Telemonitoring in heart failure: Big Brother watching over you

R. Dierckx • P. Pellicori • J. G. F. Cleland •

A. L. Clark

� Springer Science+Business Media New York 2014

Abstract Heart failure (HF) is a leading cause of hospi-

talisations in older people. Several strategies, supported by

novel technologies, are now available to monitor patients’

health from a distance. Although studies have suggested

that remote monitoring may reduce HF hospitalisations and

mortality, the study of different patient populations, the use

of different monitoring technologies and the use of dif-

ferent endpoints limit the generalisability of the results of

the clinical trials reported, so far. In this review, we discuss

the existing home monitoring modalities, relevant trials

and focus on future directions for telemonitoring.

Keywords Telemonitoring � Heart failure � Review �Structured telephone support � Implantable haemodynamic

monitoring devices

Introduction

Heart failure (HF) is a leading cause of hospitalisations in

people aged[ 65 years and is as malignant as some common

types of cancer [1]. Approximately 1–2 % of adults have HF,

rising to more than 10 % of those over the age of 70 years [2].

In Europe, 24 % of patients are readmitted within 12 weeks of

discharge [3]. According to several projection models, ageing

of the population and improved survival from acute coronary

events—due to better primary and secondary prevention—

will lead to an increase in the prevalence of HF and the

number of HF hospitalisations by[20 % in the next 20 years

[4, 5]. The annual cost of HF is estimated to be about 2 % of

the total health-care budget in Europe and North America.

Most of the costs is staff-related (doctors, nurses, technicians,

porters and managers), with hospitalisations provoking

intense health-care resource utilisation [6]. With the increase

in prevalence and incidence of heart failure, the cost of caring

for patients is very likely to rise, making alternative strategies

that might avoid recurrent hospitalisations attractive.

One possible approach is to use technology to monitor

patients remotely. As up to two-thirds of hospital admis-

sions might be prevented by improved management [7],

monitoring vital signs and providing online and on-demand

educational programmes from a distance is an appealing

strategy. Depending on the type of system available,

symptoms, weight, blood pressure, heart rate, ECG trac-

ings, oxygen saturations, haemodynamic pressure readings,

thoracic impedance and/or device diagnostic variables can

be tracked from afar.

Remote monitoring of HF patients can be divided into two

formats: structured telephone support (STS) and telemonitor-

ing (TM). STS involves regular telephone contact with patients,

usually by specialist nurses, to assess symptoms and compli-

ance and provide ongoing education. The contacts may or may

not include the transfer of physiological data (such as weight).

Patients can be called at fixed intervals (e.g. every 2 weeks or

monthly) and/or can be advised to contact the HF specialist

nurse in case of worsening of symptoms or disease-related

questions, during working hours. It has the advantage of pro-

viding a personal approach to the patient but may also increase

dependence and is human resource intensive and may not be

cost-effective. TM, on the other hand, is the (digital, broadband,

satellite, wireless or Bluetooth) transmission of physiological

R. Dierckx (&) � P. Pellicori � A. L. Clark

Department of Cardiology, Hull York Medical School, Hull and

East Yorkshire Medical Research and Teaching Centre, Castle

Hill Hospital, Cottingham, Kingston upon Hull HU16 5JQ, UK

e-mail: r.dierckx@hull.ac.uk

J. G. F. Cleland

National Heart and Lung Institute, Royal Brompton and

Harefield Hospitals, Imperial College, London, UK

123

Heart Fail Rev

DOI 10.1007/s10741-014-9449-4

data, e.g. blood pressure, heart rate, weight, electrocardiogram

and/or pulse oximetry. Patients are usually asked to make

measurements as part of their daily routine to ensure that

treatment is optimised and to allow individual patient profiles to

be developed that allow earlier detection of deviation from their

personal norm. Data are transferred to a secure web server,

which can be accessed by members of the telemedical and/or

HF team. Out-of-range values trigger an alert and prompt

health-care personnel to contact the patient and take action,

either directly by short-term advice (e.g. uptitration of diuret-

ics), or indirectly by referring the patient to the general prac-

titioner or cardiologist if long-term changes in therapy are

required. Smart systems increasingly involve the patient and

their informal carers as part of the health management team.

Trend charts, treatment reminders and refresher educational

courses can all be provided without intervention from health

professionals. A good programme should empower patients

and minimise the need for health professional involvement,

thereby improving both care and efficiency. Of course, a poorly

devised system will do none of these things.

Remote monitoring reduces emergency department/

urgent in-office visits, HF hospitalisations and mortality.

The mechanisms by which these results are achieved are

not well understood, but may include improved self-care by

increasing patient’s knowledge and compliance with

advice and medication through education and monitoring,

increased prescription of guideline-based medication by

clinicians, or earlier intervention. It might simply be that

patients find the presence of telemonitoring comforting and

that it reduces their need for formal contact with health-

care providers. Although outcome data are conflicting, the

bulk of evidence points towards a substantial benefit on

mortality and a more modest effect on hospitalisation [8–

15]. Of course, timely and appropriate hospitalisation may

be life-saving and one of the mechanisms by which te-

lemonitoring exerts benefit. The efficacy of TM may have

been underestimated because of the artificial constraints

inherent in conducting a randomised controlled trial (RCT)

of one system of care against another. It is rather difficult to

get all the advantages of service redesign when, by design,

many patients will be excluded from or assigned not to

receive it. However, some important questions remain

unanswered: who is most likely to benefit from remote

monitoring? What type of technology should be preferred?

Which data should we monitor, and how?

Remote monitoring technologies

Structured telephone support (STS)

To date, the DIAL trial is the largest multicenter RCT

comparing STS with usual care. The study population

consisted of 1,518 stable chronic HF patients, with only

37 % having experienced a previous HF hospitalisation

[16]. Patients were initially called every 14 days, and after

the fourth call, the frequency could be adjusted according

to case severity and compliance. The aim was to improve

adherence to medication and diet, promote exercise and

regularly monitor symptoms. Nurses were allowed to

change diuretic dose but not to uptitrate other HF medi-

cation. The primary endpoint was the rate of all-cause

mortality or HF hospitalisations. After a mean follow-up of

16 months, STS reduced the risk of the primary outcome

(all-cause mortality or hospitalisation for HF) by 20 %

(p 0.02), which was mainly driven by a reduction in HF

hospitalisations (RRR 29 %, p 0.005). The Kaplan–Meier

curves diverged within the first 3 months of follow-up.

Although prescription patterns were similar for patients in

the two groups, those in the intervention arm were more

likely to take the medication as prescribed. The benefits

appeared to be sustained for up to 3 years after the inter-

vention had stopped (RRR 12 %, p 0.05) [17].

The finding that STS mainly reduces HF hospitalisa-

tions—rather than mortality—was confirmed by a

Cochrane review, including 16 RCTs investigating the

effects of STS in 5,613 participants [10]. With the use of

simple telephone technology, the proportion of patients

hospitalised due to HF was reduced by 23 % (RR 0.77

(95 % confidence interval [CI] 0.68–0.87), p \ 0.0001).

However, the results of the review have been challenged

by the Tele-HF study [18]. In Tele-HF, 1,653 subjects

who had recently been discharged from hospital after an

episode of worsening HF were randomised to support

from a telephone-based interactive voice-response system

or usual care. Patients were instructed to make daily

calls and answer a series of automated questions using

their telephone keypad. After 6-month follow-up, there

was no difference in the primary composite endpoint of

death or all-cause hospitalisation between the two groups

(51.5 % for usual care vs. 52.3 % for TM). Moreover,

there were no reductions in the risk of hospitalisation for

HF, the number of days in hospital or the time to

readmission or death. However, few patients complied

with the system; 14 % of patients never used the system;

and only 55 % of patients were using the system more

than 3 times per week by the end of the trial. This study

should be seen as a failure of voice-interactive technol-

ogy, which will come as no surprise to many, and the

service by which it was delivered rather than as a failure

of the concept of STS.

Another recent study, investigating the effects of a

telephone health coaching service in a community popu-

lation of 2,698 patients with chronic illnesses [diabetes,

chronic obstructive pulmonary disease (COPD), heart

failure or coronary artery disease], reported a significant

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increase in the number of emergency admissions (by

13.6 %), outpatients visits and costs in the intervention

group. However, only a minority of the study participants

had a diagnosis of HF (n = 154) and no details were

reported on reasons for admissions and outpatient visits

[19]. This may reflect the effect of over-cautious, risk-

averse, health professionals increasing patient anxiety.

Alternatively, appropriate expert review and timely hos-

pitalisation can be life-saving. For many medical condi-

tions, there is little evidence that interventions alter

outcome; this is not the case for heart failure.

Non-invasive monitoring with home-based portable

technology

One of the first RCTs highlighting the potential benefits

of TM was the TEN-HMS trial in which 426 patients

(48 % aged [ 70 years) with a reduced left ventricular

ejection fraction, recently discharged after an episode of

worsening HF, were randomised to usual care, nurse

telephone support or TM [11]. TM included the twice

daily measurement of weight, blood pressure, heart rate

and rhythm. Both nurse support and TM reduced 1-year

mortality rates by 36 % compared to usual care but had

little impact on hospitalisation. TM may simply be inef-

fective in reducing hospitalisation. Alternatively,

improved survival means that more patients are alive and

at risk of hospitalisation. However, as noted above, it is

also possible that timely hospitalisation contributed to the

reduction in mortality. With TM, hospitalisations were

substantially shorter, implying either admission of milder

cases or earlier discharge because TM acted as a post-

discharge ‘safety net’ that increased the confidence of

staff to discharge the patient. Several other studies have

confirmed the benefit of TM [8, 14, 20–22]. However, it

is important to note that in the TEN-HMS trial, patient

contacts—other than hospitalisation (e.g. emergency room

visits, office visits, home visits by specialist nurses)—

were three times higher in the STS and TM group,

compared to the control group. For STS, this involved

mainly costly face-to-face contacts, but for TM, the

majority of support was provided by telephone.

In the Cochrane meta-analysis [10], which included

8,323 participants from 25 peer-reviewed RCTs (11 of TM

and 16 of STS), patients assigned to TM had lower all-

cause mortality (RR 0.66, 95 % CI 0.54–0.81, p \ 0.0001)

and fewer HF-related hospitalisations (RR 0.79, 95 % CI

0.67–0.94, p 0.008). However, there are limitations to the

review, as with most meta-analyses, relating to the meth-

odological quality of individual studies, heterogeneity and

potential publication bias [23].

The growing enthusiasm for TM was dampened by the

publication of the TIM-HF trial in which 710 well-treated,

stable NYHA II/III HF patients were randomised to phy-

sician-led remote monitoring or usual care [24]. Patients

were provided with devices for ECG, blood pressure and

body weight measurements and transmitted their daily

measurements using mobile phone technology. After a

median follow-up of 26 months, there was no effect of the

TM system on all-cause mortality or hospitalisation rate.

Unlike the population in the Tele-HF trial, adherence in

TIM-HF was excellent with 81 % of patients completing

more than 70 % of daily transmissions. The overall event

rate was dramatically lower than in the TEN-HMS study

that had enroled patients shortly after hospitalisation

(mortality rate of respectively 8.4 and 8.7 % per 100 per-

son-years in the HTM and UC group in TIM-HF compared

to 23 and 38 % in TEN-HMS). TM by itself cannot

improve patient outcomes but exerts its benefits by

improving the delivery of interventions that modify out-

come. It is not surprising that TM has little impact on

outcome in stable patients already receiving expert treat-

ment. The whole point of TM is to ensure that good care is

reliably, efficiently and cost-effectively delivered. Instead

of a nurse looking after 50 patients, it enables them to

manage 200. The costs of technology are lower than the

costs of staff.

The most recent trial contributing to the debate is the

Whole System Demonstrator (WSD) trial, a cluster

randomised trial that explored the effects of telehealth in

a population of patients with diabetes, COPD or HF,

recruited from 179 general practitioner sites in England

[12]. Cluster randomisation, in which a whole centre is

randomly assigned to deliver one or other strategy,

enhances the possibility of leveraging the benefits of

service redesign. In WSD, 3,230 participants were ran-

domised to TM or usual care. Different monitoring

technologies were used, but, in patients with HF, the

system always included a weighing scale. In addition,

symptom questions and educational messages were sent

to the patients either via the telehealth base unit or via a

set-top box connected to a television. After 12 months of

follow-up, fewer patients in the intervention group were

admitted (odds ratio 0.82, 95 % CI 0.70–0.97,

p = 0.017). Mortality at 12 months was also reduced by

TM (4.6 vs. 8.3 %; odds ratio 0.54, 0.39–0.75,

p \ 0.001). The study included patients with diabetes

(low annual mortality, low annual risk of hospitalisa-

tion), COPD (low annual mortality but substantial risk of

hospitalisation) and heart failure (high annual risk of

death and of hospitalisation). Accordingly, the modest,

absolute, overall reduction in mortality observed may

conceal a substantial reduction in mortality amongst

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patients with heart failure. Further analyses to confirm

this are awaited.

Invasive monitoring: implantable haemodynamic

monitors

Right ventricular pressure sensors

It is often thought that a common reason for HF hospital-

isations is a rise in left atrial pressures (LAPs) leading to

pulmonary congestion. Therapy tailored to invasively

measured haemodynamic variables thus seems an appeal-

ing and straightforward management strategy [25, 26].

There is a considerable amount of evidence to suggest that

intracardiac and pulmonary pressures start to rise several

days (and sometimes several weeks) before HF deteriorates

clinically, suggesting that early intervention targeting these

pressures might reduce the risk of hospital admission [27].

Another argument in favour of an invasive approach is the

limited sensitivity of changes in symptoms or weight gain

in predicting HF events [28, 29]. However, invasive hae-

modynamic monitoring also provides a method of

deploying a ‘health maintenance’ strategy rather than

merely ‘crisis management’. Although trends in monitoring

may spot patients who are deteriorating, thus triggering

intervention, there is a major flaw in this strategy; the

number of false-positive alerts always vastly exceeds the

true positive. It is a failed strategy as the primary approach

to delivering care. The alternative strategy is not worry

about whether a result is abnormal or not but always advise

treatment to hold or bring the patients results into the

desired range. This has far greater potential to modify the

natural history of the disease rather than waiting to the

point of crisis before acting.

In the COMPASS-HF trial, a Chronicle device (Med-

tronic Inc., Minneapolis, Minnesota) was implanted in 274

NYHA III and IV HF patients, regardless of ejection

fraction (EF) (70 patients had an EF C 50 %) [30, 31].

This implantable haemodynamic monitor (IHM) consists of

a pulse generator and pacemaker lead with a pressure

sensor located at the tip implanted in the right ventricular

(RV) outflow tract, allowing measurement of systolic and

diastolic RV pressures, and estimated diastolic pulmonary

artery pressure (PAP). Many clinicians were reluctant to

implant such large devices (the lead was 58 cm long and

the pressure sensor had a diameter of 3.7 mm) that had no

direct therapeutic benefit but might have been interested

had the technology been incorporated into a defibrillator or

pacing device for patients who required these therapies

[32]. Patients were randomised to either a treatment or a

control group, with clinicians having access to haemody-

namic data in the treatment group only. At the end of the

study period (6-month follow-up), there was a 21 % non-

significant reduction in the number of HF events in the

treatment group. However, the trial results did not meet the

primary endpoint and the Food and Drug Administration

voted against the approval of the Chronicle device [33].

Pulmonary artery pressure sensors

A next-generation IHM was investigated in the CHAM-

PION trial [13]. In 550 patients with stable (i.e.: no recent

hospitalisation) but advanced HF, again independent of EF,

a wireless pressure sensor (CardioMEMS, CardioMEMS

Inc., Atlanta, USA) was inserted into the distal pulmonary

artery, allowing intermittent measurement of pressures by a

wireless radiofrequency system that both powers and

interrogates the device. After implantation, patients were

assigned to a treatment group (n = 270), in which clini-

cians had daily access to the pressure readings or to a

control group (n = 280) in which such access was blocked.

Patients were blind to which group they had been allocated

to but the physician only received data for the treatment

group and was therefore not blind to allocation. The

treatment goal was to reduce raised PAP using diuretics,

vasodilators or neurohormonal antagonists, a ‘health

maintenance’ rather than ‘crisis management’ strategy.

After 6 months, fewer patients in the treatment group had

been hospitalised for HF, compared to the control group

(hazard ratio [HR] 0.72, 95 % CI 0.60–0.85, p = 0.0002).

After 15 months, the difference was even more pronounced

with a 37 % reduction in HF-related hospitalisations (HR

0.63, 95 % CI 0.52–0.77; p \ 0.0001). Interestingly, use of

the IHM was similarly beneficial in patients with ‘normal’

(EF C 40 %, n = 119) and reduced (n = 431) EF. The

main agents used to reduce PAP were diuretics and nitrates

(76 % of all medication changes).

It is not the measurements themselves that improve

outcome, of course. In both COMPASS-HF and CHAM-

PION, patients in the intervention limb were on higher

doses of medication to treat heart failure. In the CHAM-

PION trial, the mean number of treatment changes in the

active group was 9.1 per patient, compared to 3.8 per

patient in the control group (p \ 0.0001). The CardioM-

EMS device was safe with a 98.6 % freedom from device-

related or system-related complications. Despite the

promising results, the FDA voted against approval of the

device in 2011 because of concerns about preferential

support to patients in the treatment group and potential bias

in analysing the efficacy of the device [34]. In October

2013, a second panel review yielded a (narrowly) favour-

able conclusion with an 11–0 vote that the device is safe, a

7–4 vote that it is not effective and a 6–4–1 vote that the

benefits outweigh the potential risks of the device [35]. The

FDA recently approved the device.

Heart Fail Rev

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Left atrial pressure sensors

In the observational, first-in-man HOMEOSTASIS trial

[36], a LAP monitoring system (HeartPOD, St Jude Med-

ical Inc., Minneapolis, Minnesota), consisting of a sensor

lead and a subcutaneous antenna, was implanted in 40

patients with moderate or severe symptoms and a previous

HF hospitalisation. LAP data were obtained by placing a

handheld patient advisor module (PAM) over the subcu-

taneous antenna. Patients were instructed to perform

measurements twice daily. During the first 3 months,

patients and clinicians were blinded to LAP readings, and

treatment was based on clinical assessment of HF status.

After the observational period, the PAM was set to display

LAP results and to inform patients directly about how

much diuretic they should take. Importantly, the patient

interacted directly with the device without the intercession

of nurse or doctor. LAP-guided therapy reduced the num-

ber of HF hospitalisations between the observation and

titration period by 59 % (p 0.04).

As in the CHAMPION trial, more rigorous uptitration of

diuretics, vasodilators and neurohormonal antagonists was

presumably the mechanism behind the improvement in

outcomes. Although most patients were already treated

with BB and ACE-I/angiotensin receptor blockers (ARBs),

only 27 % received target doses at baseline. At the end of

the trial, the proportion increased to 54 %. However, the

small sample size, lack of a randomised design and ‘before

versus after’ comparisons limit interpretation. Results of

the randomised controlled LAPTOP-HF trial using the

same device are eagerly awaited.

Device diagnostic monitoring

Some implantable cardiac resynchronisation therapy

(CRT) devices and internal cardioverter defibrillators

(ICDs) act as a continuous monitoring tool and can pro-

vide HF-related diagnostic information. These ‘device

diagnostic variables’ include heart rate (both day and

night), heart rate variability (HRV), patient daily activity,

atrial and ventricular tachyarrhythmia frequency and

duration, thoracic impedance measurement, percentage

(biventricular) pacing, lead impedance and battery life.

Patients can transmit device data—wirelessly or manu-

ally—over a phone line (standard or cellular) to a secure

server, which can then be accessed by clinicians by log-

ging into a password-protected website. Monitoring of

autonomic variables [37], atrial fibrillation (AF) burden

and rate control information [38], patient activity [39] and

intra-thoracic impedance [40]—an indicator of intra-tho-

racic fluid status—have all been proposed as tools to

predict HF events. Some manufacturers have integrated

device diagnostics with measurements of blood pressure

and weight to provide a more comprehensive management

system.

Intra-thoracic impedance can be measured by applying

an electrical impulse between the pulse generator of the

device and the tip of the RV lead. Theoretically, impedance

decreases as intrathoracic fluid accumulates. Falls in intra-

thoracic impedance might thus be used as a surrogate for

pulmonary congestion.

In a small proof-of-concept study, Yu et al. [40] dem-

onstrated that intra-thoracic impedance was inversely cor-

related with pulmonary capillary wedge pressure and

started to decrease around 15 days before the patient

reported worsening of symptoms. The sensitivity for pre-

dicting HF hospitalisations was 77 %. The European In-

Sync Sentry observational study, involving 373 patients,

found a sensitivity and positive predictive value of 60 %

for a fluid index[60 (based on impedance measurements)

in predicting clinical HF deterioration [41]. However,

results from the larger SENSE-HF trial (n = 501) were

disappointing and found that at best, the sensitivity and

positive predictive valve of invasive impedance monitoring

in predicting heart failure deterioration were 42 and 38 %,

respectively [42, 43].

The DOT-HF trial investigated the clinical utility of

ambulatory thoracic impedance monitoring in HF patients

with an ICD ± CRT. Study participants were randomised

to have information available to physicians and patients as

an audible alert in case of threshold crossings (access arm)

or not (control arm) [44]. Integration of the algorithm into

daily clinical practice did not show any reduction in the

primary endpoint of all-cause mortality and HF hospitali-

sations (HR 1.52, 95 % CI 0.97–2.37, p = 0.06). More-

over, the number of outpatient clinic visits and HF

hospitalisations was higher in the access arm compared to

the control arm, probably due to false-positive alerts or

over-reaction to true positive alerts that could have been

managed less urgently. Further investigation is ongoing in

the OptiLink-HF trial, where an OptiVol/CareLink� sys-

tem (Medtronic Inc., Minneapolis, Minnesota) is being

used to provide physicians with wireless alerts of threshold

deviations indicating worsening heart failure. The study is

expected to end in November 2014 [45].

Monitoring of single variables has a low sensitivity in

predicting clinical deterioration; combining multiple

variables might be more useful. In the PARTNERS-HF

trial (n = 694), an algorithm was developed based on AF

burden (duration of episodes and ventricular rate), high

fluid index (C60), low patient activity, abnormal auto-

nomic balance (high night-time heart rate or low heart rate

variability) or significant device therapy (decreased % of

CRT pacing or ICD shocks) [46]. The algorithm was

considered positive if a patient had either two abnormal

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criteria or a very high fluid index (C100) alone. Patients

who tested positive had a 5.5-fold risk of HF hospitali-

sation within the next month. These promising results

have recently been confirmed by Cowie et al. [47]. Based

on the same device diagnostic data used in the PART-

NERS-HF trial, the authors calculated a daily HF risk

score in a development data set (n = 921) and a valida-

tion data set (n = 1321). Patients who were in a high-risk

state on any day in the last 30 days were 10 times more

likely to be hospitalised for HF in the next 30 days

compared to patients who had a low risk score on each of

the last 30 days. However, this ‘crisis management’

approach should be seen as supplementary to a ‘health

maintenance’ strategy rather than the main goal of

monitoring.

Unresolved issues

The bulk of the data suggest that telehealth may be a useful

tool to keep patients out of the hospital and prolong sur-

vival. The effectiveness of TM will depend on patient

selection, the service supporting it and what it is being

compared to. TM is unlikely to improve the outcome of

stable, well-managed patients, especially if the service is

not well organised. It has been adopted enthusiastically by

some health-care providers in the expectation that savings

will inevitably accrue. However, many have been disap-

pointed, often because TM has been deployed as part of

care for stable patients in the community rather than as part

of discharge strategy for patients at high risk of readmis-

sion and death whose treatment often cannot be optimised

during their hospital stay.

Thokala et al. [48] performed decision analysis

modelling to examine the cost-effectiveness of different

remote monitoring technologies (STS with human–

human contact, STS with human–machine interface, and

HTM) compared to usual care for patients with a recent

HF admission, using data from acute hospitals in the

UK. HTM was the most cost-effective strategy and

yielded an estimated incremental cost-effectiveness ratio

(ICER) of £11 873/quality-adjusted life year (QALY)

compared to usual care. In the UK, the National Institute

for Health and Clinical Excellence (NICE) typically

recommends in favour of funding interventions with an

ICER below thresholds of £20 000/QALY. However,

cost-effectiveness analyses depend very much on the

target population and the service organisation. Monday

to Friday, 9 a. m.–5 p. m. services aimed at improving

care for patients with a recent hospital discharge appear

cost-effective, services that target stable patients or offer

24/7 support, that focus on crisis detection and man-

agement, do not [48–50].

The following issues need to be addressed:

1. What is the mechanism of benefit?

It can be difficult to tease out why patients benefit from

telemedicine. Simply monitoring the patient will not confer

benefit unless action results. A consistent finding is that

patients in the active limb of studies showing a benefit from

TM are more likely to be on higher doses of disease-

modifying agents, which probably mediates benefit. Pre-

sumably, monitoring encourages titration of medication

and provides a mechanism for auditing when care is sub-

optimal. There may be an additional contribution made by

detecting impending deterioration, thereby stimulating

intervention to prevent admission. There are other ways of

ensuring robust delivery of excellent care but they are

costly in terms of human resources. It often takes little

effort to find out what has been done; it can take a great

deal of effort to spot what is missing or left undone. An

electronic patient record (EPR) is essential to manage this

sort of problem. An effective TM system requires an EPR,

which may be an important aspect of how TM works.

2. Which patients are most likely to benefit?

Although it is imprecise to compare trials that have

investigated different patient populations and technologies

over different follow-up periods, a common characteristic

of patients in ‘positive’ trials is the history of a recent HF

hospitalisation (Table 1). In contrast, the TIM-HF trial

suggested that stable, well-managed HF patients do not

benefit from TM. Indeed, in order for an intervention to

show effect, it is important to select those patients who are

‘sick enough’ to benefit. Following an admission for heart

failure, patients are at substantial risk of readmission and it

is in this situation that TM has most to offer. Also, if

enough resources are expended to ensure that management

is equally good in patients assigned to the control group,

the appropriate primary outcome of a trial is not patient

outcome but the costs of the service.

3. Which data should we monitor? How often?

As HF is a complex disease, often intertwined with

several comorbidities, we cannot rely on a single variable

to guide management [28, 29]. The exception to this rule

seems to be the invasive monitoring of intracardiac pres-

sures. In contrast to traditional vital signs, which are an

indirect reflection of HF status, intracardiac pressures are

directly related to congestion and are a target for treatment.

However, monitoring intracardiac pressures alone is unli-

kely to provide a safe and effective TM system. Systemic

arterial pressure is still required to guide therapy. Poten-

tially, this could also be monitored by an implanted chip. A

randomised controlled trial examining the effect of adding

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invasive haemodynamic monitoring to non-invasive TM

would be of great interest. Also, new ‘sensors’ will be

developed, for instance blood bio-markers [51] or sensors

that measure cardiac output and vascular resistance as well

as pressure.

4. Who receives the data, who takes action and how?

To process the TM data efficiently, three important

strategies are required. Firstly, efforts have to be made to

engage patients as active members of the TM loop,

enabling them to deal with most of the care decisions

themselves and requiring help from professionals on rela-

tively rare occasions (Fig. 1). The health professional

should act as a ‘guardian angel’ to spot when the patient–

technology interaction is failing. This requires a TM

Table 1 Selection of telemonitoring trials and overview of main characteristics and results

Trial N System Recent

hospitalisation

Age (years) EF (%) Duration

FU

Mortality HF

hospitalisations

Observational trials

CARME [22] 92 BP, HR, weight No 66.3 36 ± 14 % 11.8 months NA -67.8 % (p 0.01)

Randomised controlled trials

WHARF [8] 280 Weight and

symptoms

Yes 59 B35 6 months -56 %

(p \ 0.003)

NS

TEN-HMS

[11]

426 Weight, BP, HR

and rhythm

Yes 67 (48 %

[70 years)

\40 (mean

25 %)

240 days;

15 months

-36 %

(1 year)

(p 0.032)

NS

WSD [12] 3,230 HF: weight (other

variables not

specified)

No 70 NA 12 months p \ 0.001 NA

CHAMPION

[13]

550 Pulmonary artery

pressures

Yes 61.5 21.5 % had

EF C 40 %

6 months NA -28 %

(p 0.0002)

TEMA-HF

[14]

160 Daily BP, weight

and HR

Yes 76 35 % 6 months p 0.01 NS

Tele-HF [18] 1,653 Telephone-based

interactive voice-

response

Yes 61 70.6 % had

EF \ 40 %

180 days NS NS

Home-HF

[20]

182 Weight, BP, HR

and oxygen

saturation

Yes 72 (45 %

C75 years)

39 % had

EF C 40 %

6 months Days alive

and out of

hospital:

NS

Fewer emergency

HF

hospitalisations

(p 0.01)

Mobitel [21] 120 Weight, BP and

HR

Yes 66 27 % 6 months NA NA

TIM-HF [24] 710 Daily ECG, BP and

weight

No 66.9 27 % 26 months NS NS

COMPASS

[30]

274 RV systolic and

diastolic

pressures and

ePAD (at least

weekly review by

clinicians)

Yes 58 70 patients

with

EF C 50 %

6 months NA NS

BP blood pressure, ECG electrocardiogram, EF ejection fraction, HR heart rate, NA not available, NS not significant, RV right ventricle, ePAD

estimated pulmonary arterial diastolic pressure

Fig. 1 Primary and secondary loop of care decisions in third-

generation telemonitoring systems (adapted from FP7 HeartCycle

programma)

Heart Fail Rev

123

system that not only provides the patient with feedback but

also with instructions on how to deal with common prob-

lems (‘decision support’). After all, patients are the largest

available workforce and improving their knowledge and

self-care skills will undoubtedly translate into better quality

of care [52]. Such a decision-support system is currently

being investigated in the European HeartCycle FP7 project.

Secondly, instead of dealing with data passively, waiting

for alerts to occur that only then trigger action (when it may

be too late), a more pro-active attitude could impede the

progression of heart failure more effectively. Cleland et al.

[43] have referred to this as a so-called health maintenance

strategy, whereby an ideal range of values is identified and

active optimisation of therapy is stimulated to maintain

patients in a safer zone (Fig. 2). One of the main obstacles of

crisis detection, the number of false-positive alerts, is avoi-

ded by this approach. Importantly, by providing a service

only during usual working hours, the strategy requires fewer

staff and therefore lower running costs. This strategy may

also bypass some medico-legal issues.

Thirdly, if we want TM to be successful, coordination

and multidisciplinary communication is key. The best

approach might be the development of specialised tele-

health centres, run by ‘telehealth specialists/technicians’,

perhaps specialised HF nurses or physicians, who have

adequate background knowledge about HF and its comor-

bidities, and are able to optimise therapy and decide on

specialist referral if necessary. They would bridge the gap

between community, primary and secondary care. Of

course, this will only be possible with sophisticated IT-

support and efficient electronic record keeping. As the

potential role of telemonitoring in other chronic conditions

is investigated (such as diabetes and chronic lung disease),

it may even be that a subspecialty of ‘telemedicine’ is the

best way forward, allowing patients to have a single point

of contact rather than with multiple teams. An alternative

view is that TM is best delivered as an extension of expert

local care by the same people who will see the patient at

clinic, in their home or during admission. Ultimately, local

circumstances and champions will determine the shape of

the service.

5. How are the data to be handled?

A major potential problem with telemonitoring is the

huge volume of data that can be generated. It is not prac-

tical for all the data to be examined ‘manually’. TM should

be an integral part of a patient electronic health record.

Transmitted measurements are usually automatically

compared to preset limits. However, instead of merely

defining standard thresholds for alerts, a sophisticated and

pro-active decision-support system (not only for patients,

but also for health-care professionals) could deliver better

results. Such a system might highlight patients who are not

yet optimally treated, but who have vital signs that allow

further uptitration of medication. As soon as a patient

migrates outside of the ‘health maintenance zone’, the

patient and/or telehealth team would be notified to inter-

vene to prevent measurements straying too far from ideal

and intervening long before an actual crisis situation

occurs. Such a strategy would obviate the need for a 24/7

service and improve cost-effectiveness [53].

In conclusion, telemonitoring is a promising approach

that could empower patients with heart failure and allow

them to take a much more active part in their own man-

agement. It has the potential to help reduce the need for

hospitalisation and perhaps to improve mortality, but fun-

damental questions remain: which physiological variables

are most useful; which patients should be targeted; how

should the data be collected, filtered and managed; what is

the mechanism of benefit; and who is going to manage the

data. Further research is required to address these issues.

We suspect that in a decade or so, the clinical community

will be wondering what all the fuss was about and why we

took so long to implement such an intuitively obvious

strategy for improving patient care.

Acknowledgments Prof. Cleland is supported, in part, by the NIHR

cardiovascular Biomedical Research Unit at the Royal Brompton and

Harefield NHS Foundation Trust and Imperial College, London.

Conflict of interest Departmental research support has been

received from Philips.

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