140622 IFCC-Worldlab PoCT and Telehealth Monitoring ... KLOSS_IFCC Worl… · Use of TH to monitor the disease and/or its management eHealth Telemonitoring Telehealth Telecare mHealth

IFCC PoCT Satellite Meeting Istanbul 2014

PoCT Enabling Patient-Centred Care

PoCT and Telehealth Monitoring

Dr. Christian Kloss

Instanbul, June 22, 2014

PoCT and Telehealth Monitoring (THM)

2

• Where PoCT can close gaps in THM?

• What works, where are major challenges?

• What are potential further uses?

Disclosures - Dr. Christian Kloss

3

• Medical DirectorEurope and Middle East

of Alere

• Managing Director ofGesellschaft für Patientenhilfe

(GPH),

an Alere subsidiary

Dr. Christian Kloss

Luise-Ullrich-Str. 4

D-82031 Grünwald

Email: [email protected]

Alere empowers individuals to take greater

control of their health under the supervision

of their healthcare providers

4

Patient

Health Data

Exchange

Data

Interpretation-

Validated

Algorithms

Behavioral

Change-

Coaching &

Connectivity

Ability to

Improve

Health

Outcomes

Biometric

Devices

(e.g., PoCT)

Telehealth Monitoring?

5

Telehealth (TH)1

The remote exchange of data

between a patient and health

care professional(s) to assist in

the diagnosis and management

of a health care condition(s)

One of several application:

Telehealth Monitoring (THM)

Use of TH to monitor the

disease and/or its management

eHealth

Telemonitoring

Telehealth

Telecare

mHealth

Telemedicine

1 Prof. Stanton P. Newman, City University London, Continua Alliance April 2013

Telehomecare

Remote monitoringTelecoaching

How can PoCT be used for THM?

6

Three conditions to use a marker (signs & symptoms,

physiological parameters or PoCT) for THM:

1. A marker useful to monitor (i.e., obtainable and

predictive)

2. A more appropriate monitoring frequency can be

reached by additional testing (typcially at home)

3. The patient (or person performing the test) needs

support in interpreting results and adjusting the

management accordingly

Some PoCT emerge as useful for THM

7

Tester adjusts

management

himself (PSM)

Tester needs

assistance (PST)

PoCT1

Clinical

(home-)monitoring

(incl. physiological)

CHF Signs & symptoms

Weight, fluids

BNP

Signs & symptoms

Activity, diet

Blood glucoseDM

COPD Signs & symptoms

SpO2? Spirometry?

Multiplex?

Anticoagulation

with VKAs

INR INRSigns & symptoms

Clinical

situation

Hypertension Signs & symptoms

Blood pressure

(Blood glucose)

Examples

Focus of this

presentation

1 Outside a professional setting

Activity + IR?

Elements in the monitoring system for selected clinical situations

PoCT Telehealth Monitoring inAnticoagulation with VKAs

8

Anticoagulation – one of the large health

care problems

9

• 6 million patients in Europe require long-term oral anticoagulation (LTOAC)1

Major indications: Atrial fibrillation, mechanical heart valves, DVT

• Main agents for LTOAC: VKAs and DOACs

• While VKAs are well established, challenge is the narrow therapeutic range

• Insufficient anticoagulation may lead to thromboembolic disease (e.g., ischemic stroke),

too strong anticoagulation may lead to bleeding (e.g., haemorrhagic stroke).

• DOACs claim a wider therapeutic range and hence a better safety profile

• Alternatively, the narrow therapeutic range of VKAs can be better managed by tighter

monitoring via PSM and PST, resulting in significant increase in efficacy and safety of

VKA treatment using PoCT (with Telehealth Monitoring in case of PST)

• INR: 2nd largest PoCT segment (after blood glucose)

1 Source: ISMAAP

Tighter control VKAs results in fewer adverse events

10Jones, M et al. Heart 2005;91:472-477

Hospital admission rate as a function of INR level

Typical

“Therapeutic

Range”

� Useful marker

to monitor

Suboptimal INR control of up to 1/3 of patients on VKAs

11Le Heuzey, J-Y et al., Thromb. Haemost. (2014), 111

Patients with inadequate INR control (target range 2.0-3.0)

Self-monitoring (using PoCT) increases the time in therapeutic range

12Heneghan, C et al. (2012) Lancet 367 (9508), S. 404–411

n = 6,417 (meta-analysis of individual patient data based on 21 trials)

� Higher monitoring

frequency gives

better control

Patient self-

testing

PST possible

(� THM)

Both, PSM and PST are established in regular care in several countries

13Alere

Selected PST/PSM

programs in regular care:

US:

Alere’s Home Monitoring

service (PST)

Germany:

PSM reimbursed

The Netherlands:

National Thrombosis

Service (PST/PSM)

Patients on VKAs

Cannot self-test Can self-test

Can self-

dose

Patient

self-

manage-

ment

PSM

possible

SoC

Cannot self-

dose

Alere runs a large INR Telehealth Monitoring program in the US

14Alere

Software

(CoagClinic)

• Central Control

• Disease Mgmt

• Decision Support

• Compliance

• INR & Dose

Tracking

INR Monitor

• POC incl. home

• Easy fingerstick

• 1 drop of blood

• Results in ~1 min.

• Onboard QC

Home Service

• Enrollment

• Training

• Supplies

• Reporting

• Support

[ ]

INR Visibility + Tighter Control = Patient SafetyUS: Alere’s Home

Monitoring service

• 10,000 clicians

• 450,000 patients

• 30 millions INR tests

Alere THM program workflow

15Alere

Patient:After initial face-2-face

training, performs home

INR test weekly

Patient IDProviders will identify

patients based on

suitability criteria

Patient AgreementRequires trainable,

consenting patient and

PST Rx from physician

Physician/Patient:Adjusts Patient medication

&/or diet based on results

received by Alere

Alere:Automatically relays result

to patient’s physician via:

• Phone/Fax/Email

• CoagClinic

• EMR Interface

Alere:Offers provider/payer

regular reporting on

program enrollment,

barriers, outcomes

Patient:Reports results via

MobileLink, web, or

phone to Alere

Optimizing

VKA

control

Significant effect of INR PSM/PST on thrombosis prevention (-49%)

16Heneghan, C et al. Lancet 367 (9508), S. 404–411. DOI: 10.1016/S0140-6736(06)68139-7.

HR for thrombosis patient self-monitoring vs. SoC

PSM and PST reduce thrombo-embolic events

17Garcia-Alamino, JM et al. Cochrane Review (2010)

HR for thrombembolic events PSM vs. PST

� Patient needs

support in reacting

STABLE Study: Real-world evidence for weekly INR measurement in THM

18DeSantis, G et al. Am J Manag Care. 2014;20(3):202-209

STABLE analyzed close to 30,000 patients



Significant effect of more frequent (weekly testing in all subgroups)

Better TTR than in SoC


Better INR control that SoC with weekly testing in all age groups


Weekly testing superior to less frequent testing


� Higher monitoring

frequency gives

better control

Why so far no broader adoption of INR THM (PST)?

24

Reimbursement

• Reimbursement pathways for Telehealth Monitoring do (yet) exist in many countries

• Budget for THM “between the chairs”: GP, specialist, hospital, biologist, G

� New reimbursement pathways often need to be established

CVs typical for lab analysers are often requested for serial PoCT

• THM is a complex intervention (systemic approach). Importance and contribution of

different parts of the CV (pre-analytical, analytical, intra-individual, inter-individual) and the

effect of serial testing have to be fully appreciated

• Some countries requiring same CV for home and lab testing

� New paradigm needed for analytical quality in serial home testing/THM

Consistent understanding of required QC/QA for home PoCT

• Although built-in QC is often established, this is not consistently recognized as sufficient

• Connectivity allows new options (including central lab QC, even at home)

� Appropriate QC/QA needs to be consistently defined

Example: INR PST

PoCT Telehealth Monitoring in

Chronic Heart Failure

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The heart failure epidemic

26

1 López-Sendón J Medicographia. 2011;33:363-369

2 Cowie MR, Medicographia. 2011;33:370-376

• Prevalence estimated at 2-3% of adult population, increasing with age1

• 26 million patients with heart failure around the world (6.5 in Europe) 1

• 6-9 fold increase in sudden death vs. normal population1

• Prognosis worse than many cancers (>50% mortality within 4 years) 1

• HF accounts for ca. 5% of total internal

medicine hospitalizaiton2

• Around ~USD 25 billion direct costs of

heart failure in US (2010), est. USD 78b

in 2030)1

• 1-2% of the national health budget

spend on HF (60% thereof in hospital)2

• THM aims at improving risk factors and

early detections of imminent

decompensations

CHF is heavily by multiple factors

27

Smoking

Lack of activity

Misnutrition

Age

Genetics

Hypertension

Overweight

Lipometa-

bolic

Disorder

Diabetes

CHD

Renal

Failure

Depression

Chronic

Heart-

failure

Current THM aims at preventing (re)admission using signs, symptoms and weight changes for monitoring

28

Kg

Days

(no excess

body fluid)

Decompensated heart failure

Onset

Intervention

still possible

Emergency

(Pulmonary edema)

5-14

2-3

Stable heart failure

Worsening

shortness of breath,

edema

Visible edema

(ca. +6kg)

� Promote self-management

Address preventable influencers, e.g.:

• Disease awareness

• Diet

• Exercise

• Smoking

� Telecoaching

� Detect decompensations early

Regular monitoring of

• Clinical signs & symptoms

• Weight

Rule-based decision engine to filter

alerts

� Telemonitoring

• Strong outcomes:

• Increase & stabilization of

Quality of Live

• Mortality ca. -50% in year 1

• Hospitalizations -20 to -40%

• Programs in Germany and

France (others in preparation)

Connecting with care team

and patient

Monitoring and early

intervention in case of

imminent decompensations

Prevention via self-

empowerment/education

• Founded 2005 in Germany

• Flagship program CORDIVA:

Currently serving > 11,000

patients with chronic heart

failure

• Three principles:

GPH: a leading provider for CHF-HM in Europe

29

CHF-patient at home

Patient‘s physicians

CORDIVA service centre

�

�

�

��

�

�

CORDIVA improves quality of life

301 Quality of life Minnesota Living with Heart Failure questionnaire

Quality of life - total1 Quality of life - physically1 Quality of life: emotionally1

p < 0,03

15 pts

improvement

p < 0,03

6 pts

improvement

3 pts

improvement

p < 0,03

Current CORDIVA halves mortality in year one

311 n = 7,684 and 5,759

Mortality per quarter in percent, CORDIVA compared with control1

Cardiovascular hospitalizations are reduced

321 n = 281

Hospitalizations per year in CORDIVA group

0,61 0,620,51

0,84

0,42

0,30

Year before 1st year 2nd year

Cardiovascular Other causes

1.45

1.04

0.81

Further Improvement

potentials:

1. Ca. 1/3 of

cardiovascular

hospitalizations could

be avoided in addition

2. To obtain these results

a strong involvement

of the service centre

(trained nurses) is

needed

-44% all-cause hospitalizations

-64% cardiovascular hosp.

The predictive values of clinical parameters on its own for acute heart failure are moderate

33Dao Q et al. J Am Coll Cardiol. 2001;37:379-385.; Zhang, J et al. (2009) Eur. J. Heart Fail. 11 (4), S. 420–427

Variable Sensitivity (%) Specificity (%) Accuracy (%)

Hx of HF 62 94 80

Dyspnea 56 53 54

Orthopnea 47 88 72

Rales 56 80 70

S3 20 99 66

JVD 39 94 72

Edema 67 68 68

Weight 65 33 -

� The combination of these markers give better predictive values

BNP is an established parameter for several clinical questions in CHF management

34Maisel AS et al. NEJM 2002; 347 (3): 161-167; Maisel, AS et al., (2008) Eur J Heart Fail 10 (9), pp. 824–839

ROC Diagnosis Breathing not Properly

Some uses for BNP in CHF

� Diagnosis (within most

guidelines)

� Prognosis (risk stratification

and readmissions)

� Screening

± BNP-guided therapy

(medication titration)

? THM

Already 2008 first algorithms for THM using BNP have been suggested

35Maisel AS et al., European Journal of Heart Failure 10 (2008) 824–839

The development of Alere’s Heart Check enables monitoring at home

36Maisel et al. (2013) JACC Vol. 61, No. 16,

Alere Heart Check

METER

Connectivity

• GPRS – server-based

connectivity

• Blue Tooth – local peripheral

connectivity (i.e. weight

scale)

Power

• AC & rechargeable battery

Interface

• Easy to use screen and

fingerstick access

TEST STRIP

BNP range:10-5,000 pg/ml

Stability: 12 months

Opt. Conditions: 18-34° C

Blood Volume: 12 µL

HABIT Trial aimed at evaluating the feasibility of using BNP for THM


BNP and weight were measured for prediction

38

Maisel et al. (2013) JACC Vol. 61, No. 16,

* Unevaluable subjects collected < 5 BNP results during the monitoring period (N=20), or did not monitor weight (N=4).

• Study enrolled 163 evaluable patients (out of 187 total*)

with HF signs and symptoms of ADHF discharged from

the hospital or in an outpatient setting.

• Measured weight and BNP levels daily for 60 days with a

finger-stick test.

• Patients and physicians were blinded to BNP levels.

• The composite outcome was ADHF events:

cardiovascular death, admission for decompensated HF,

or clinical HF decompensation requiring either parenteral

HF therapy or changes in oral HF medications.

HABIT: Demographics in line with SoC


Variable N (%), or Median (IQR)

Number of Subjects 163 (100.0)

Enrolled after Hospital Discharge 63 (38.7)

Gender M 142 (87.1)

NYHA I 5 (3.1)

NYHA II 56 (34.4)

NYHA III 67 (41.1)

NYHA IV 6 (3.7)

LVEF > 40% 49 (30.1)

LVEF percent 30 (20, 45)

Age (median, IQR) 63 (56, 70)

BMI 30 (26, 34)

Initial Weight (lb) 206 (173, 235)

Initial BNP (pg/ml) 431 (202, 915)

HABIT: History and medication at enrollment


Medication N (%)

ACE inhibitor 86 (52.8)

Aldosterone Antagonist 68 (41.7)

Digoxin 46 (28.2)

ARB 39 (23.9)

Beta Blocker 141 (86.5)

Diuretic 146 (89.6)

Antiarrhythmic 12 (7.4)

Monitoring Parameter Median (IQR)

Monitoring Period (Days) 65 (59, 69)

Observation Period (Days) 76 (63, 81)

# of BNPs per Patient 46 (33, 54)

# of Weights per Patient 53 (43, 58)

Medical History N (%)

Hypertension 122 (74.8)

Hyperlipidemia 109 (66.9)

Arrhythmia 100 (61.3)

CAD 84 (51.5)

Cardiac

Intervention80 (49.1)

AFIB 76 (46.6)

Pacemaker/ICD 63 (38.7)

Angina 57 (35.0)

CKD 56 (34.4)

MI 54 (33.1)

Diabetes (IR) 43 (26.4)

Diabetes (NIR) 42 (25.8)

Valvular Heart

Disease33 (20.2)

HABIT recorded 56 ADHF events


ADHF Events within the Monitoring Period (Primary Outcome)

N(Events) N(Patients) Percent Count

0 123 75.46 0

1 28 17.18 28

2 10 6.14 20

3 1 0.61 3

5 1 0.61 5

ADHF* 40 24.54 56

*Includes 22 ADHF Hospitalizations and 1 CV death.

Example 1: BNP & weight gain, hospitali-zation for ADHF


400

600

800

1000

1200

BN

P (

pg

/ml)

PID=0060-0006, Age=83, Gender=M, NYHA=III, LVEF=62

0 10 20 30 40 50 60 70

SB

SW

WG

Sy

mp

tom

s

Day

-15

-10

-5

0

5

10

15

We

igh

t-M

ea

n (

lb)

Example 2: Rising BNP, no weight gain, hospitalization


1000

1500

2000

2500

3000

3500

4000

4500

5000

BN

P (

pg

/ml)

PID=0060-0008, Age=76, Gender=M, NYHA=II, LVEF=24

0 10 20 30 40 50 60 70

SB

SW

WG

Sy

mp

tom

s

Day

-15

-10

-5

0

5

10

15

We

igh

t-M

ea

n (

lb)

100

200

300

400

500

600

700

800

900

BN

P (

pg

/ml)

PID=0072-0008, Age=84, Gender=F, NYHA=III, LVEF=55

0 10 20 30 40 50 60 70

SB

SW

WG

Sy

mp

tom

s

Day

-15

-10

-5

0

5

10

15

We

igh

t-M

ea

n (

lb)

Example 3: HFpEF, no ADHF, periodic BNP excursions not correlated with weight


Example 4: HFrEF, falling BNP, no ADHFs


200

400

600

800

1000

BN

P (

pg

/ml)

PID=0071-0012, Age=60, Gender=F, NYHA=II, LVEF=20

0 10 20 30 40 50 60 70

SB

SW

WG

Sy

mp

tom

s

Day

-15

-10

-5

0

5

10

15

We

igh

t-M

ea

n (

lb)

Daily BNP explained risk in addition to the clinical signs & symptoms


Coefficient and HR, poisson regression used to predict ADHF

The daily hazard model yields a moderately-weak AUC


0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1-SPECIFICITY

SENSITIV

ITY

Patient Days w/o ADHF vs. Days with ADHF (Count 9979 vs. 56)

Daily Hazard Model

1.0% risk per day

0.75% risk per day

0.25% risk per day

0.50% risk per day

As expected, serially measured BNP showed strong autocorrelation


Paired BNP test results for all patients at all time t vs. shifted time t+τ:

101

102

103

101

102

103

BNP(t)

BNP(t+1)

N=5525, Spearman R=0.936

101

102

103

101

102

103

BNP(t)

BNP(t+14)

N=4066, Spearman R=0.865

Time Shift 1 Day Time Shift 14 Days

Habit dispersion grows over time: CVi was much larger than CVa


0 5 10 15 20 25 30 35 40 45 500

10

20

30

40

50

60

70

80

Time Between Measures (days)

Dis

pers

ion (

Perc

en

t)

Dispersion of BNP Measures vs. Time Between Measures • The analytical CV (CVa,

10.4% to 14.6%,

depending on the BNP

concentration) is only a

small proportion of the

dispersion between

measurements

• Intra-individual CV (CVi)

as a function of time

between measurements:

1 day 20.7%

2 days 24.6%

3 days 28.5%

14 days 35.6%

42 days 48.9%

Episodes of rising BNP are associated with a clear risk increase


Conclusions from HABIT

51

• Home measurement of BNP is feasible and safe

• BNP is very dynamic (high CVi)

• Daily BNP patterns following treatment for ADHF are rich in information that

is as diverse and heterogeneous as the patients and their heart disease

• The widely fluctuating patterns may identify those patients who are not

optimized and therefore require tight observation and management

• Traditional events defined as hospital readmissions and outpatient clinic

visits with therapeutic intervention may not truly represent all instances of

acute decompensation and worsening conditions requiring therapeutic

intervention

• HFpEF and HFrEF seem to have different predictive profiles (daily BNP and

weight; paper submitted)

�HABIT is the basis for further validation of a THM algorithm

(incl. paradigm for the serial testing)

The French HELP Study (Prof. Jourdain) adds BNP in THM as a third arm to HM

52

What needs to be done for full adoption?

53

• Fully understand use for prediction in serial monitoring across the

different disease sub-groups

• Establish reimbursement in new pathways

• Define necessary CVs in view of the complex, systemic intervention

with serial measurements at home

• Define appropriate QC/QA system

• Further improve system with new markers and fully address

comorbid patient

Potential further uses of

PoCT Telehealth Monitoring

54

Potential further uses of PoCT in THM

55

Area Potential approach

Individual patient based („traditional THM“)

(Pre) Diabetes Insuline resistance ? + Activity programs ?

COPD Colonisation ?

Multiplex / infection monitoring ?

Asthma FeNO ?

CHF BNP-guided therapy ?

Multimorbid patient Patient-centric multimarker program?

Population based („broader definition THM“)

HIV CD4 monitoring (PIMA, Alere q)

Influenza Isothermal amplification (Alere i)

Population screening

(Cardiometabolic profile)

Multimarker (incl. lipids, HbA1C)

Examples

Documents

140622 IFCC-Worldlab PoCT and Telehealth Monitoring ... KLOSS_IFCC Worl… · Use of TH to monitor the disease and/or its management eHealth Telemonitoring Telehealth Telecare mHealth