How to use Information from

Implantable Sensors

Jag Singh MD DPhil FHRS

Massachusetts General Hospital

Harvard Medical School

Boston

Session: Follow up of CRT patients

Presenter Disclosure Information

FINANCIAL DISCLOSURE:

Research Grants: Boston Scientific, Biotronik,

Medtronic, St. Jude Medical

Steering Committee, DSMB Boston Scientific, CardioInsight,

Consulting: Medtronic, St. Jude Medical

Sorin Group, Biosense Webster,

theheart.org, Respicardia Inc.

Educational Symposia: Boston Scientific, Biotronik,

St. Jude Medical, Sorin Group

Stock Options: None

Salary Support: None

Speaker Bureau: None

Overview

• The Paradigm Shift in CRT Patient Care

• Sensors: • What and Why?

• Types

• Levels of Monitoring

• Role of Sensors • Clinical situations

• Risk Stratification

• Sensors in the Pipeline

• Tying it all together

CRT Patient: Need for a Multi-factorial Approach

TRIGGERS

ACUTE HF

CHRONIC HF

CO-MORBIDITIES

CRT

Patient

Chronic renal failure

Percent of Comorbid Diseases in CHF Patients

Depression

Thyroid

Lower respiratory

Osteoarthritis

Peripheral vascular

Dyslipidemia

Valve

Ocular

COPD

Diabetes

Arrythmias

CAD

HTN

Braunstein JB. J Am Coll. Cardiol. 2003 Oct 1;42(7):1226-33.

The Goal: Stabilize Disease Progression

• Cumulative effect of

recurrent acute heart

failure events leads to

progressive decline in

cardiac function

• Sensor strategies and

timely intervention may

help

Gheorghiade M, et al, AJC 2005

What is a Sensor?

• In a closed system, the sensor is an element

connected to the device that detects change

and signals the ‘effector’ to initiate a

response

• The effector could be:

– The Patient

– The Healthcare Provider

– The Device

Device Based Monitoring & Intervention

• Simple Sensors

• Heart rate and derivatives

• Accelerometers

• Impedance-based

• Sophisticated Sensors

• Pressure: left atrial pressure, pulmonary artery pressure,

RV dP/dt, etc.

• Heart Sounds: PEA

• Cardiac Output: Doppler

• Chemicals: PO2, PCO2, pH, electrolytes and glucose

• Biomarkers: TNF, BNP, etc.

Merchant, Dec and Singh JP. Circulation EP, 2010:3: 357

Heart Rate Trend and Hospitalization

Hospital stay

Ellery et al. Clin Res Cardiol 2006

Respiration Sensor : Progressive increase in Minute

Ventilation prior to HF event

Respiration Rate

Tid

al V

olu

me

Heart Rate Variability (SDANN) and Clinical

Course

Days from the Implant Visit

Sur

viva

l Pro

babi

lity

0 60 150 240 330 420

0.70

0.75

0.80

0.85

0.90

0.95

1.00

Quartiles of SDANN Two weeks

Days from the Three Month Visit

Sur

viva

l Pro

babi

lity

0 60 150 240 330 420

0.70

0.75

0.80

0.85

0.90

0.95

1.00

Quartiles of SDANN Three Months

Days from the Six Month Visit

Sur

viva

l Pro

babi

lity

0 60 150 240 330 420

0.70

0.75

0.80

0.85

0.90

0.95

1.00

Quartiles of SDANN Six Months

Days from the Implant Visit

Sur

viva

l Pro

babi

lity

0 60 150 240 330 420

0.70

0.75

0.80

0.85

0.90

0.95

1.00

Quartiles of Footprint Two weeks

IV (highest)IIIIII (low est)

Days from the Three Month Visit

Sur

viva

l Pro

babi

lity

0 60 150 240 330 420

0.70

0.75

0.80

0.85

0.90

0.95

1.00

Quartiles of Footprint Three Months

Days from the Six Month Visit

Sur

viva

l Pro

babi

lity

0 60 150 240 330 420

0.70

0.75

0.80

0.85

0.90

0.95

1.00

Quartiles of Footprint Six Months

Fantoni et al, JACC 2005:10;1875-82 Gilliam, FR, Singh JP et al, J Electrocardiol, 2007

HRV-HF Registry

Accelerometers & Physical Activity

Kadhiresan et al, AJC 2002:89;1435-7

Integrating the

Information

• Chicken & Egg

Situation

• Was the HF episode

precipitated by AF or

vice-versa?

• Non-responsiveness

worsened by atrial

arrhythmia

Merchant, Dec and Singh JP. Circulation

EP; 2010:3: 357

Risk Stratifying Prospectively: Using Device-based Signals

Simple Sensors and Outcome

• Clinical risk score

– Contak-Renewal Study

– Variables extracted from

device were dichotomized with

score of 1 for:

– SDANN <43

– mean HR >74

– Footprint < 29

– Physical activity % <5.

– Total score= sum of

dichotomized variables

– Low (1)

– Moderate: 2-3

– High: 4

Singh JP et al Europace 2010

N=838

18 % Mortality

4 % Mortality

PARTNERS-HF: Prognosticating Heart Failure Hospitalizations

• 694 CRT patients followed up for 1 year

• 141 HF hospitalizations

• Device diagnostic criteria when positive, were associated with a 5.5

fold increased risk for HF hospitalization

Whellan DJ et al, JACC 2010: 55: 1803

DOT-HF Study: Proactive intervention with

Audible Impedance Alerts

• 335 patients randomized to OptiVol information with audible alerts

• Heart Failure hospitalizations near 2-fold higher in the Access arm versus control arm

• 3-fold increase in outpatient visits

• Role of Impedance measures questionable?

Van Veldhuisen et al. Circulation 2011: 124

Sophisticated Sensors

Early Recognition & Intervention

for ADHF

Multivector Impedance

INTRA-CARDIAC VECTORS

• LV ring – RV ring

• LV ring – RA ring

INTRA-THORACIC VECTORS

• RV ring – Can

• LV ring – Can

• RA ring – can

• RV coil – Can

Khoury DS. J Am Coll Cardiol. 2009 Mar 24;53(12):1075-81

- Multiple vectors

- The LV ring–Can vector resulted in the fastest and largest change

in impedance and highly reflective of LV end diastolic volume and

LA pressure.

SV

SZ

Positive Correlation of

SZ and SV

LVEDV

ED

Z

Inverse Correlation of EDZ

and LVEDV

Impedance-based Sensor for Hemodynamics A Measure of LV volume changes

Ejection phase Filling phase

Impedance Impedance

R = 0.86

P < 0.001

R = -0.86

P < 0.001

Maier et al., Clin Res Cardiol 2007, 96 (Suppl 1):V1027

Proactive vs. Reactive Intervention Impedance vs. Hemodynamic sensors

Merchant F, Dec GW and Singh JP. Circulation EP, 2010:3: 357

SONR : PEA Sensor CLEAR & Respond-CRT Study

• Hemodynamic sensor embedded in the atrial lead

• Picks up cardiac vibrations which correlate with the the amplitude of the

first heart sound and dP/dt max

• Automatic optimization & programming

• ? Prognosticating

Rickards et al.. The Multicenter PEA Study Group. Pacing Clin Electrophysiol 1996;19:2066-2071. Bongiorni et al.. Pacing Clin Electrophysiol 1996;19:1682-1688. Tassin et al.. Pacing Clin Electrophysiol 2009;32(Suppl. 1):S101-104.

Left Atrial Pressure Sensor Device

Merchant, Dec and Singh JP. Circulation EP, 2010:3: 357

Proximal Anchor

Distal

Anchor Sensor

Diaphragm ~ 3

mm

Implantable

Sensor Lead

(ISL)

• 40 patient study

• Prospective, observational and first-in-human

• After initiation of LAP-guided therapy – frequency of elevated readings (>25 mmHg) was reduced by 67%

– Mean daily LAP fell (from 17.6 mmHg to 14.8 mmHg)

– Doses of ACEI, ARB’s and BB were up-titrated by 37% and 40%, respectively, where as doses of loop diuretics fell by 27%

– Reduced compliance and LAP> 25 associated with HF

Ritzema J et al. Circulation 2010; 121: 1086

LAP = 3 mmHg

LAP = 26 mmHg

a v

a v

P R

Daily Variations in Left atrial pressures (Further evaluation in LAPTOP-HF Study)

- Significant up titration of B-blockers

and ACE inhibitors

- Diuretic use comparable in stability

period

Ritzema et al, Circulation 2010

Pulmonary Artery Pressure Monitoring

*CardioMEMS Inc., Atlanta, Georgia, USA; Caution: Investigational Device

Champion Study, Lancet 2010

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

100.0%

0 90 180 270 360 450 540 630

HR = 0.71 (0.55-0.92), p = 0.009

No. at RiskTreatment270 226 202 169 130 104 84 62Control 280 223 186 146 113 80 57 39

TreatmentControl

Su

rviv

al o

r F

ree

do

m fro

m

First

HF

Ho

sp

ita

liza

tio

n

- Catheter-based delivery system

- Implanted PA branch diameter 7-15 mm

- Target range (mmHg):

– PA systolic: 15-35

– PA diastolic: 8-20

– PA mean: 10-25

29% relative risk reduction

550 NYHA III patients from 63 centers

Next Generation Hemodynamic Detection

• Bio-degradable material

• Miniaturization allowing implantations with minimally invasive techniques

• Locations: Endovascular & Epivascular

• Integrating the information for HF treatment

• Clinical testing and validation

EAP technology Bio-MEMs Sensor Technology (NASA)

Raisin System Links Therapy, Behavior and Physiology (Proteus Heart Study)

Self administration of Furosemide

Orthopnea / disrupted sleep Restful sleep

Putting it all together: Sensing & Intervening

Chronic HF

- Neurohormonal

status

- Compliance

- Myocardial risk

Heart Failure

Hospitalization

Triggers

- Ischemia

- Electrolytes

- Endothelial

function

• Arrhythmias

Co-Morbid - Diabetes

- HT

- COPD

- AF

EARLY DETERMINANTS

- Hemodynamic Instability

- LAP

- PADP

- RV dp/dt

- PEA

- Impedance-based

- Neurohormones

- Inflammatory markers

- CRP

- IL-6

- BNP, etc

- Acute vascular changes

- MR

- Venous drainage

LATE

DETERMINANTS

- Physical Activity

- Heart Rate

-HRV

-SDANN

-Footprint

- OptiVol

- Impedance-based

measures

Summary

• Where are we now? – Risk stratification for mortality

– Predicting heart failure exacerbations

– But still no concrete strategy and limited adoption

• Where do we need to be? – Sensors coupled with remote monitoring integrated into clinical

practice, facilitating personalized medicine

• The Future : – Multiple Sensors

– Automated prediction and prevention of heart failure

– Provide for care beyond heart failure

– Personalized medicine, with self-management strategies is here to stay

The Integrated Futuristic Device

Courtesy: David Hayes

Some important points

• Cardiomems – Intensive PAD monitoring

works

– Goal PAD 8-20

– Needs physician intervention

regularly

– Simpler implant

• LAP – Puts patient in control

– Computer algorithm incorporated

– Takes any intrinsic pre-capillary

PH out of the equation

– Direct access to LA and

waveforms

– More involved procedure

- Problem with both is that they leave the RV and RA out of the picture

- That’s why physical exam is important to understand patients physiology

- 24 hour averages maybe preferable than 2-points in time

Summary: Sensor Strategies

Current Generation:

• Surrogate measure

• Stand-alone

• Provider-dependent

• Intervention-independent

Next Generation:

• Primary measure

• Networked

• Provider-independent

• Intervention-dependent

Modified from Cleland, JACC 2009

Case example: LAP pre- and post Diuretics 60 year old man with ischemic CM (LVEF 25%)

a

v

a v

P R

= 40 = 13 LAP (mmHg) = 40 P LAP (mmHg) = 13

0900 hrs, Dynamic Therapy LAP-Guidance: Furosemide 80 mg 1300 hrs

Ritzema J, et al. Circulation 2010;121:1086-1095.