Combined Heart Failure Device Diagnostics Identify Patients at Higher Risk of Subsequent Heart Failure Hospitalization

1. Combined Heart Failure Device Diagnostics Identify Patients at Higher Risk of Subsequent Heart Failure Hospitalizations:Results from PARTNERS HF Study • on behalf of the PARTNERS HF Study Investigators David J. Whellan, MD FACC Associate Professor of Medicine Director, Jefferson Coordinating Center for Clinical Research Jefferson Medical College Whellan et al. JACC Vol. 55, No. 17, April 27, 2010:1803–10

2. Disclosure • I will not discuss off label use or investigational use in my presentation. • I have financial relationships to disclose: • Employee of: Thomas Jefferson University • Consultant for: Medtronic • Stockholder in: NA • Research support from: Medtronic • Honoraria from: Medtronic

3. Background • Despite medical interventions, there remains a high rate of HF hospitalizations in the CRT-D patient population. • Identifying at risk patients is a challenge • Recent publications have shown a single parameter HF diagnostic identifies high risk patients 1,2 1. Small et al. J Card Fail. November 2009; 15(9):813. 2. Perego et al. Interv Card Electrophysiol 2008, 23:235-242.

4. Hypothesis • Routine evaluation of combined diagnostics recorded by implantable devices can identify HF patients at risk for subsequent heart failure hospitalizations.

5. Combined Diagnostic Algorithm OptiVol VT/VF therapy AF burden Rate during AF %CRT HR Activity HRV • Based on 8 Diagnostic trends which are recorded daily • Individual algorithms for each trend have been used to flag significant observations from the trends in Medtronic ICDs/CRTs

6. Study Design • Prospective observational study • Subjects with CRT ICDs • 12-month follow-up, scheduled visits every 3 months • Clinical and device data collected at all visits • Limitations: • Clinicians had access to the diagnostics • Review and/or interventions based on trends were not required and alerts were not utilized

7. Methods – Categorization of Events • All CV and HF-related events were collected • Events and deaths were classified and adjudicated by an independent committee • Primary endpoint was the number of HF hospitalizations with pulmonary congestion

8. Combined Algorithm Positive Combined Algorithm = any 2 criteria + OR Fluid Index ≥100

9. Combined Algorithm • Algorithm criteria were tested on an independent data set from a registry (819 patients) to determine optimal # of criteria met to trigger combined algorithm • Combined algorithm also used prior finding that a high (≥100) fluid index alone has higher specificity

10. Monthly Evaluation Model Start* 30 60 90 ...Repeat until End of Follow-up Diagnostic Risk Assessment 1 HF Event Assessment 1 Evaluation 1 Diagnostic Risk Assessment 2 HF Event Assessment 2 Evaluation 2 Diagnostic Risk Assessment 3 HF Event Assessment 3 Evaluation 3 * Day 0 = later of consent date or 60 days post-implant • Repeated using Quarterly (90 days) and Semi-monthly (15 days) evaluations

11. Statistical Methods • Cox proportional hazards model to adjust for pre-defined clinical variables including: • Age • Gender • Heart Failure Etiology • NYHA Class* • Diabetes • HF Medication Regimen (Diuretics, ACE/ARB, B-Blocker)* • Sub-group analysis for subjects with and without a HF event. * Most recent prior to evaluation

12. Results: Cohort and Event Rates • 694 patients in this analysis cohort who had impedance monitoring and >2 months of FU • 60 patients (8.5%) had 78 monthly evaluation periods with at least one HF hosp. (pulmonary) • Low event rate: 1.4% (78/5693) of monthly evaluations had HF hosp. (pulmonary)

13. Baseline Characteristics (N = 694)

14. Combined Diagnostics Triggered % of evaluations when ≥2 Diagnostic Criteria Met (N = 960) AF AF+RVR OptiVol Low Night Low Low ICD Shock(s) Index ≥60 Activity HR HRV Pacing% 1324 monthly evaluations with combined algorithm triggered ≥ 2 Diagnostic Criteria Met OptiVol Fluid Index ≥100 ohm days Met 43% 29% 28% % of triggered evaluations

15. Kaplan-Meier HF Hospitalization Curves P < 0.0001 Hazard Ratio = 5.5 (95% CI: 3.4 – 8.8) + Diagnostic Evaluations with Heart Failure Hospitalization (Pulmonary) - Diagnostic Days After Diagnostic Evaluation Risk of a HF hosp. for pts with + Diagnostic was 5.5 x risk of pts w/ - Diagnostic

16. Multivariable Analysis P-Value 0.90 0.15 0.91 0.18 0.06 0.35 0.7 0.90 <0.0001 Age Gender Heart Failure Etiology NYHA Class* Diabetes (@baseline) Diuretics* ACE/ARB* Beta-Blockers* + Combined Diagnostic * Before evaluation date Hazard Ratio Patients w/ + combined diagnostic were 4.8 times more likely to have a HF hospitalization with pulmonary congestion independent of other clinical variables.

17. Subgroup by HF Event P = 0.85 Subjects with Pulm HF Hosp. P < 0.0001 Subjects withoutPulm. HF Hosp.

18. Effect of Evaluation Frequency Evaluation Frequency 15 Days (Semi-Monthly) 30 Days (Monthly) 90 Days (Quarterly) Hazard Ratio • More frequent evaluations enhance risk stratification. • Monthly evaluations provide reasonable balance ofrisk stratification benefit and clinician effort.

19. Conclusion • Patients with a + combined diagnostic were 5.5x more likely to have a HF hospitalization with pulmonary congestion before the next evaluation • The combined algorithm is an independent predictor of HF hospitalization in patients without a HF hospitalization, while it provides limited information in patients who have experienced a HF hospitalization.

20. Clinical Implications • Monthly evaluation of combined diagnostics can identify patients at a higher risk of a HF hospitalization within the next month. • Intervening by either modification of medications (i.e. diuretic dosing) or increase surveillance (i.e. clinic visit) may reduce clinical events

21. CaseStudy 1 HF Hosp. w/Pulm. Cong. 30 Day Evaluation window AF+ Fluid +

22. Case Study 2 HF Hospitalization with Pulmonary Congestion Evaluation Window OptiVol Index+ Activity+ HRV+