Effect of implanted device-based impedance monitoring with - - PowerPoint PPT Presentation

Effect of implanted device-based impedance monitoring with telemedicine alerts on mortality and morbidity in heart failure (OptiLink HF)

Michael Böhm, Helmut Drexler†, Hanno Oswald, Karin Rybak, Ralph Bosch, Christian Butter, Gunnar Klein, Bart Gerritse, Johannes Brachmann for the OptiLink HF Study Investigators

† In memoriam

In Memoriam Helmut Drexler

Participating German Investigators (65 active Centres)

• H. Oswald (Medizinische Hochschule Hannover); J. Brachmann (Klinikum Coburg); K. Rybak (Kardiologische Praxis Dessau); C.

Israel (Evangelisches Krankenhaus Bielefeld); S. Käab (Klinikum München-Grosshadern); Chr. Butter (Herzzentrum Bernau); D. Bimmel (St. Marien Hospital Bonn); B. Huegl (Marienhaus Neuwied); Th. Heitzer (Klinikum Dortmund); Y. Seivani (Klinikum Bad Segeberg); R. Bosch (Cardio Centrum Ludwigsburg); D. Puplat (Asklepios Klinik Schwalmstadt); M. Ringwald (Kardiologische Praxis Bruchsal); D. Bänsch (Universitätsklinikum Rostock); M. Böhm (Universitätsklinikum Homburg-Saar); K. Gutleben (Herz- und Diabetes Zentrum Bad Oeynhausen); K. Seidl (Klinikum Ingolstadt); U. Tebbe (Klinikum Lippe-Detmold); K. Mischke (Universitätsklinikum Aachen); Chr. Perings (St. Marien Hospital Lünen); M. Haude (Lukaskrankenhaus Neuss); A. Knapp (Kardiologische Praxis Parchim); B. Zrenner (Krankenhaus Landshut-Achdorf); V. Schächinger (Klinikum Fulda); J. Schmitt (Universitätsklinikum Giessen); S. Willems (Universitätsklinikum Hamburg-Eppendorf); Chr. Stellbrink (Städtisches Klinikum Bielefeld); M. Hinterseer (Klinik Füssen); R. Erbel (Universitätsklinikum Essen); E. Hoffmann (Städtisches Klinikum München- Bogenhausen); C. Felder (Kardiologische Praxis Köln); U. Sechtem (Robert-Bosch Krankenhaus Stuttgart); H. Killat (Kardiologische Praxis Hassloch); M. Sotiriou (Kardiologische Praxis Wiesbaden); J. Schwab (Universitätsklinikum Bonn); S. Kuster (DRK- Krankenhaus Ratzeburg); F. Nagel (Kardiologische Praxis Augsburg); U. Fossmeyer (Kardiologische Praxis Traben-Trabach); S. Ruppert (Kardiologische Praxis Nürtingen); W. Raut (Krankenhaus Buchholz); D. Jäger (Klinikum Friedrichshafen); G. Mentz (Kardiologische Praxis Mainz); J. Schlichting (Kardiologische Praxis Herne); H. Keller (Kardiologische Praxis Coburg); T. Markert (Kardiologische Praxis Gaggenau); R. Jochheim (Kardiologische Praxis Hattingen); K. Jocham (Kardiologische Praxis Memmingen);

• Th. Muenzel (Universitätsklinikum Mainz); K. Goehl (Klinikum Nürnberg-Süd); H.P. Schultheiss (Charite Berlin); B. Lemke

(Märkische Kliniken Lüdenscheid); P. Mahr (Kardiologische Praxis Wiesbaden); Chr. Weiss (Städtisches Klinikum Lüneburg); Th. Wetzel (Kardiologische Praxis Dortmund); J. Stachowitz (St. Vincenz Krankenhaus Paderborn); M. Gawaz (Universitätsklinikum Tübingen); R. Gradaus (Klinikum Kassel); H. Fahlenbrach (Kardiologische Praxis Krefeld); S. Sack (Städtisches Klinikum München- Schwabing); B. Hammer (Kreiskrankenhaus St. Ingbert); J. Rieber (Kardiologische Praxis Leinfelden); G. Metzger (Kardiologische Praxis Bochum); Th. Lawo (Kliniken Bergmannsheil Bochum); S. Brune (Kardiologische Praxis Stade); A. Hummel (Universitätsklinikum Greifswald); R. Cierpka (Kardiologische Praxis Hannover); W. Hartung (Kardiologische Praxis Hannover); U. Gremmler (Kardiologische Praxis Peine); G. Bauer (Kardiologische Praxis Bad Mergentheim); S. Zieger (Kardiologische Praxis Esslingen); W. Haerer (Kardiologische Praxis Ulm); A. Hostert (Kardiologische Praxis Bad Neuenahr); M. Boitz (Kardiologische Praxis Berlin); G. Gola (Kardiologische Praxis Bernau); M. Henrichs (Kardiologische Praxis Rangsdorf); E. Liomin (Kardiologische Praxis Friedrichshafen); N. Schoen (Kardiologische Praxis Mühldorf); S. Helbig (Kardiologische Praxis Nürnberg); K. Droese (Kardiologische Praxis Dortmund); B. Lodde (Kardiologische Praxis Dortmund); W. Landgraf (Kardiologische Praxis Dortmund); Th. Fadgyas (Kardiologische Praxis Dortmund); Chr. Kirsch (St. Josef Krankenhaus Salzkotten)

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Board Members

Executive Committee

• Prof. Dr. med. Michael Böhm (Co-Chair)

Homburg / Saar

• Prof. Dr. med. Johannes Brachmann (Co -Chair)

Coburg

• Prof. Dr. med. Gunnar Klein

Hannover

• Dr. med. Hanno Oswald

Hannover

• Dr. med. Karin Rybak

Dessau PD Dr. med. Ralph Bosch Ludwigsburg PD Dr. med. Christian Butter Bernau

Event Adjudication Committee

• Prof. Dr. med. Markus Haass

Mannheim

• Prof. Dr. med. Wilhelm Haverkamp

Berlin PD Dr. med. Stefan Stoerk Wuerzburg

Data Safety Monitoring Board

• Prof. Dr. med. Stefan Anker (Chairman)

Berlin

• Prof. Karl Swedberg

Gothenburg, Sweden

• Prof. Hein Wellens

Maastricht, Netherlands

• Prof. Luigi Tavazzi

Cotignola (RA), Italy

• Prof. Stuart Pocock

London, United Kingdom

Disclosures

Authors received grant support from the study sponsor. Authors are responsible for the design and conduct of this study, the drafting and editing of the presentation and its final contents. Statistical support for the presentation and for the study design was provided by Bart Gerritse, Medtronic Bakken Research Center (BRC) Maastricht, Netherlands, and Joao Monteiro, Medtronic plc., United States. Sponsored and funded by Medtronic (Minneapolis, MN, USA).

Primary objective

Does Intrathoracic Impedance Monitoring with an Automatic Wireless Telemedicine Notification Compared to Standard Clinical Assessment Reduces All-cause Death or Cardiovascular Hospitalizations? Rationale and design of OPTILINK HF : Eur J. Heart Fail 2011;13:796-804 Trial Registration ClinicalTrials.gov ID: NCT00769457 Optimization of Heart Failure Management using OptiVolTM Fluid Status Monitoring and CareLinkTM (OptiLink HF Study)

Inclusion criteria

ICD Indication according to SCD-HeFT Chronic Heart Failure (HF), NYHA II – III, LVEF <= 35%, Optimized Medical Therapy (OMT) CRT-ICD Indication according to the ESC Guidelines (2008) Chronic Heart Failure (HF), only NYHA III, LVEF <= 35%, QRS >= 120msec , LVEDD >= 55mm Optimized Medical Therapy (OMT)

Additional Inclusion criteria

Patients with potentially higher risk of cardiac decompensation / detoriation At least 1 out of 4 criteria

• HF-related Hospitalization

within the last 12 months

• r
• IV-/Oral Diuretic Treatment

within last 30 days

• r
• Increased BNP

within last 30 days

> 400pg/ml

• r
• Increased NT-pro-BNP

within last 30 days

> 400 pg/ml (< 50 years)

> 900 pg/ml (50-75 years) > 1.800 pg/ml (> 75 years)

Exclusion criteria

Chronic renal failure needing renal dialysis Increased serum creatinine value (> 2,5 mg/dl)

within 14 days prior to enrollment

Chronic Obstructive Pulmonary Disease (COPD)

GOLD standard class III (severe) and IV (very severe)

OptiLink HF Study Design

Access arm:

telemedicine guided, no audible alert for fluid retention

Control arm:

standard clinical assessment, no alert for fluid retention

Risk stratified:

NYHA II vs. III, Ischemic vs. Non-Ischemic, Atrial Fibrillation, Primary vs. Secondary Prevention (VT/VF before Implant) Implant Enrollment Randomization 1:1 Access arm Control arm Follow up

6 Months every 6 Months

day 3 – 21 after implant

Follow up

6 Months every 6 Months

OptiLink HF Study Design Follow-Up

• 18 months/Patient
• Extended Follow-Up, if re-consent achievable

Primary endpoint

• composite of all-cause mortality and CV hospitalizations

Analysis

• time to first event

Secondary endpoints

• all-cause mortality
• CV hospitalizations
• composite of all-cause death and HF hospitalizations
• HF hospitalizations
• CV mortality
• all-cause hospitalizations during follow-up

Statistical methods

• Study was designed to include 1000 patients
• 80% power to detect 8.4% event free rate difference at 18 months

(72% control vs. 80.4% intervention, 30% relative risk reduction)

• Two interims analyses at 33% and 67% of 238 expected primary endpoints

(using p<0.0001 and p<0.001 respectively)

• Stratified log-rank test to compare between randomized arms

(Stratification variables: NYHA, Ischemic status, VT/VF history, AF history, device type)

• Hazard ratios (HR) and 95% confidence intervals (CI) reported from

Stratified Cox proportional hazards regression models

Telemedicine guided Intervention

Brachmann J, et al. Eur J HF. 2011;13(7):796-804.

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Mean follow-up was 22.9 ± 18.2 months

Patients enrolled (n = 1002) Randomised Patients 1:1 (n = 1002) Allocated to no alert transmission (n = 497) Received alert disabled system (n = 497) Did not receive alert disabled system (n = 0) Allocated to transmit fluid index alerts (n = 505) Received alert enabled system (n = 505) Did not receive alert enabled system (n = 0) Follow-up (mean 23.6 ± 14.0 months) Died (n = 59) Withdraw consent / Not completed (n = 61) Device replaced with incompatible system (n = 2) Follow-up (mean 22.3 ± 13.7 months) Died (n = 63) Withdraw consent / Not cpmpleted (n = 65) Analysed intention-to-treat (n = 505) Analysed intention-to-treat (n = 497) Excluded (n = 0)

Patient flow

Baseline characteristics

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Intervention (N = 505) Control (N = 497) Age – years 66.1 ± 10.1 66.4 ± 10.7 Male sex* 77% 82% Ischemic cardiomyopathy 54% 55% LV ejection fraction – % 26.7 ± 6.1 26.7 ± 6.1 Ventricular tachyarrhythmia 15% 14% NYHA class II, III 20%, 80% 19%, 81% HF hospitalisation within last 12 months* 68% 60% VR-ICD, DR-ICD, CRT-D 22%, 14%, 64% 25%, 14%, 61% Hypertension 72% 71% Beta-blocker 95% 92% ACE Inhibitor or ARB 91% 94% Diuretic 95% 95% Nitrate or vasodilator 8% 8% Aldosterone antagonist 69% 70%

Plus-minus values are means ± SD. *Significantly different between groups (P<0.05)

Intervention effectiveness

All Intrathoracic Fluid Threshold Crossings (FTC) 1748 In N (%) of 505 intervention patients 406 (80.4%) FTC transmitted via telemedicine 1324 % of all FTC* (75.7%)

* FTC not transmitted due to patient in hospital at time of FTC: 71 (4.1%), or not linked to system

FTC followed with Intervention acc. Protocol (CIP) 1128 % of 1324 transmitted FTC (85.2%) Patient reported worsening of HF symptoms 425 % of 1128 followed FTC (37.7%) Medical actions taken, including medication changes 529 % of 1128 followed FTC (46.9%)

Primary endpoint: All-Cause Death or CV Hospitalisation

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239

Control

227

Intervention

19

All-Cause Death or CV Hospitalisation by Subgroups

All-Cause Death

20

Intervention Control

CV Hospitalisation

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Intervention Control

Primary and Secondary endpoints

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Intervention (N = 505) Control (N = 497) Hazard Ratio (95% CI) P Value Primary composite endpoint – no. (%) Death from any cause or first CV hospitalisation 227 (45.0) 239 (48.1) 0.87 (0.72 – 1.04) 0.13 Death from any cause 59 (6.2) 63 (8.5) 0.89 (0.62 – 1.28) 0.52 First CV hospitalisation 214 (42.4) 221 (44.5) 0.89 (0.73 – 1.08) 0.22 Secondary endpoints Death from any cause or first HF hospitalisation – no. (%) 139 (27.5) 155 (31.2) 0.81 (0.64 – 1.03) 0.09

after adjustment on HF hospitalisation, IV diuretics prior to baseline 0.77 (0.61 – 0.98) 0.03*

First HF hospitalisation – no. (%) 119 (23.6) 128 (25.8) 0.87 (0.67 – 1.12) 0.28 Hospitalisations for HF – no. (events per patient per year) 220 (0.24) 218 (0.30)

• 0.20**

Death from cardiovascular causes – no. (%) 46 (9.1) 48 (9.7) 0.90 (0.59 – 1.35) 0.60

* Stratified Cox Regression Model with history of HF hospitalization (12 months) and IV diuretics (30 days) prior to baseline as covariates. For all the other endpoints the treatment arms did not differ significantly after same adjustment ** P-value based on a negative binomial model with treatment as covariate and log(follow up time) as an offset

Conclusions

OptiLink HF did not show superiority of a specific intra- thoracic impedance and telemedicine-based heart failure disease management strategy over standard clinical assessment. Telemonitoring depends upon multiple factors, successful transmission, subsequent intervention/medical action, and patient adherence. These latter obstacles need to be overcome.

• M. Böhm

Klinik für Innere Medizin III (Kardiologie / Angiologie / Internistische Intensivmedizin) Universitätskliniken des Saarlandes Homburg/Saar michael.boehm@uks.eu

Thank you!

Alert-Guided Remote Management

1748 Fluid Threshold Crossings (FTC) 1324 (76%) Successful transmissions 979 (56%) Patients contacted 529 (30%) Medical action taken 455 (26%) Medication altered

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Primary and Secondary endpoints

26 Intervention (N = 505) Control (N = 497) Hazard Ratio (95% CI) P Value Adjusted Hazard Ratio* (95% CI) Adjusted P Value* Primary composite endpoint – no. (%) Death from any cause or first CV hospitalisation 227 (45.0) 239 (48.1) 0.87 (0.72 – 1.04) 0.13 0.84 (0.70 – 1.02) 0.07 Death from any cause 59 (6.2) 63 (8.5) 0.89 (0.62 – 1.28) 0.52 0.86 (0.59 – 1.24) 0.41 First CV hospitalisation 214 (42.4) 221 (44.5) 0.89 (0.73 – 1.08) 0.22 0.86 (0.71 – 1.05) 0.14 Secondary endpoints Death from any cause or first HF hospitalisation – no. (%) 139 (27.5) 155 (31.2) 0.81 (0.64 – 1.03) 0.09 0.77 (0.61 – 0.98) 0.03 First HF hospitalisation – no. (%) 119 (23.6) 128 (25.8) 0.87 (0.67 – 1.12) 0.28 0.82 (0.63 – 1.06) 0.13 Hospitalisations for HF – no. (events per patient per year) 220 (0.24) 218 (0.30)

• 0.20**
• Death from cardiovascular causes – no. (%)

46 (9.1) 48 (9.7) 0.90 (0.59 – 1.35) 0.60 0.89 (0.58 – 1.34) 0.57 * Results from a Cox proportional-hazards regression model with history of HF hospitalisation (12 months), IV-Diuretics (30 days) and treatment arm as covariates. Moreover, this model is stratified by NYHA, ischemic status, VT/VF history. AF history and device type ** P-value based on a negative binomial model with treatment as covariate and log(follow up time) as an offset

Primary and Secondary endpoints up to 18-M-Follow-up

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Intervention (N = 505) Control (N = 497) Hazard Ratio (95% CI) P Value Primary composite endpoint – no. (%) Death from any cause or first CV hospitalisation 194 (38.4) 205 (41.2) 0.88 (0.73 – 1.08) 0.23 Death from any cause 30 (5.9) 41 (8.2) 0.71 (0.44 – 1.14) 0.15 First CV hospitalisation 184 (36.4) 193 (38.8) 0.89 (0.72 – 1.09) 0.25 Secondary endpoints Death from any cause or first HF hospitalisation – no. (%) 109 (21.6) 128 (25.8) 0.81 (0.62 – 1.05) 0.11 First HF hospitalisation – no. (%) 97 (19.2) 108 (21.7) 0.85 (0.65 – 1.13) 0.26 Hospitalisations for HF – no. (events per patient per year) 142 (0.24) 172 (0.30)

• 0.21*

Death from cardiovascular causes – no. (%) 21 (4.2) 30 (6.0) 0.69 (0.39 – 1.21) 0.19

* P-value based on a negative binomial model with treatment as covariate and log(follow up time) as an offset