integrating monitoring into the infrastructure and workflow of routine practice
DESCRIPTION
Integrating Monitoring into the Infrastructure and Workflow of Routine Practice. Philip B. Adamson, MD Associate Professor of Physiology Director, The Heart Failure Institute at Oklahoma Heart Hospital Oklahoma City, Oklahoma. Call us – We’ll Talk Patient reported symptoms Daily weights - PowerPoint PPT PresentationTRANSCRIPT
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