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C A R D I O L O G Y G R A N D R O U N D S

Presentation:

Optimizing VAD and Transplant Outcomes

Speaker: Peter M. Eckman, MDSection Head ‐ Advanced Heart Failure, Minneapolis Heart Institute® at Abbott Northwestern Hospital Chair, Allina CV Service Line Heart Failure

Date: Monday, September 21, 2015, 7:00 – 8:00 AM

Location: ANW Education Building, Watson Room

OBJECTIVES At the completion of this activity, the participants should be able to:

1. Describe contemporary strategies for matching donated hearts with waiting recipients.

2. Describe techniques for non‐invasive evaluation of circulation of patients with ventricular assist

devices.

3. Describe the general principles of waveform analysis.

ACCREDITATION Physicians: This activity has been planned and implemented in accordance with the Essential Areas and policies of the Accreditation Council for Continuing Medical Education (ACCME) through the joint sponsorship of Allina Health and Minneapolis Heart Institute Foundation. Allina Health is accredited by the ACCME to provide continuing medical education for physicians.

Allina Health designates this live activity for a maximum of 1.0 AMA PRA Category 1 CreditTM. Physicians should only claim credit commensurate with the extent of their participation in the activity.

Nurses: This activity has been designed to meet the Minnesota Board of Nursing continuing education requirements for 1.2 hours of credit. However, the nurse is responsible for determining whether this activity meets the requirements for acceptable continuing education.

Others: Individuals representing other professional disciplines may submit course materials to their respective professional associations for 1.0 hours of continuing education credit.

DISCLOSURE STATEMENTS Speaker(s): Dr. Eckman has declared the following relationship; Consultant: Thoratec Corp.

Planning Committee: Dr. Michael Miedema, and Eva Zewdie have declared that they do not have any conflicts of interest associated with the planning of this activity. Dr. Robert Schwartz declared the following relationships ‐ stockholder: Cardiomind, Interface Biologics, Aritech, DSI/Transoma, InstyMeds, Intervalve, Medtronic, Osprey Medical, Stout Medical, Tricardia LLC, CoAptus Inc, Augustine Biomedical; scientific advisory board: Abbott Laboratories, Boston Scientific, MEDRAD Inc, Thomas, McNerney & Partners, Cardiomind, Interface Biologics; options: BackBeat Medical, BioHeart, CHF Solutions; speakers bureau: Vital Images; consultant: Edwards LifeSciences.

9/21/2015

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OPTIMIZING VAD & TRANSPLANT OUTCOMES

Peter Eckman, MD, FACCSection Head – Advanced HF

Disclosures:

Honoraria/Consulting: Thoratec, HeartWare, Medtronic, St. Jude Medical, Shape Medical Systems, Novartis (Prior)

Grant Support: Thoratec (Prior), Shape Medical Systems (Prior), HeartWare (Prior)

Intellectual property interest in the technique for VAD optimization

9/21/2015

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PowerSpeed

Unmet Clinical Need

LVAD Optimization

• Currently done clinically = “Art”

• Echo “optimization” at rest

• Current devices cannotautomatically adjust based on changing hemodynamic needs

• Change settings with rest versus exercise?

• Can gas exchange measurement help determine optimal settings?

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Exercise‐based optimization

1. Can we use gas exchange analysis to detect clinically meaningful differences in physiologic parameters with different LVAD speed settings?

2. Is there a difference between “optimal” settings at rest and with exertion?

3. How often do clinically set parameters match physiologically‐determined settings?

Methods• Speeds tested both at rest and with low intensity set treadmill exercise (0.8‐2.2 mph with 0‐2% grade)

• PETCO2 and Ventilatory Drive (VD) were measured at each condition at two‐minute increments

• Data from the last 30 seconds of each stage were analyzed

• Five LVAD speeds were tested in each patient (clinically set baseline, ‐400, ‐200, +200, and +400 rpm)

9/21/2015

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38

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m Hg)

HM2 VAD Speed Relative to Baseline

#

Rest‐Exercise Difference

By ETCO2

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6 X

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8 X

9 X

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Total 10/13

ΔETCO2 (mm Hg)

Rest Exercise

Mean±SD 2.0±0.8 2.0±0.8

1. Can we detect meaningful differences in ETCO2? Rest Exercise

VD* ETCO2 VD* ETCO2

1 X

2

3

4 X

5 X X X

6 X X

7

8 X X

9 X

10 X

11 X

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13

Total 0/13 2/13 7/13 3/13

2. Is there a difference between rest and exercise?

3. Is the clinical setting optimal?

Caccamo, ISHLT 2011

*VD = Ventilatory drive

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Lessons learned

1.Helpful in establishing physiologic value in VAD optimization

2.Impractical – Requires special equipment, can’t be applied in real‐time

3.Not always clear what the “best” setting is, and this would be expected to be very dynamic

Serendipity• Interested in studying endothelial function in VAD patients (role in thrombosis/hemorrhage?)

• Flow‐mediated dilation with U/S

• Commercially available devices didn’t work very well – variable signals assumed to be noise

• We bought our own equipment with intention of performing pulse wave analysis

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Wait…what?

Pulse wave analysis to study patients with blunted to absent pulses?

0.00

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PAT Signal Amplitude (Volts)

Pulsatility Index

Signal with amplitude that varies in time

Wikipedia “Fourier Series”

Time “domain”

Frequency “domain”

Approximation of a square wave by

adding sine waves

Each frequency has an amplitude (An)

Back to school…

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Basics: DSP Techniques (FFT & PSA)

Time (seconds)

Frequency (Hz)

Amplitude

Power

f0

f1 f2 f3FFT = Fast Fourier TransformPSA = Power Spectral Analysis

Fundamental

Harmonics

Jean Baptiste Joseph Fourier(21 March 1768 – 16 May 1830)

French mathematician & Physicist

Power Spectrum

1 2 3 4 sec

Amplitude

1 2 3 4 sec

Amplitude

1 2 3 (Hz)

Power

f0

f1 f2

2 4 6 (Hz)

f0

f1 f2

60 BPM

Power

120 BPM

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Freq

uen

cy

Time

Avery Li‐Chun Wang (Shazam)

Pulse Wave Analysis for VAD Optimization

(Non‐Invasive)

Peripheral Arterial

Tonometry

Power Spectral Analysis

Optimize Pump Speed

Time (sec) Frequency (Hz)

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0 0.5 1 1.5 2 2.5 3 3.5 440

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IABP ON IABP OFF IABP ON AGAIN

IABP (Central Pressures, considered Gold Standard)

PAT (Peripheral Arterial Tonometry)

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Time (sec)

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IABP (Central Pressures, considered Gold Standard)

PAT (Peripheral Arterial Tonometry)

IABP ON IABP OFF IABP ON AGAIN

Frequency (Hz)

Frequency (Hz)

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Do peripheral measurements reflect central pressures in VAD patients?

Central Aortic Pressures“IABP”

Peripheral Acquisition“Peripheral Arterial Tonometry”

The aortic valve doesn’t always open if you have an LVAD

8800 RPM 9400 RPM

9000 RPM

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Can we detect state of aortic valve without echocardiography?

• 25 HMII patients undergoing clinically‐driven echo‐based optimization

• Pump speed was ramped up between 8000 to 12000 RPM, in steps of 400 RPM

• At every pump speed change:

– PAT signals were acquired and PSA was performed

– Clinical LVAD parameters were noted

– AoV status was confirmed by the echo

21

9/21/2015

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09200 08000 08400 08800 09600 10000 10400 10800 11200 11600 12000 092000

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Function of Pump Speed

Peak f0 Peak f1 Peak f2

Energy f0 Energy f1 Energy f2

Open

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Pump Speed Pump Speed Pump Speed

09200 08000 08400 08800 09600 10000 10400 10800 11200 11600 12000 092000

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g(

)

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Function of Pump Speed

Peak Ratio f0/f1 Peak Ratio f0/f2

Energy Ratio f0/f1 Energy Ratio f0/f2

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Pump Speed

Ra

tio o

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eaks

(f0

/f1)

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Ra

tio o

f P

eaks

(f0

/f2)

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Open Closed Open

9200 11600 9200 (Ret)

Open Closed Open

Ratio of Peaks (f0/f 1)

Ratio of Peaks (f0/f 2)

01

23

45

67

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Signal Power

9200

Open

11600

Closed

9200 (Ret)

Open

AoV Status: Ratiometric Analysis

Outcome: Detection of Aortic Valve State

72% subjects demonstrated characteristic ratio signatures of AoV status

2

2

11 1

Total (n = 25)

9/21/2015

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• It is possible to detect the AoV status using PAT and DSP techniques

• DSP techniques to identify clinically useful signatures may be used to:

– Optimize therapy

– Identify pathology

– Enhance safety & efficacy

Atrial Fibrillation

0 1 2 3 4 50

0.5

1

1.5

2

Frequency (Hz)

Po

wer

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0

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Am

p

Ventricular Tachycardia

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p

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Po

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Acoustic spectral analysis

Kaufmann F et al, ASAIO J 2014

• 97 patients with normal function HVAD

• 8 with thrombus

• Acoustic Fourier analysis

Is pulsatility important?

• Non‐pulsatile devices may lead to lower rates of baroreceptor afferent discharge and less inhibition of sympathetic nerve activity

• Muscle sympathetic nerve activity is increased in patients with non‐pulsatile devices (below); may reduce possibility of ventricular recovery

Markham DW et al, Circ Heart Fail 2013.

0

250

500

750

1000

P‐VAD CF‐VAD CONTROL

Supine NE(pg/mL)

60 HUT NE

9/21/2015

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What’s next?

• Enhance specificity & sensitivity of current DSP techniques

• Test additional applications: thrombus detection, arrhythmia detection, hemorrhage

• Patient‐specific adaptation applied to clinical setting

• Can be applied to any current or future device

How do we know if a donor heart

is an appropriate size for a particular recipient?

9/21/2015

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You are on call for heart transplant…• Donor offer: 29F, South Dakota, Head Trauma

– 170 cm, 122 kg

• Potential Recipients:– #1: 63M, 182 cm, 90 kg (74% of donor)– #2: 66M, 178 cm, 84 kg (69% of donor)

• Current Clinical Approach– Will accept up to 25‐30% undersized (smaller donor)– Will accept 30% (sometimes more) oversized– Other factors:

• Sex matching and pulmonary pressures (PVR)• Donor age• Anticipated cold ischemic time• Consider height discrepancy

Background

• If a heart is too small, expect restrictive hemodynamics or RV failure

• If a heart is too big, may not be able to close the chest!

• What to do if there is a weight match but major height discrepancy (or vice versa)?

• Weight alone correlates poorly with cardiac size and has did not show mortality benefit at 5 years after transplant*

Tanner JM, J Appl Physiol, 1949Gutgesell HP & Rembold CM, Am J Cardiol 1990*Patel ND et al, Circulation 2008

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Adult Heart TransplantsKaplan‐Meier Survival by Donor/Recipient Gender

(Transplants: January 1982 – June 2011)

0

20

40

60

80

100

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Survival (%)

Years

Male/Male (N = 49,836)

Male/Female (N = 16,151)

Female/Male (N = 7,840)

Female/Female (N = 9,247)

Median survival: male/male=10.9; male/female=9.5; female/male=11.0; female/female=11.2

All pair‐wise comparisons with male/female were significant at p < 0.0001. No other pair‐wise comparisons were significant at p < 0.05

JHLT. 2013 Oct; 32(10): 951-964

2013

Adult Heart TransplantsKaplan‐Meier Survival by Donor/Recipient Gender Conditional

on Survival to 1 Year(Transplants: January 1982 – June 2011)

0

20

40

60

80

100

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Survival (%)

Years

Male/Male (N = 39,277)

Male/Female (N = 11,886)

Female/Male (N = 6,123)

Female/Female (N = 7,017)

Median survival: male/male=13.2; male/female=12.6; female/male=13.8; female/female=14.3

All pair‐wise comparisons were significant at p < 0.05 except male/male vs. female/male and female/male vs. female/female

JHLT. 2013 Oct; 32(10): 951-964

2013

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Adult Heart TransplantsKaplan‐Meier Survival by Donor/Recipient Weight Ratio

(Transplants: January 2003 – June 2011)

For recipients with PVR: 5+ wood units

50

60

70

80

90

100

0 1 2 3 4 5 6

Survival (%)

Years

<0.8 Weight ratio (N=99) 0.8‐<0.9 Weight ratio (N=132)

0.9‐<1.1 Weight ratio (N=301) 1.1‐<1.2 Weight ratio (N=121)

1.2+ Weight ratio (N=236)

No pair‐wise comparisons were significant at p < 0.05

JHLT. 2013 Oct; 32(10): 951-964

2013PE: Small hearts are a problem if PH+

ADULT HEART TRANSPLANTS (2006‐6/2011)Risk Factors For 1 Year Mortality with 95% Confidence Limits

Recipient Height

0.0

0.5

1.0

1.5

2.0

2.5

150 160 170 180 190 200

Hazard Ratio of 1 Year Mortality

Recipient Height (cm)

p = 0.0016

(N = 10,473)JHLT. 2013 Oct; 32(10): 951-964

2013

PE: Could this be higher risk congenital patients?

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Donor BMI/Recipient BMI ratio

0.0

0.5

1.0

1.5

2.0

0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5


Donor BMI/Recipient BMI

p = 0.0029

(N = 10,473)JHLT. 2013 Oct; 32(10): 951-964

2013

PE: Heavy/oversized donor is “protective”


Recipient Height

0.0

0.5

1.0

1.5

2.0

2.5

150 160 170 180 190 200


Recipient Height (cm)

p = 0.0022

(N = 10,332)JHLT. 2013 Oct; 32(10): 951-964

2013

PE: But if recipient is very tall, there is a penalty

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Ischemia Time

0.0

0.5

1.0

1.5

2.0

2.5

60 120 180 240 300 360


Ischemia time (minutes)

p < 0.0001

(N = 10,332)JHLT. 2013 Oct; 32(10): 951-964

2013

PE: We get nervous above CIT of 4h (240m)

Background• The concept of ideal or “predicted” heart size and chamber sizes has been recently developed and validated through the MESA studies

• Formulas to predict heart volume and mass based on age, sex, height, weight

• Recent study suggested that right and left ventricular mass was a better predictor than weight alone

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Reed RM et al, JACC Heart Fail 2014

Hypothesis: Prediction of survival after heart transplant

based on the MESA‐derived “virtual heart size” is superior

to weight‐based donor‐recipient matching

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Methods

• Study Population: UNOS database (n=45,483)– Adults (>18)– Dates: 10/1/87 – 12/31/12– Heart alone

• Recipients– Excluded height >250 cm or <100 cm (n=1090)– Excluded weight >160 kg or <40 kg (n=218)– Excluded BMI >60 or <15 (n=67)

• Donors– Excluded height >250 cm or <100 cm (n=6504)– Excluded weight >160 kg or <40 kg (n=143)– Excluded BMI >60 or <15 (n=27)– Excluded if age <12 (n=169)

• Left with 37,265 matched transplants

Methods

• Virtual Heart Size Measures: – Total cardiac volume (TCV)

Estimate of of anatomic size– Total ventricular mass (TVM)

Estimate of physiologic “size”

• Time points: – 1 month (Impact on early outcome)– 1 year (Program benchmark)– 10 years (Impact on late outcome)

• Calculate TCV, TVM for recipient and donor• Mismatch = [Recipient – Donor]/Recipient *100

– If donor is bigger (oversized), will have negative values– If donor is smaller (undersized), will have positive values

• For recipient, we are calculating their “ideal” heart size

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Distribution of mismatches

TVM (nearest 5%)

Oversized Undersized

Weight (nearest 5%)


TCV (nearest 5%)


Survival stratified by weight

Unadjusted Kaplan‐Meier survival curve of donor‐recipient groups

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0.01.02.03.04.05.06.07.08.09.1

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-100 -90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50Weight mismatch (%)

1 month weight (adjusted) 1 year weight (adjusted)

10 years weight (adjusted)

Red line = Population Mortality Rate

0.01.02.03.04.05.06.07.08.09.1

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1 month TVM (adjusted)

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1 year TVM (adjusted)

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-100 -90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50TVM mismatch (%)

10 years TVM (adjusted)


‐35% +10% ‐30% +10%

‐20% +20%

1 month 1 year 10 years

Oversize (big donor)

1.25 (1.04, 1.49)

1.18 (1.04,1.34)

1.04 (0.92, 1.16)

Undersize(small donor)

1.25 (1.12, 1.40)

1.18 (1.09, 1.28)

1.04 (0.96,1.11)

Odds Ratios For Mortality (Ideal: ‐30% to +10%)

Donor bigger Donor smaller Donor bigger Donor smaller

Donor bigger Donor smaller

9/21/2015

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Applying our theory….Candidate #1 Candidate #2

Weight 137% 140%

Total ventricular mass 100% 109%

Total cardiac volume 100% 118%

• Donor offer:– 170 cm, 122 kg

• Potential Recipients:– #1: 182 cm, 90 kg (74% of donor)

– #2: 178 cm, 84 kg (69% of donor)

• With conventional criteria, would decline for both

• Virtual heart matching analysis suggests that size matching considerations alone would predict favorable outcome for either recipient

What do clinicians want to know?

• Which hearts can we safely accept?

• Which hearts should we decline?

• Are the outcomes different based on sex match/mismatch?

• What about in the presence of pulmonary hypertension?

• Other interactions/confounders

9/21/2015

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Acknowledgements

• VAD Optimization– Ashish Singal, PhD– Harrison Kelner– Jennifer Nelson, RN– Aimee Hamel, RN– Mohammed Almekkawy, PhD

– Sameh Hozayen, MBBS– Mitsuhiro Oura– Evan Johnson– Marco Caccamo, DO

• Virtual Heart Size– Ziad Taimeh, MD– Sue Duval, PhD– Cindy Martin, MD– Rebecca Cogswell, MD

• Dr. Dan Garry and LHI• Medical Device Center Innovation Fellowship Program

• Shape Medical Systems

53

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-100 -90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50TCV mismatch (%)

.4.42.44.46.48.5

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-100 -90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50TCV mismatch (%)

1 month TCV (adjusted) 1 year TCV (adjusted)

10 years TCV (adjusted)


‐15% +20%

1 month 1 year 10 years

Oversize (big donor)

1.13(0.98, 1.30)

1.10(0.999, 1.22)

1.03(0.94,1.12)

Undersize(small donor)

1.30(1.03,1.65)

1.22(1.03, 1.45)

0.84(0.73,0.98)

Odds Ratios For Mortality (Ideal: ‐15% to +20%)

Donor bigger Donor smaller Donor bigger Donor smaller

Donor bigger Donor smaller

‐15% +20%

Supporting slides

56

9/21/2015

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Methods

• Equations:

• Predicted left ventricular mass (g)

ɑ × height (m)0.54 × weight (kg)0.61

ɑ = 6.82 for females and 8.25 for males.

• Predicted right ventricular mass (g)

ɑ × age (years)-0.32 × height (m)1.135 × weight (kg)0.315


• Predicted heart mass (mHT) (g)

Right ventricular mass + left ventricular mass

Methods

• Predicted left ventricular volume (ml)ɑ × height (m)1.25 × weight (kg)0.43

ɑ = 10 for females and 10.5 for males.

• Predicted right ventricular volume (ml)ɑ × age (years)-0.258 × height (m)1.582 × weight (kg)0.382


• Predicted heart volume (vHT) (ml)right ventricular volume + left ventricular volume

9/21/2015

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Methods

• Predicted total heart volume (ml)6 (LVEDP) + 12

• The percentage of match was calculated according to the percent difference inbetween the donor heart and the recipient heart, which we defined as:

% difference = 100 – ((Recipient-Donor)/Recipient)*100)

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