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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

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slide1

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

disclosure
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
background
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.

hypothesis
Hypothesis
  • Routine evaluation of combined diagnostics recorded by implantable devices can identify HF patients at risk for subsequent heart failure hospitalizations.
combined diagnostic algorithm
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
study design
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
methods categorization of events
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
combined algorithm
Combined Algorithm

Positive Combined Algorithm = any 2 criteria +

OR Fluid Index ≥100

combined algorithm1
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
monthly evaluation model
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
statistical methods
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

results cohort and event rates
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)
combined diagnostics triggered
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

kaplan meier hf hospitalization curves
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

multivariable analysis
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.

subgroup by hf event
Subgroup by HF Event

P = 0.85

Subjects with

Pulm HF Hosp.

P < 0.0001

Subjects withoutPulm. HF Hosp.

effect of evaluation frequency
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.
conclusion
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.
clinical implications
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
case study 1
CaseStudy 1

HF Hosp. w/Pulm. Cong.

30 Day Evaluation window

AF+

Fluid +

slide22

Case Study 2

HF Hospitalization with

Pulmonary Congestion

Evaluation Window

OptiVol Index+

Activity+

HRV+