Remote monotoring of cardiac rhythm devices: present and future Pacemaker and ICD

Transcription

1 Remote monotoring of cardiac rhythm devices: present and future Pacemaker and ICD Philippe Mabo University Hospital, Rennes, France ESC congress, Paris 30 August 2011 U642

2 Disclosures Biotronik: research grants, consulting Boston Guidant: research grants, consulting Medtronic: research grants, consulting St Jude Medical: research grants, consulting Sorin Group: speaker, research grants, consulting

3 Device management Scheduled control: 4-6 weeks post-implant every 6 months Stored data interrogation, acute testing Unscheduled control: First shock Iterative shocks (> 2 within 24 hours, ) Unexpected symptoms (syncope ) Device «alarm» ( battery voltage, lead impedance ) Manufactor alert

4 Device FU Present limitation Fast increase in device implant rate (ICD, CRT) Large cohorts of patients requiring systematic FU No correlation between scheduled visit and clinical event Real benefit of in-office control: device dysfunction? event analysis? need for reprogramming? prediction of an unexpected event?

5 Remote device management Patient with remote feaature implant Home transmission «box» Data transmission via phone network Manufacturer data center View data via internet, , SMS Remote data: Device status and stored data analysis Event (or alert) transmission

6 Easy device follow-up

7 Early detection of event Lead dysfunction A Lazarus, PACE 2007

8 Early detection of event Inappropriate therapy: T wave oversensing

9 Early detection of event Heart failure event triggerred by AF AT/AF total h/day V. rate during AT/AF, bmp OptiVol fluid index Patient activity h/day Thoracic impedance Average V. rate, bpm

10 Remote FU - Expected benefit Patient care improvement Easy device follow-up: no more need for in-office FU? Early detection of events: clinical value? Cost reduction Out patient visit Transportation (if reembursed) Less hospitalisations?

11 Remote FU No need for scheduled in-office FU? Standard control may be performed remotely Battery status Lead impedance Automatic sensing and pacing thresholds Stored data analysis Clinical status GP or cardiologist Need to share data

12 In-clinic ICD FU: real clinical impact Retrospective analyse data from 1739 in-clinic ICD visits in 169 patients Only 6% of scheduled in-clinic visits resulted in device reprogramming or patient hospitalization In 94% of all scheduled visits, remote follow-up would have sufficed ICD remote monitoring could potentially diagnose > 99% of arrhythmia- or device-related problems, if combined with clinical follow-up by the local general practitioner and/or the referring cardiologist Heidbuchel, Europace. 2008;10:351 7

13 Remote FU Event transmission Clinical impact Atrial fibrillation/flutter Embolic risk (OAC) Hemodynamic deterioration (CRT) Risk of inappropriate therapy (ICD) «Ventricular» events Repetitive condensator charges on NSVT (battery depletion) Inappropriate therapies (QoL, battery depletion) Lead dysfunction (safety, inappropriate therapy) need for clinical intervention?

14 Remote device management Clinical validation - Prospective trials Pacemaker: COMPAS trial (Presented ESC 2010) ICD: TRUST (Published) CONNECT (Published) EVATEL (Presented ESC 2011) ECOST (Presented ESC 2011)

15 COMPAS - Study Design The study investigates whether Home-Monitoring followup of PM patients is as efficient as conventional method in terms of Significant Serious Adverse Event (SSAE). CONTROL Group: patients receiving the standard of care Enrolment & Randomisation one month after implantation 18 months ACTIVE Group: patients followed with Home-Monitoring only Mandatory follow-up Conventional follow-up Additional follow-up triggered by Home-Monitoring or patient

16 Survival (%) COMPAS Survival probability for primary endpoint Death (all causes), Cardiovascular SAE, Device related SAE 100 N = ACTIVE Group CONTROL Group Log Rang Test p=0.63 Hazard Ratio : 0.90 (95%CI ) Non inferiority p< Months Kaplan-Meier Method

17 COMPAS Event rate Cardiovascular SAE ACTIVE (n=248) CONTROL (n=246) Events Patients Events Patients Ventricular arrhythmia Atrial arrhythmia Stroke * Heart failure ** 6 6 Acute Coronary Event Valvular heart disease Pericardial disease Other TOTAL 37 SAE (29 pts: 11,7%) * Including 4 deaths ** Including 3 deaths p=ns 33 SAE (32 pts: 13,0%)

18 Number of FUs per patient Total Follow-Ups over time 601 FUs in Control group vs 389 FUs in Active group 3.6 3, , , , , , , , , , , , , ,0-56% Interim FU p< % after last FU p<0.001 Δ=+1.15/year ACTIVE Group ± 95%CI CONTROL Group ± 95%CI Months -36% Δ=+0.50/year

19 COMPAS: unnecessary FU Interim FUs more contributory in the active group p<0.001 p=ns ACTIVE Group CONTROL Group

20 ICD TRUST and CONNECT Surrogate endpoints - TRUST N Charma et Al Circulation 2010; 122: N=1339 pts Number of total in-office device evaluations Reduction by 45% without affecting safety Median time to evaluation: <2 vs 36d (p=0.002) - CONNECT GH Crossley et Al JACC 2011; 57: N=1997 pts Time from clinical event to clinical decision. Reduced from 22 days to 4.6 days (p<0.001) Lenght of CV hospital stay: 3.3d vs 4.0d (p=0.002)

21 TRUST Trial: Primary endpoint N Charma et Al Circulation 2010; 122:

22 TRUST: Secondary endpoint Control (10.4%) HM (10.3%) Major Adverse Events (MAE) Death Stroke Event leading to reoperation.

23 CONNECT - Primary Endpoint Time from event to clinical decision in the Remote Arm was significantly shorter than in the In-office Arm (p<0.001) Event to Clinical Decision (median time) (per patient with at least one event) 22 Median time in the Remote arm was 4.6 days vs. 22 days in the In-office arm Number of Days Remote Arm (N=172 pts) In-office Arm (N=145 pts) Note: Data includes events for patients who crossed over, were non-compliant or had alerts occur prior to home monitor setup

24 CONNECT - Clinic Visits Annualized Rate Per Patient Year Remote Arm In-office Arm 0 Scheduled Visits Unscheduled Visits All Clinic Visits The observed rate of total clinic visits per patient year was Remote (3.92) vs. In-office (6.27)

25 CONNECT - Health Care Utilization Annualized Rate Per Patient Year p= p= p= Remote Arm In-office Arm 0.00 CV Hospitalization Emergency Department Unscheduled Clinic Visit * * Includes Urgent Care Visits

26 CONNECT Hospitalization duration Remote Arm = 3.3 days per hospitalization In-office Arm = 4 days per hospitalization Mean reduction 18% Estimated savings per hospitalization $1,659* Number of Days Mean Length of Stay Per Hospitalization Remote Arm In-office Arm 0 ICD CRT-D Overall (p = 0.002) * Estimated using the Medicare Limited Data Set - Standard Analytic Files from

27 EVATEL and ECOST First randomised trials with robust clinical endpoint Nearly similar design, inclusion criteria and endpoints Nearly similar results Cost/effectiveness analysis: pending

28 EVATEL and ECOST Differences between studies? Why the results are apparently different? ECOST EVATEL Sponsor Industry Institutional/Gov. Nb systems tested 1 (HM) All available Nb patients F/u duration (mo) Mean=27 12 Baseline characteristics Secondary prevention 1-st implant 47% 85% 35% 100%

29 EVATEL and ECOST Primary endpoint EVATEL ECOST p = p : NS Control (28.5%) Remote (38.5%) Primary endpoint Remote (30.2%) Death from any cause CV hospitalisation Ineffective or inappropriate therapies Control (41.5%) Major Adverse Events (MAE) Death from any cause Major cardiovascular event Major device-related adverse event Hypothesis of non-inferiority validated in ECOST (p<0.05) but not in EVATEL on per protocol analysis (p=0.0862)

30 Remote FU Inappropriate therapy EVATEL Control Remote Inappropriate or ineffective ICD therapy 60 (8.1%) 38 (5.5%) Ineffective ICD therapy 5 (0.7%) 6 (0.9%) Inappropriate ICD therapy 55 (7.5%) 33 (4.7%) ECOST - 32% Number of patients with 1 Inap Shock 22 (10.4%) 11 (5.0%) 0.03 Number of patients hospitalized %

31 EVATEL and ECOST Concordent results As compared with Standard care - Remote monitoring of ICD patients is clinically safe - No clear evidence that RM can contribute to prevent major CV events (even if favorable trend in ECOST) - Clear evidence that RM reduces the risk and number of inappropriate therapies: RRR=37-52% - Significant reduction on charged shocks and total shocks with remote monitoring : possible impact on device longevity (ECOST) - RM cost-effective? No clear response at that time

32 Remote device management Future? May be a new standard for device FU? But Need for the medical community to define clinical objectives for remote device FU Do not become slave of the technique Financial issues: to be considered for both health care systems and manufactors: new economic model to design and to validate Legal issues Are we openning the «Pandore box»?