Integrating Monitoring into the Infrastructure and Workflow of Routine Practice

Integrating Monitoring into the Infrastructure and Workflow of

Routine Practice

Philip B. Adamson, MDAssociate Professor of Physiology

Director, The Heart Failure Institute at Oklahoma Heart HospitalOklahoma City, Oklahoma

2

Monitoring Strategies forHeart Failure Patients

Call us – We’ll Talk

– Patient reported symptoms

– Daily weights

Come See Us!

– Frequent Assessment

– JVP, AJR

Ancillary Providers

– PA/NP/RN

Device-based monitoring

– Remote acquisition

– Continuous assessment with early warning

3

Why Is This Important?

Device era has created many new opportunities in patient management– Advances in technology

– Ability to proactively monitor patient

– Ability to monitor therapeutic responses

Device era has also created many new challenges– Need for coordination of care

– Need for collaboration

– Risk of data overload

4

The Risks of Poor Integration

Patients not knowing who to contract with symptoms

Important monitoring data not utilized to influence care

Important clinical data not integrated into device programming decisions

Numerous opportunities to improve quality of care and clinical outcomes missed

5

Head-to-Head Comparison:Body Weights and RVDP

Before Hospitalization

* * *

*P<0.05 vs 1 day before hospitalization. Bourge RC, et al. Presented at the American College of Cardiology Scientific Sessions 2006.RVDP, right ventricle diastolic pressure.

Weight (lb)

100

150

200

250

300

7weeks

4weeks

2weeks

1day

5days post

RV Diastolic Pressure (mm Hg)

10

15

20

25

7weeks

4weeks

2weeks

1day

5days post

6

Pressure Change Detection Concept

Threshold Crossing - Detection

ePAD

Reference

Detection Threshold

ePAD, estimate of pulmonary artery diastolic pressure; HF, heart failure.Adamson PB, et al. Circulation. 2005:abstract.

25

30

35

40

45

50

P(m

mH

g)

05/20/04 06/14/04 07/10/04 08/04/04 08/30/04 09/24/04 10/20/040

1

2

3

4

Det

ecto

r

Date

HF Hospitalization

7

Continuous Hemodynamic Information: Prediction of Congestion

Sensitivity

Pressure EventsWithout

Diuretic Change

Days of Early

Warning (Median)

Learning Set 83% (35/42) 1.6/pt-yr (3.8) 20

Test Set 81% (43/53) 1.6/pt-yr (3.9) 26

Overall 82% (78/95) 1.6/pt-yr (3.8) 24

•Adamson PB, et al. Circulation. 2005:abstract.

8

Monitoring Features of Therapy Devices

Atrial Depolarizati

on

Heart rate

AFIB/ATACH

APACE

Ventricular Rate Response

Heart rate

VT/VF

VPACE

Impedance

Patient Activity

Heart Rate Variability

AFIB, atrial fibrillation; ATACH, atrial tachycardia; APACE, atrial pacemaker skike; VT/VF, ventricular tachycardia/ ventricular fibrillation; VPACE, ventricular pacer spike.

9

Origins of Heart Rate Variability

VHP, variation in heart period.Katona PG and Jih F. J Appl Physiol. 1975;39:801-805..

0 100 200 300 400

PC

(ms)

1000

750

500

250

0

++

VHP(ms)

10

Heart Rate Variability and CRT

CRT, cardiac resynchronization therapy.Adamson PB, et al Circulation. 2003;108:266-269.

50

75

100

125

150

175

200

CRT-ONCRT-OFF

Sta

nd

ard

Dev

iati

on

of

Atr

ial

Cyc

le L

eng

th (

ms)

11

Device-Based HRVand Survival

HRV, heart rate variability; SDAAM, standard deviation of 5-minute median atrial-atrial intervals..Adamson PB, et al. Circulation 2004;110:2389-2394.

0.80

0.85

0.90

0.95

1.00

0 2 4 6 8 10 12

Months

Su

rviv

al

SDAAM >100ms

SDAAM 50-100ms

SDAAM <50ms

SDAAM <50ms vs SDAAM >100ms:Hazard ratio =3.2; P=0.02

12

Heart Rate Variability and Outcomes

N=262

40

50

60

70

80

90

100

1 3 5 7 9 11 13 15 17 19 21

Week

HR

V (

ms)

No-HF

Minor event

Hospitalized

HF, heart failure; HRV, heart rate variability.Adamson PB, et al. Circulation 2004;110:2389-2394.

13

Continuous HRVBefore Hospitalization

Hea

rt R

ate

Var

iabi

lity

(ms)

Nig

ht H

eart

R

ate

(B

PM

)P

atie

nt A

ctiv

ity

(min

utes

/day

)

Days Relative to Hospital Admission

-80 -60 -40 -20 0 2060

70

80

-80 -60 -40 -20 0 20140

160

180

200

220

-80 -60 -40 -20 0 2072

74

76

78

80

HRV, heart rate variability.Adamson PB, et al. Circulation 2004;110:2389-2394.

14HRV, heart rate variability.Adamson PB. Congest Heart Fail. 2005;11:327-330.

Clinical Application of Continuously Measured Heart Rate Variability

HRV Value (SDAAM)

Predicted Event Risk Suggested Action

<50 ms High Every 2-4 Weeks

50-100 ms IntermediateEvery 6-8 weeks with

remote monitoring monthly

>100 ms LowEvery 12-16 weeks

with remote monitoring monthly

Persistent declinefor 7 days

High As for <50 ms

15

Other Parameters thatHerald Congestion

Mor

e

F

luid

Le

ss

-28 -21 -14 -7 0

60

70

80

90

Imp

edan

ce (

W)

Days Before Hospitalization

Impedance Reduction

Duration of Impedance Reduction

Reference Baseline

Slide Missing

17

Information Flow from Device

18

Insight into Patient Status

AT/AF

V rate during AF

Patient Activity

Resting Night HR

HR Variability

% Pacing

Intrathoracic Impedance Physiologic Information

19

Barriers to Change

EP and heart failure collaboration– Time

– Established routines

– Geographic separation

– Financial concerns

– Patient volumes

– Information Systems

• Schedule

• Utility

•EP, electrophysiology.

20

Suggested Information Integration

CHF Patient

Device Implant

Device Referral

Device Follow-up

Remote orin-office

CHF Clinical Team

EP Clinical Team

HF Data

EP Data

Data Exchange

CHF, congestive heart failure; EP, electrophysiology; HF, heart failure.

21Adapted from Burke M, et al. AJN.104;(12) 40-44.

Strategies for Effective Collaboration

Develop relationships: “same team”

Determine preferred communication methods HF, EP, referring MDs

Know what you want to find out or report

Package information

– Much easier with new device diagnostics

Context of clinical situation

– Which details are most appropriate to share?

– Which details directly affect best clinical decisions?

– Reporting clinically essential information?

– Explain findings within appropriate context

22HF, heart failure; EP, electrophysiology.

Key Aspects for Improving Outcomes

Optimization of medical therapy

Optimization of device therapy

Education for both inpatients and outpatients– Reasonable expectations being given to patients

– Consistent information being given to patients

Increased outpatient access to healthcare professionals

Long-term patient follow-up

Routine communication between HF and EP

23

Monitoring for Proactive Management

Continuous physiologic parameters predict impending congestion– Autonomic control alterations, impedance changes,

and intracardiac pressure increases

– “Early warning” of meaningful changes

Communication Is the key element to success– EP and HF collaboration

Prevent congestion – Prevent progression?

EP, electrophysiology; HF, heart failure.

24