41Go yield highly reproducible and stable measurements over the short term, although suffer from an unacceptably high failure rate when implanted for long periods of time.35Go

Two sensors seem most promising in monitoring CHF in paced patients. The oldest one, available in implanted devices, measures peak endocardial acceleration (PEA) by an accelerometer embedded in the tip of a dedicated lead.42Go,43Go As PEA is closely correlated with maximum and minimum LV dP/dt, the system, which is stable over time, measures cardiac contractility. However, it requires a dedicated lead. The most recent system measures transthoracic impedance,44Go–50Go using current injections similar to those used to measure MV. As it measures ‘static’ impedance, it is capable of detecting the early accumulation of fluid in the lung of patients with CHF. In a preliminary series of 33 patients in NYHA functional classes III or IV, a high correlation was observed between static impedance and (i) symptoms before hospitalization for CHF and (ii) pulmonary capillary wedge pressure and net fluid loss or gain. The sensitivity of impedance in predicting hospitalization for fluid overload was 76.9%, with 1.5 FP alarms per patient-year of follow-up,51Go limiting the reliable application of the system to disorders associated with pulmonary fluid overload.

Our expert system was highly sensitive to cardiac decompensation, while preserving high specificity. Furthermore, the monitoring of variables related to ventilation as well as activity enabled this system to warn of events that were not strictly haemodynamic and which, if not treated rapidly, might have had major health consequences in these seriously ill patients. Finally, this patient-independent information is of particular value to the physician-in-charge of the long-term care.

Limitations of the study
A limitation of our system is its sensitivity to events other than cardiac decompensation, which may trigger inappropriate alarms. However, the trigger of an alarm in response to adverse clinical events that are not of pulmonary or haemodynamic origin might, in fact, reveal other disorders, which otherwise would remain unsuspected. Another limitation of the study was its retrospective design, based on an a posteriori review of the patients' medical records. Finally, no conclusion can be drawn at this time with respect to the true preventive value of the expert system.


   Conclusions
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Conclusions
Acknowledgements
 References
 
An expert system that holds promise for the long-term ambulatory monitoring of CRT system recipients was developed. This new diagnostic technology should eventually be applicable to telemedicine.


   Acknowledgements
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Conclusions
 Acknowledgements
References
 
The authors thank Rémi Nitzsché and Rodolphe Ruffy for reviewing the manuscript.

Conflict of interest: none declared.


   References
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Conclusions
 Acknowledgements
 References
 
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