European Journal of Heart Failure (2014) 16, 663–670 doi:10.1002/ejhf.79
Long-term effectiveness of the combined minute ventilation and patient activity sensors as predictor of heart failure events in patients treated with cardiac resynchronization therapy: Results of the Clinical Evaluation of the Physiological Diagnosis Function in the PARADYM CRT device Trial (CLEPSYDRA) study Angelo Auricchio1*, Michael R. Gold2, Josep Brugada3, G. Nölker4, Siva Arunasalam5, Christophe Leclercq6, Pascal Defaye7, Leonardo Calò8, Oliver Baumann9, and Francisco Leyva10 1 Division
of Cardiology, Fondazione Cardiocentro Ticino, Lugano, Switzerland; 2 Medical University of South Carolina, Charleston, United States of America; 3 Thorax Institute, Hospital Clinic, Barcelona, Spain; 4 Herz- und Diabeteszentrum, Nordrhein-Westfalen, Bad Oeynhausen, Germany; 5 Desert Valley Hospital, Bear Valley Road, Victorville, CA, USA; 6 Cardiology Department, Pontchaillou, University Hospital, Rennes, France; 7 Cardiology Department, University Hospital, Grenoble, France; 8 Cardiology Division, Policlinico Casilino, Roma, Italy; 9 Sorin CRM SAS, Clamart, France; and 10 Centre for Cardiovascular Sciences, University of Birmingham, Birmingham, UK
Received 17 December 2013; revised 3 February 2014; accepted 7 February 2014 ; online publish-ahead-of-print 17 March 2014
Aims
Monitoring early signs of clinical deterioration could allow physicians to adjust medical treatment for patients at risk of acute heart failure decompensation. To date, several strategies using different surrogate measures of clinical status emerged, but none has yet been proven to predict clinical events. We hypothesized that the Physiological Diagnostic feature, which combines data from minute ventilation and physical activity sensors, predicts heart failure events in patients implanted with cardiac resynchronization therapy with defibrillation (CRT-D) devices. ..................................................................................................................................................................... Methods The Clinical Evaluation of the Physiological Diagnostic feature in the PARADYM CRT device (CLEPSYDRA) trial is and results a multicentre, prospective, non-randomized, double-blind study comprising 521 CRT-D patients with heart failure [67.4 ± 10.1 years (mean ± SD), 82% male, New York Heart Association class III/IV 85.0%/6.7%, QRS 155.3 ± 26.6 ms, left ventricular ejection fraction 25.7 ± 7.7%]. The objective of the study was the sensitivity and false positive rate of the Physiological Diagnostic algorithm to predict heart failure events within the following month. After a mean followup of 17.0 ± 8.7 months, 130 (25.6%) patients experienced a heart failure event. The sensitivity of the algorithm to predict an event was 34% and the false positive rate was 2.4 per patient-year. ..................................................................................................................................................................... Conclusion Thirty-four per cent of heart failure events occurring within a month were predicted by the Physiological Diagnostic algorithm, and 2.4 alerts per patient per year were not followed by an heart failure event within the subsequent month.
.......................................................................................................... Keywords
Activity • Cardiac resynchronization therapy • Heart failure monitoring • Minute ventilation • Predictive algorithm
*Corresponding author: Division of Cardiology, Fondazione Cardiocentro Ticino, Via Tesserete 48, CH–6900 Lugano, Switzerland. Tel: +41 91 8053340, Fax: +41 91 8053213, E-mail:
[email protected]
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The efficacy of cardiac resynchronization therapy (CRT) in heart failure (HF) patients with prolonged QRS duration and reduced ejection fraction is well established. It has been shown that CRT restores the coordination of contraction and relaxation among cardiac chambers, leading to improved exercise tolerance, reverse cardiac remodelling, and better survival among HF patients with a ventricular conduction delay. Despite efforts to understand the characteristics of patients responding to CRT and applying the latest findings to select appropriate CRT candidates, the proportion of patients not responding to CRT therapy, (i.e. remaining symptomatic and being hospitalized for HF) is largely unchanged. As the leading cause of hospital readmission, HF is a significant burden on health-care systems worldwide.1 In the USA alone, >39 billion US dollars were spent on the care of patients with HF in 2010 and most of this was spent on inpatient care. The US Centers for Medicare and Medicaid Services publicly reports rates of readmission within 30 days of discharge from a HF hospitalization, and 30 day readmission rates are likely to be bundled to reimbursement for the index hospitalization. Given the focus on 30 day readmission by payers and policy makers, there is a performance-driven need to develop treatment and management strategies that reduce early (and late) readmissions and ultimately mortality.2,3 Cardiac implantable electronic devices (CIEDs) record a variety of intracardiac signals as well as ventilation and physical activity. An increase in minute ventilation (corresponding to the ratio amplitude/period) may reflect a shortness of breath and a devicedetected reduction in patient activity may reflect clinically meaningful reductions in functional capacity. Accordingly, we hypothesized that combining measures of minute ventilation (MV) and patient activity would be useful in predicting HF hospitalizations. The current study explores the effectiveness of combining minute ventilation and patient activity sensors in predicting HF-related events in patients undergoing CRT with defibrillation device (CRT-D).
Methods Patient population Between 14 September 2009 and 13 April 2011, a total of 521 CRT-D patients were enrolled in 64 centres in Europe, the USA, and Canada participating at the CLEPSYDRA (Clinical Evaluation of the Physiological Diagnostic function in the PARADYM CRT device) study (ClinicalTrials.gov identifier NCT00957541), a multicentre, prospective, non-randomized, double-blind, single-arm trial. Eligible patients had New York Heart Association (NHYA) class III or IV symptoms of HF despite receiving optimal medical therapy, with a left ventricular ejection fraction of 30% or less from ischaemic or non-ischaemic causes, an intrinsic QRS duration of ≥120 ms, sinus rhythm or permanent atrial fibrillation or flutter with a controlled ventricular rate, and planned CRT-D implantation for indicated primary or secondary prevention of sudden cardiac death. Patients with a major coexisting illness or a recent cardiovascular event were excluded, as described previously.4 Patients should have experienced a HF-related event within the 6 months prior to enrolment and they all signed a written informed consent. Local Institutional Review Boards or Medical Ethics Committees of each participating institution approved the study protocol.
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Introduction
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Study design including follow-up and clinical evaluation of heart failure progression The study design has been published previously.4 Briefly, all patients underwent a history taking and physical examination, a medication evaluation, a 12-lead electrocardiogram and an echocardiogram at baseline. Follow-up visits were planned at 1 month post-implant and at 3-month intervals for 13 months, at months 4, 7, 10, and 13 until study closure or for a minimum of 13 months, whichever occurred first. Study termination was planned at the 13 month visit of the last patient enrolled. In the USA, patients were planned to terminate the study at device approval or study closure. During each scheduled or unscheduled follow-up visit, a device check was undertaken, medication recorded, 12-lead electrocardiogram performed, blood pressure measured and HF symptoms, NYHA functional class assessed and status worsening documented. Measurement of left ventricular ejection fraction at follow-up visits was optional. In the case of a worsening of HF status (decompensation or hospitalization), additional tests such as BNP and creatinine measurements were planned. At each study visit and any unscheduled visit, the CRT-D pulse generator was interrogated to retrieve data related to HF status; these data were stored in the database for further analyses. The last patient’s visit occurred on 6 June 2013. All patients self-assessed their clinical status in a diary and during monthly phone calls to provide additional information on medication changes, practitioner visits, and adverse events based on a pre-specified questionnaire.
Device programming The device and its programming have been extensively described previously.4 Briefly patients were implanted with a PARADYM CRT-D device (model 8770; Sorin CRM, Saluggia, Italy), a triple-chamber, rate responsive CRT-D device, featuring the PARAD+ algorithm and generating biphasic shocks of 42 J (maximal stored energy). In addition, the device features the Physiological Diagnostic (PhD) algorithm and a slow VT zone with a minimum rate of 120 bpm. The device parameter’s programming and lead choice was left to the investigators’ discretion. Although the PhD algorithm was activated in all patients, algorithm information was not displayed during the course of the study. This ensured that all participating physicians remained blinded to the algorithm data. Additional programming was left at the physicians’ discretion and was performed in accordance with device labelling and patient need.
Algorithm description The PhD algorithm combines data from a sensor of minute ventilation and a sensor of patient activity.4,5 Minute ventilation is a measure of respiratory capacity, derived from trans-thoracic impedance measurements between the can and the right ventricular (RV) lead, assessed breath-by-breath, allowing analysis of respiratory flow (in L/min) in different situations (rest and activity periods). Global activity is based on measurements from an accelerometer sensor located in the device can. Mean average data from the two variables (MV and activity) were recorded in the device on a daily basis and smoothed weekly by the PhD feature to yield a score. Changes were classified as increases or decreases. Thresholds were adjusted on each patient’s baseline status for each variable before indicating improvement or worsening, with the patient acting as his/her own reference. The more active the © 2014 The Authors European Journal of Heart Failure © 2014 European Society of Cardiology
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patient, the greater change was required to register a point score; while lower resting ventilation was assumed to reflect a healthier status, so a greater change was required to register a point score. These were then used to define a worsening in the patient’s HF status: (i) a sustained increase in rest ventilation and a sustained increase in effort ventilation not associated with an increase in activity; and (ii) reduced patient activity with no change in MV, or with an increased MV. Further details on the algorithm have been published elsewhere.4 The device does not generate alerts during the first 28 days after implantation (learning phase), but after this period the daily indication status is stored in the device memory. As clinical indications could not be generated in the first month following implantation, HF events occurring in the second month were considered for the analysis only if they were preceded by an indication in the same month.
Adjudication of heart failure events All clinical events were adjudicated by an independent Clinical Event Committee (CEC) and classified as HF-related or not: death; hospital admission; emergency room visit or urgent care visit requiring intravenous (i.v.) drug treatment (diuretics, inotropic medication); invasive intervention (assist device); admission to intensive care; initiation of any i.v. drug treatment related to heart failure, without hospital admission or emergency room or urgent care visit. The CEC comprised three physicians (Appendix 1) who were not participating in the trial and who were blinded to investigational sites and patients’ identity.
Objectives of the study The primary study objective was the evaluation of the effectiveness of the algorithm in detecting HF-related events assessed by its sensitivity and false positive rate. This primary objective was compared with a null hypothesis (benchmark) of 60% for sensitivity and two false positives per patient-year for the false positive rate. The effectiveness of the algorithm in detecting cardiovascular hospitalizations (defined as events classified as congestive heart failure, ischaemia, rhythm disorders and cardiac arrest) was also assessed.
Data collection and statistics Data originated from two sources: print-outs from the device programmer at each patient follow-up or visit, and the case report forms. Clinical indications for each patient were extracted from the patient files and imported into the Matlab (R2010a; Mathworks Inc., Natick, MA, USA) environment along with the information relating to each adjudicated HF event from the case report forms. Patients were included in the algorithm effectiveness analysis provided they were successfully implanted and had accurately defined adverse events (date and type of the event properly recorded in the case report form). The performance of the new sensor-based diagnostic feature was evaluated using events adjudicated as HF-related by the CEC. The HF-related clinical events were considered eligible for analysis only if the device was capable of generating indications in the 4 week period before the event and if the event did not occur during a hospitalization. Multiple clinical indications occurring on consecutive days were regarded as a single indication, taking the date of the first indication. Each eligible HFrelated adverse event was classified as either true positive or false negative according to the following rules: any eligible adjudicated HF event (as previously defined) preceded by at least one clinical indication in the 4 weeks before the event was considered as a true positive. All other events were considered as false negatives. Any clinical indications not
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© 2014 The Authors European Journal of Heart Failure © 2014 European Society of Cardiology
occurring in the 4 week period before a clinical event of any kind or 1 week following a hospitalization were considered false positive. The sensitivity was calculated as the ratio of accurately predicted adverse events to the total number of adverse events. The false positive rate was calculated as the number of clinical indications that did not accurately predict adverse events divided by the total follow-up period for all patients expressed as false positive clinical indications per patient-year. Continuous variables were expressed as means, standard deviations (SDs), ranges, medians, and interquartile ranges. Categorical variables
Table 1 Demographic characteristics at baseline Parameters All patients, n = 521 ................................................................ Age Male sex, n (%) Weight BMI Heart failure aetiology, ischaemic, n (%) LVEF QRS duration NYHA functional class, n (%) I II III IV Systemic hypertension, n (%) Conduction disorders, n (%) LBBB RBBB IVCD Paced rhythm (dependent) Rhythm, n (%) Chronic atrial fibrillation or flutter Sinus rhythm Associated co-morbidities, n (%) Renal failure Diabetes Dyslipidaemia Chronic pulmonary disease Sleep apnoea Measurements Systolic blood pressure Diastolic blood pressure Heart rate Creatinine Medication, n (%) Diuretic ACE inhibitors/substitutes/sartans Beta blocker Statins Aldosterone antagonist/spironolactone Implantation, n (%) (n = 508) De novo Upgrade
67.4 ± 10.1 years 427 (82.0%) 84.1 ± 17.2 kg 28.4 ± 5.4 kg/m2 298 (57.2%) 25.7 ± 7.7% 155.3 ± 26.6 ms 1 (0.2%) 31 (6.1%) 443 (85.0%) 34 (6.7%) 290 (55.7%) 349 (67.0%) 49 (9.4%) 54 (10.9%) 40 (7.7%) 107 (20.5%) 414 (79.5%) 93 (17.9%) 190 (36.5%) 251 (48.2%) 90 (17.3%) 46 (8.8%) 119.5 ± 18.7 mmHg 70.9 ± 11.9 mmHg 72.2 ± 16.4 bpm 1.4 ± 1.2 mg/dL 461 (88.5%) 454 (87.1%) 455 (87.3%) 285 (54.7%) 220 (42.2%) 356 (70.2%) 151 (29.8%)
BMI, body mass index; LVEF, left ventricular ejection fraction; NYHA, New York Heart Association; LBBB, left bundle block branch; RBBB, right bundle block branch; IVCD, intra ventricular conduction disorder; ACE, angiotensinconverting enzyme.
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Figure 1 Consolidated Standards of Reporting Trials (CONSORT) diagram of the study. AE, adverse events; CRT-D, cardiac resynchronization
Table 2 Clinical outcomes Heart failure events, n(%) 262 130 (25.6%) ................................................................ Heart failure deaths Heart failure hospitalizations Intravenous diuretic administration Intravenous inotropes administration Surgery Left ventricular assistance device Other type of hospitalization Heart failure events other than death and Heart failure hospitalization Intravenous treatment administration Emergency room or urgent care with Intravenous Oral diuretics Cardiovascular hospitalization* Hospitalization ‘all cause’
33 243 66 7 5 4 161 13
33 (6.5%) 123 (24.2%) 45 (8.9%) 6 (1.2%) 5 (1.0%) 4 (0.8%) 94 (18.5%) 10 (2.0)
10 2
7(1.4%) 2 (0.4%)
1 566 1016
1 (0.2%) 224 (44.1%) 323 (63.6%)
* Defined as events classified as congestive heart failure, ischaemia, rhythm disorders, and cardiac arrest
were expressed in frequency distributions. Cumulative survival and freedom from events were evaluated using Kaplan–Meier analyses. Statistical analyses were performed by an independent statistician from
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therapy with defibrillation; PhD, Physiological Diagnostic.
Soladis (Lyon, France) in SAS v9.2 (SAS Institute, Cary, N.C.) on the frozen database dated 20 September 2013.
Results Patient demographics Main baseline characteristics are displayed in Table 1 and were typical of a CRT-D population. The vast majority were male patients with history of ischemic cardiomyopathy. One-fifth of the population (20.5%) had a history of atrial fibrillation and many patients had other co-morbidities. One-third of CRT-D implants were upgrades from a previous pacemaker or implantable cardioverter defibrillator, or a CRT device replacement. Out of the 521 patients included, implantation was attempted in 516 (99%) and was successful in 508 (97.5%). The study flow diagram is shown in Figure 1.
Clinical events The mean follow-up of the study was 17.0 ± 8.7 months (median 16.4 months, minimum 0 to maximum 40.7 months). A total of 69 patients (13.6%) died during the course of the study. Of these deaths, 33 (6.5%) were adjudicated as HF-related. Non-HF-related deaths were from cardiovascular causes (2.6%), infections (0.2%), © 2014 The Authors European Journal of Heart Failure © 2014 European Society of Cardiology
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A
B
Figure 2 Kaplan–Meier estimates of the cumulative incidence of heart failure (HF)-related events (A) and HF deaths and HF hospitalizations
systemic organ failures (0.8%), pulmonary causes (0.6%), cancer (0.2%) or other causes (2.8%). Eight patients (1.6%) underwent cardiac transplantation. A total of 130 (25.6%) patients experienced 262 HF-related events: 33 HF deaths in 6.5% of patients, 243 HF hospitalizations in 24.2% of patients, and 13 HF events other than death and hospitalization in 2.0% of patients. A detailed counting of the study clinical events is provided in Table 2. The overall Kaplan–Meier estimates of the cumulative incidence of HF related-events are shown in Figure 2. Death from any cause or HF hospitalization (whichever came first) was experienced by 147 patients (28.9%, 285 events).
Algorithm performance Device data collected from the device programmer were available for approximately 90% of the follow-up visits. As clinical indications
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(B).
could not be generated in the first month following implantation, HF events occurring in the second and following months were considered for analysis only if they were preceded by an indication in the same month. A total of 103 HF-related adverse events in the eligible population (n = 457) were included in the analysis. A total of 35 alerts led to correct prediction of a subsequent event, while 68 events were not predicted (false negative). As shown in Figure 3, the distribution of HF related-events and cardiovascular hospitalizations (predicted and unpredicted events by the algorithm) in relation to time (in months from the implantation) was unequally distributed over the follow-up, with a peak after month 13. The overall algorithm sensitivity was 34%. However, the sensitivity of the algorithm ranged from 62% at month 2–3 to 12% at month 10–11 (Figure 4). Finally, 2.4 alerts per patientyear were generated by the device and not followed by an event within a month.
Figure 3 Predicted and unpredicted heart failure (HF)-related events and cardiovascular (CV) hospitalizations with time.
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Figure 4 Physiological Diagnostic (PhD) sensitivity in predicting heart failure (HF) related-events and cardiovascular hospitalizations with
Adverse events Events relating to the implantation were: pocket haematoma (2.8%), pocket infections (1.8%), pneumothorax (0.8%), pleural effusions (0.4%), and endocarditis (0.4%). Adverse events included lead dislodgements (9.8%), diaphragmatic stimulation (5.7%), and oversensing (1.0%).
Discussion CLEPSYDRA is the first large, observational, blinded study to assess the sensitivity and predictive value of a novel algorithm combining information of two different device-based sensors, minute ventilation and physical activity, among patients indicated for a CRT-D. The overall sensitivity of the novel algorithm for the detection of HF related events was as low as 34% and the false positive rate was 2.4 per patient year. Thus, changes in minute ventilation and in physical activity, alone or in combination, do not always reflect worsening of HF. The results of this study may have significant implications for the use of device-based algorithms in HF patients.
Assessment of heart failure using device-based sensors Device diagnostics such as heart rate, heart rate variability, physical activity level, intrathoracic impedance, or minute ventilation may be helpful in predicting clinical outcome in ambulatory HF patients with implanted devices. Intuitively, a decrease in patient activity is a sensitive and objective marker of declining functional capacity. Combined with other device-based diagnostics, such as intra-thoracic impedance or minute volume trends, these data may
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time.
provide a wealth of information regarding a patient’s clinical status. In terms of clinical application, these device diagnostic variables are easily obtained from routine device interrogation. In addition, the recent advent of wireless transmission and remote monitoring highlights the potential for using device-based sensors to predict clinical deterioration. Remote monitoring allows close follow-up of these parameters, enabling intervention before clinical decompensation.6 – 9 The low sensitivity of device-based algorithms tested in CLEPSYDRA study is not surprising in the light of the results of other recent studies testing algorithms using single device-based sensors. In this respect, the Sensitivity of the InSync Sentry OptiVol feature for the prediction of Heart Failure (SENSE-HF) trial which enrolled 501 patients, explored the sensitivity and positive predictive value of intra-thoracic impedance (OptiVol) data in relation to HF hospitalizations.10 Of 58 adjudicated HF hospitalizations during the first 6 months in Phase I (double blinded, 6 months), only 12 were predicted by intra-thoracic impedance (sensitivity = 20.7%). Sensitivity appeared to be dynamic in nature, with a progressive increase from 5.3% at 34–63 days after implantation to 42.1% at 148 days or more after implantation. It was, nevertheless too low for clinical application. Our results also show changes in sensitivity over time, but in contrast to the increase seen in SENSE-HF, we observed the highest sensitivity (62%) at months 2–3 before seeing a decrease. Although caution should be exercised when interpreting these findings, a possible explanation could be that HF events occur via different mechanisms early after CRT implantation or after a few months of therapy. In the HOME-CARE study, the projected sensitivity of the individual parameters to predict major cardiovascular events was variable and ranged from 23.6% for patient activity to 50.0% for P–P interval variability.2 The basic predictor, comprising five variables was associated with a sensitivity of 56.9%. The enhanced © 2014 The Authors European Journal of Heart Failure © 2014 European Society of Cardiology
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predictor with seven variables reached a sensitivity of 65.4%, indicating the value of the add-on strategy. These results indicate that nearly two-thirds of major cardiovascular events (not occurring within 30 days of implantation or within 30 days after previous hospitalization) may be predicted in conjunction with a specificity of 99.5%, equivalent to 1.83 false positive alarms per patient-year, used as fixed input for the algorithm optimization procedure. Although comparison of direct algorithm performance is challenging because of differences in study methodology, the types of cardiovascular (or HF) events they predict, and the method of determining specificity, it would appear that singleor dual-sensor device-based algorithms are insufficient to predict impending HF hospitalizations.2,11 – 16 The reason for the inability to predict impending HF hospitalizations may be related to the complexity and multitude of the mechanisms leading to acute decompensation in chronic HF as well as to the fact that the physiological response to events differs and changes with time. The effect of co-morbidities on sensor-derived measures may also contribute to such changes.
Study limitations The study has methodological limitations. The lack of control group precludes quantification of the incremental effect of the sensor-derived measures. A further limitation, common to all predictive algorithms developed so far, is their inability to predict all the cardiovascular events that lead to hospitalization or death.2 In addition, HF symptoms related events, such as breathlessness and/or fatigue, that may have led to medication or fluid or salt intake changes, were not considered in the primary endpoint of the study. This may have potentially led to false positive classification and therefore to reduce the algorithm performance. These findings call for great caution in the interpretation of any positive or negative results. Finally, the study was neither randomized nor designed to assess clinical outcomes, therefore no claims for mortality rate or other clinical outcomes can be made.
Conclusions The overall sensitivity of the combined minute ventilation and patient activity sensors in predicting HF events in CRT-D patients is low. Further studies are needed to assess whether integration of other parameters that are currently recorded by implantable electronic devices, such as heart rate variability, atrial fibrillation burden, and ventricular arrhythmia burden add to the sensitivity of dual-sensor technology in predicting HF events.
Acknowledgments The authors thank the following Sorin CRM employees: Emmanuel Prades, for study coordination, Ophelie Calas-Zeroug for Statistical analyses and Anne Rousseau-Plasse, and Frédérique Maneval for editorial assistance.
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© 2014 The Authors European Journal of Heart Failure © 2014 European Society of Cardiology 1.
Funding The Clepsydra study was supported by Sorin CRM SAS, Clamart, France. Conflict of interest: A.A. is a consultant for Sorin Group, Medtronic, Biotronik GmBH, St Jude Medical, and EBR System. J.B. has received research grants from Medtronic, Boston Scientific, St Jude Medical, Biotronik, and Sorin Group. M.R.G. is a consultant for Boston Scientific, St Jude Medical, and Medtronic and has received research grants from Boston Scientific, Medtronic, St Jude Medical, and Sorin Group, and lecture fees from Boston Scientific, Medtronic, St Jude Medical, Biotronik, and Sorin Group. F.L. is a consultant and has received research support from Medtronic, St Jude Medical, Boston Scientific and Sorin Group. G.N. has received lecture fees from Medtronic, St Jude Medical and Sorin Group and is a consultant for Biotronik. S.A. is a consultant for Abbott endovascular Aegerion pharmaceutical and Synergy Corporation. P.D. has received lecture honorary/travel support from Boston Scientific, Medtronic and Sorin Group and has performed clinical studies supported by Boston Scientific, Medtronic and Sorin Group. C.L. has received lecture honorary/travel support from Biotronik, Boston Scientific, Medtronic, St Jude Medical and Sorin Group; he is a consultant to St Jude and performs/has performed clinical studies supported by Biotronik, Medtronic, Sorin Group, Boston Scientific and St. Jude Medical. L.C. has received lecture honorary/travel support from Biosense, Biotronik, Boston Scientific, Medtronic, Najamed, St Jude Medical and Sorin Group; he performs/has performed clinical studies supported by Bayer, Biotronik, Boston, Medtronic, Sorin Group and St. Jude Medical.
APPENDIX Study Steering Committee Angelo Auricchio, MD, PhD, Joseph Brugada, MD, Michael Gold, MD PhD, Francisco Leyva, MD, FRCP, FACC. Clinical Event Committee Chairman: J.N. Trochu MD, PhD, Nantes, France; V.M. Conraads MD, PhD, Antwerp, Belgium; A. Bank MD, St Paul, MN, USA. Study investigators Canada: Bernard Thibault, Jean-François Sarrazin, Raymond Yee. France: Jean-Marc Dupuis, Philippe Ritter, Pascal Defaye, Salem Kacet, André Pisapia, Marc Burban, Gilles Lande, Sélim Abbey, Nicolas Sadoul, Serge Cazeau, Christine Alonso, Christophe Leclercq, Paul Bru, Frédéric Anselme, Antoine da Costa, Marc Delay. Germany: Johannes Sperzel, Georg Nölker, Johannes Brachmann, Hendrik Bonnemeier, Lars Eckardt, Herbert Nägele. Italy: Roberto Mantovan, Vittorio Calzolari, Leonardo Caló. Slovenia: Igor Zupan. Portugal: Pedro Manuel Pulido Garcia Adragão. Spain: Juan Gabriel Martinez, Rafael Peinado, Julio Beiras Torrado, Josep Lluis Mont Girbeau, Ignacio Fernandez Lozano, Luis Tercedor. Sweden: Frieder Braunschweig. Switzerland: Tiziano Moccetti. The Netherlands: Peter Paul Delnoy. UK: Francisco Leon Leyva, Ian Beeton, André Ng, Francis Murgatroyd. USA: Russell Reeves, Michael R. Gold, Karoly Kaszala, Dan Dan, Andrew Kaplan, Kimberly Parks, Bryan Frain, John Fedor, Eli Gang, Carmine Oddis, Rajesh Malik, Robert Malanuk, Steven Kutalek, Siva Arunasalam, Kevin Heist.
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© 2014 The Authors European Journal of Heart Failure © 2014 European Society of Cardiology
