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

2 Monitoring Strategies for Heart 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 RVDP, right ventricle diastolic pressure. Weight (lb) weeks 4 weeks 2 weeks 1 day 5 days post RV Diastolic Pressure (mm Hg) weeks 4 weeks 2 weeks 1 day 5 days 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 P(mmHg) 05/20/0406/14/0407/10/0408/04/0408/30/0409/24/0410/20/ Detector Date HF Hospitalization

7 Continuous Hemodynamic Information: Prediction of Congestion Sensitivity Pressure Events Without Diuretic Change Days of Early Warning (Median) Learning Set83% (35/42)1.6/pt-yr (3.8)20 Test Set81% (43/53)1.6/pt-yr (3.9)26 Overall82% (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: PC (ms) VHP (ms)

10 Heart Rate Variability and CRT CRT, cardiac resynchronization therapy. Adamson PB, et al Circulation. 2003;108: CRT-ONCRT-OFF Standard Deviation of Atrial Cycle Length (ms)

11 Device-Based HRV and Survival HRV, heart rate variability; SDAAM, standard deviation of 5-minute median atrial-atrial intervals.. Adamson PB, et al. Circulation 2004;110: Months Survival SDAAM >100ms SDAAM ms SDAAM <50ms SDAAM 100ms: Hazard ratio =3.2; P=0.02

12 Heart Rate Variability and Outcomes N= Week HRV (ms) No-HF Minor event Hospitalized HF, heart failure; HRV, heart rate variability. Adamson PB, et al. Circulation 2004;110:

13 Continuous HRV Before Hospitalization Heart Rate Variability (ms) Night Heart Rate (BPM) Patient Activity (minutes/day) Days Relative to Hospital Admission HRV, heart rate variability. Adamson PB, et al. Circulation 2004;110:

14 HRV, heart rate variability. Adamson PB. Congest Heart Fail. 2005;11: Clinical Application of Continuously Measured Heart Rate Variability HRV Value (SDAAM) Predicted Event RiskSuggested Action <50 msHighEvery 2-4 Weeks msIntermediate Every 6-8 weeks with remote monitoring monthly >100 msLow Every weeks with remote monitoring monthly Persistent decline for 7 days HighAs for <50 ms

15 Other Parameters that Herald Congestion More Fluid Less Impedance (W) Days Before Hospitalization Impedance Reduction Duration of Impedance Reduction Reference Baseline

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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 ImpedancePhysiologic 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 or in-office CHF Clinical Team EP Clinical Team HF Data EP Data Data Exchange CHF, congestive heart failure; EP, electrophysiology; HF, heart failure.

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

22 HF, 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.

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