MobCon DH 2015 - Charles Bruce, Gary Conkright - collecting and leveraging physiological data to...

CONFIDENTIAL © MobCon Digital Health. All rights reserved.


Charles BruceProfessor of Medicine, Mayo Clinic

Revolutionizing Heart Care Through

Sensor Technology

charlesbruceCharles Bruce

2 February 13, 2015


Outline

What got us started?

Addressing the “clinical need”

Developing the solution

Challenges

Lessons learned

Future

3 February 13, 2015

CONFIDENTIAL © MobCon Digital Health. All rights reserved.4 February 13, 2015

My Mother


Vision: Remote Care

Develop integrated adaptable system

“Dashboard” overall health/well-being

Screening

Prevention

Management

Lengthen independence

5 February 13, 2015

Health Dashboard


Total clinic patients 1,260,000

Hospital admissions 131,000

Hospital days of patient care 608,000

Personnel 59,509

Total revenue (millions) 9,421

Mayo Clinic Facts

6 February 13, 2015


Population and Disease

7 February 13, 2015

Active disease

Asymptomatic disease

Overt risk factors for disease

Genetic & dietary predisposition


Developing the Solution

CONFIDENTIAL © MobCon Digital Health. All rights reserved.9 4/17/2015

Dis

Asymp

At risk

Predisp

• Alzheimer’s

• CHF

• AF

• HT

• DM

Person

• Algorithms

• CHF

• AF

• Biofluidics

• Prolonged ECG• Weight scale• Blood Pressure monitor• Accelerometer• Glucometer• Analytes• Other

Intelligent medical cabinet

Device companies

Pharmacy

Health care provider

Sensors

• Unobtrusive• Remote• Pervasive

Relatives

MobileHub

Sessile

• Data storage

• Data management

Infrastructure


Our Approach

Long-term, unobtrusive monitoring to promote mobility and independence

Focus on emerging epidemics

Atrial fibrillation

Congestive heart failure

Obstructive sleep apnea

Integration of physiological signals

Ensure system non-proprietary, adaptable, integrated

10 February 13, 2015


Remote Monitoring Platform

CHF Monitor “Proof of Concept”

ECG

Activity

Breathing

Scale

BP

Diagnostic/treatment algorithm


27 grams

STM32+BT ECG+ACC Battery (18 grams)


Personal Wireless Health Monitor



Remote Patient Care

ECG

Respiration

Physical activity

Symptoms

Wireless BP

and weight



7Getting started

At tach BodyGuardian Cont rol Unit to SnapSt rip

Be sure you are using a fully-charged BodyGuardian Control Unit.

1. Line up the BodyGuardian Control Unit and SnapStrip, as shown in Figure 3.

Figure 3. BodyGuardian Control Unit on SnapStrip

Tip: The snaps aren’t evenly spaced. The orientation of the logo on both the BodyGuardian Control Unit and the SnapStrip must be the same.

2. Press firmly so all four snaps are connected.

Prepare your skin

• health care provider, shave the area.

• Clean the area with soap and water or an alcohol wipe.

• Dry your skin completely.

• Do not apply anything to your skin, such as lotions, oils, sprays.

At tach BodyGuardian Cont rol Unit to your chest

1. Peel away the adhesive backing on the SnapStrip patch.

2. Place the BodyGuardian Control Unit on your chest. Your health care provider will select ONE of three placement options for you, as shown in Figure 4.







Remote Monitoring and AF Recurrence

27 patients undergoing DCCV or AF ablation

Monitored for 26.2 days


Ave time to AF

recurrence

11:23:18

Days : Hours : Minutes

Vogt et al: ACC, 2014

AF

No AF59%


Remote Monitoring and AF Recurrence

Physical Activity* significantly increased prior to recurrent AF

Patients with recurrent AF had higher average HR

72.6 ± 7.9 versus 64.1 ± 7.0 (p=0.02)

Average physical activity 5.9 ± 1.3 entire group

Peak activity level 15 min prior to recurrence higher than average

activity level

18.6 ± 10.8 versus 5.7 ± 1.2 (p=0.0002)


Vogt et al: ACC, 2014* Scale of 0 to 100


The Clinical Need


Quantified Self and Remote Care

23 4/17/2015

PreventionAtrial

Fibrillation

Chronic

Disease

Management

Heart Failure

Non BiologicEconomic

GPS

EnvironmentalSmartphone

Smart home

WearablesConsumer

Medical grade

ImplantablesPacemakers

Pressure sensors

Care Continuum

Data

Knowledge


Prevention: Atrial Fibrillation (AF) and Stroke

78-year-old woman fainted at casino

Palpitations beforehand

Paramedics recorded ECG

HT


Does she has AF?

If so, is she at risk for stroke?


0

5

10

15

20

0 1 2 3 4 5 6 7 8 9

CHA2DS2VASc Score


Lip et al: Chest, 2010

Points

Congestive heart failure 1

Hypertension 1

Age >75 years 2

Diabetes 1

Stroke 2

Vascular disease 1

Age 65 to 74 years 1

Sex category (Female) 1S

tro

ke

or

TE

ra

tio

(%

/ye

ar)


0

5

10

15

20

0 1 2 3 4 5 6 7 8 9

CHA2DS2VASc Score


Lip et al: Chest, 2010

Points

Congestive heart failure 1

Hypertension 1

Age >75 years 2

Diabetes 1

Stroke 2

Vascular disease 1

Age 65 to 74 years 1

Sex category (Female) 1

Total points 4

Str

oke

or

TE

ra

tio

(%

/ye

ar)


Prevention: Atrial Fibrillation and Stroke

Under diagnosed

Undertreated



Prevention: Atrial Fibrillation and Cryptogenic Stroke

Sanna et al: NEJM, 2014

Implantable Cardiac Monitor versus control: Detection of AF by 36 Months

No at risk

Control 220 194 167 114 72 36 7

ICM 221 191 173 102 57 29 8

Months since randomization

AF

de

tecte

d

(% o

f patients

)

0

20

40

60

80

100

0 6 12 18 24 30 36

0

10

20

30

0 6 12 18 24 30 36

HR 8.8 (95% CI 3.5-22.2)

P<0.001

ICM

Control


Chronic Disease Management: Heart Failure

Demographics of an Epidemic


Prevalence>6.6 M over age 18 (2.8%)

~9.5 M by 2030 (3.5%)

Incidence >680,000 new cases/year

Mortality ~57,000 deaths 2008 (more that all cancer combined)

Morbidity diagnosis annual office Most common discharge in patients >65 years; 12 M visits

Lifetime risk 1 in 5 after age 40

Roger et al: Circulation, 2012; Heidenreich et al: Circulation, 2011


Chronic Disease Management: Heart Failure


Heart Disease and Stroke Statistics Update: Circulation, 2010

Economic Burden

Total cost 39.2 ($B)

4.1

35.1

Direct expense

Indirect expense (excluding morbidity)

Expenditures ($B)

4.7

20.92.5

3.2

3.8

Hospital cost

Nursing home

Physician

Drugs, etc

Home health


U.S. Government Response

CMS – HF Outcome Quality Measures

30-day HF mortality and re-admission rates are publicly reported

30-day HF re-admissions will be linked to reimbursement


http:hospitalcompare.hhs.gov


An Opportunity?



Remote Heart Failure Care


Adapted from Desai, Stevenson: NEJM, 2010

Patient

Patient’s

physiological

indicators

Recommended

therapy plan

Anticipated

change in

status

Patient

receiving

own data

Midlevel team

member

empowered to

make decision

Midlevel team

member who must

wait for MD to

review and make

decision

MD

Most direct path to action Longer paths to action

Repeat measurement

Therapy implemented

Data transmitted by patient

Data received and processed for trends and alerts

Patient contacted

Non BiologicEconomic

GPS

EnvironmentalSmartphone

Smart home

WearablesConsumer

Medical grade

Data


Pressure sensors

WearablesConsumer

Medical grade


Pressure sensors

Knowledge


The Challenges

Technological

Adherence

Creating a tight feedback loop

Efficacy and proof points

Reimbursement

Regulations



Innovation Forces

Agonists

Escalating costs

Declining reimbursement

Aging boom

Increasing chronic disease

Antagonists

Innovator’s dillemma

Fee for service

Regulation



Elements Disruptive Innovation


Christensen: The Innovator’s Prescription

Business

Model Innovation

Technology

Enabler

Regulations

& Standards

Facilitate

Change

Value

Network


The FDA


BodyGuardian now being

used as a clinical tool


Future


Opportunities

Creation of framework for delivering appropriate and timely care

Proposed model - dedicated tele-medical center that operates 24/7

Practice integration

Improve outcomes

Cost savings

40 4/17/2015


HF Remote Monitoring NIH Research


PI Charles J. Bruce

Co-I

Paul Friedman, MD

Virend Somers, MD, PhD

Kevin Bennet, Engineer

Lyle Olson, MD

Kristin Vickers Douglas, PhD, LP

Rickey Carter, PhD

CoordinatorsNancy Lexvold, RN

Cindy Truex, RN

Remote LabRevelee Kaplan, CRAT

Doug Beinborn, RN; Peter Brady, MD

Industry Partner Preventice


Patients With Established Cardiac Disease

Chronic kidney disease, Diabetes, Coronary artery disease, and Peripheral

vascular disease most important predictors of hyperkalemia

Hyperkalemia significant predictor of all-cause mortality


Jain et al: Am J Cardiol, 2012


Bloodless Potassium


Bloodless Potassium


Bloodless Potassium


Big Data - Analytics


Medical institutionsPhysician specialistsPrimary care physicians NursesTechniciansPeoplePrevent-aid


Prevent-aid

Date post:	17-Jul-2015
Category:	Healthcare
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MobCon DH 2015 - Charles Bruce, Gary Conkright - collecting and leveraging physiological data to...

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