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
19DeSantis, G et al. Am J Manag Care. 2014;20(3):202-209
20DeSantis, G et al. Am J Manag Care. 2014;20(3):202-209
Significant effect of more frequent (weekly testing in all subgroups)
Better TTR than in SoC
21DeSantis, G et al. Am J Manag Care. 2014;20(3):202-209
Better INR control that SoC with weekly testing in all age groups
22DeSantis, G et al. Am J Manag Care. 2014;20(3):202-209
Weekly testing superior to less frequent testing
23DeSantis, G et al. Am J Manag Care. 2014;20(3):202-209
� 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
25
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
37Maisel et al. (2013) JACC Vol. 61, No. 16,
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
39Maisel et al. (2013) JACC Vol. 61, No. 16,
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
40Maisel et al. (2013) JACC Vol. 61, No. 16,
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
41Maisel et al. (2013) JACC Vol. 61, No. 16,
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
42Maisel et al. (2013) JACC Vol. 61, No. 16,
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
43Maisel et al. (2013) JACC Vol. 61, No. 16,
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
44Maisel et al. (2013) JACC Vol. 61, No. 16,
Example 4: HFrEF, falling BNP, no ADHFs
45Maisel et al. (2013) JACC Vol. 61, No. 16,
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
46Maisel et al. (2013) JACC Vol. 61, No. 16,
Coefficient and HR, poisson regression used to predict ADHF
The daily hazard model yields a moderately-weak AUC
47Maisel et al. (2013) JACC Vol. 61, No. 16,
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
48Maisel et al. (2013) JACC Vol. 61, No. 16,
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
49Maisel et al. (2013) JACC Vol. 61, No. 16,
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
50Maisel et al. (2013) JACC Vol. 61, No. 16,
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