journals.plos.org Open in urlscan Pro
35.190.43.188  Public Scan

Form analysis
1 forms found in the DOM

Name: searchForm — GET /plosone/search

<form class="_2kYK9F4JyicQEvs85XPiny" name="searchForm" action="/plosone/search" method="get"><input id="search" type="text" name="q" placeholder="SEARCH" required="" aria-label="Search"><button id="headerSearchButton" type="submit"
    title="Submit search" aria-label="Submit search" class=" _2t432kRILm79F3WhLGdN6N _1ZnlodV6MiltAFPjbT7D2U"><span>Search</span></button><input type="hidden" name="filterJournals" value="PLoSONE"></form>

Text Content

Skip to main content
Advertisement

 * plos.org
 * create account
 * sign in

 * * Publish
   * About
   * Browse
   
 * 
 * Search
   advanced search
 * Browse Topics

Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.



 * 272
   Save
   
   Total Mendeley and Citeulike bookmarks.

 * 101
   Citation
   
   Paper's citation count computed by Dimensions.

 * 14,240
   View
   
   PLOS views and downloads.

 * 10
   Share
   
   Sum of Facebook, Twitter, Reddit and Wikipedia activity.

Open Access

Peer-reviewed

Research Article


COMPARATIVE EFFECTIVENESS OF DIFFERENT FORMS OF TELEMEDICINE FOR INDIVIDUALS
WITH HEART FAILURE (HF): A SYSTEMATIC REVIEW AND NETWORK META-ANALYSIS

 * Ahmed Kotb ,
   
   * E-mail: akotb@ottawaheart.ca
   
   Affiliation University of Ottawa Heart Institute, Ottawa, Canada
   
   ⨯
 * Chris Cameron,
   
   Affiliations Department of Community Medicine and Epidemiology, University of
   Ottawa, Ottawa, Canada, The Ottawa Hospital Research Institute, University of
   Ottawa, Canada
   
   ⨯
 * Shuching Hsieh,
   
   Affiliation University of Ottawa Heart Institute, Ottawa, Canada
   
   ⨯
 * George Wells
   
   Affiliations University of Ottawa Heart Institute, Ottawa, Canada, Department
   of Community Medicine and Epidemiology, University of Ottawa, Ottawa, Canada
   
   ⨯


COMPARATIVE EFFECTIVENESS OF DIFFERENT FORMS OF TELEMEDICINE FOR INDIVIDUALS
WITH HEART FAILURE (HF): A SYSTEMATIC REVIEW AND NETWORK META-ANALYSIS

 * Ahmed Kotb, 
 * Chris Cameron, 
 * Shuching Hsieh, 
 * George Wells

x
 * Published: February 25, 2015
 * https://doi.org/10.1371/journal.pone.0118681
 * 


 * Article
 * Authors
 * Metrics
 * Comments
 * Media Coverage

 * Abstract
 * Introduction
 * Methods
 * Results
 * Discussion
 * Supporting Information
 * Acknowledgments
 * Author Contributions
 * References

 * Reader Comments
 * Figures





ABSTRACT


BACKGROUND

Previous studies on telemedicine have either focused on its role in the
management of chronic diseases in general or examined its effectiveness in
comparison to standard post-discharge care. Little has been done to determine
the comparative impact of different telemedicine options for a specific
population such as individuals with heart failure (HF).


METHODS AND FINDINGS

Systematic reviews (SR) of randomized controlled trials (RCTs) that examined
telephone support, telemonitoring, video monitoring or electrocardiographic
monitoring for HF patients were identified using a comprehensive search of the
following databases: MEDLINE, EMBASE, CINAHL and The Cochrane Library. Studies
were included if they reported the primary outcome of mortality or any of the
following secondary outcomes: all-cause hospitalization and heart failure
hospitalization. Thirty RCTs (N = 10,193 patients) were included. Compared to
usual care, structured telephone support was found to reduce the odds of
mortality(Odds Ratio 0.80; 95% Credible Intervals [0.66 to 0.96]) and
hospitalizations due to heart failure (0.69; [0.56 to 0.85]). Telemonitoring was
also found to reduce the odds of mortality(0.53; [0.36 to 0.80]) and reduce
hospitalizations related to heart failure (0.64; [0.39 to 0.95]) compared to
usual post-discharge care. Interventions that involved ECG monitoring also
reduced the odds of hospitalization due to heart failure (0.71; [0.52 to 0.98]).


LIMITATIONS

Much of the evidence currently available has focused on the comparing either
telephone support or telemonitoring with usual care. This has therefore limited
our current understanding of how some of the less common forms of telemedicine
compare to one another.


CONCLUSIONS

Compared to usual care, structured telephone support and telemonitoring
significantly reduced the odds of deaths and hospitalization due to heart
failure. Despite being the most widely studied forms of telemedicine, little has
been done to directly compare these two interventions against one another.
Further research into their comparative cost-effectiveness is also warranted.


FIGURES

  

Citation: Kotb A, Cameron C, Hsieh S, Wells G (2015) Comparative Effectiveness
of Different Forms of Telemedicine for Individuals with Heart Failure (HF): A
Systematic Review and Network Meta-Analysis. PLoS ONE 10(2): e0118681.
https://doi.org/10.1371/journal.pone.0118681

Academic Editor: Wen-Chih Hank Wu, Providence VA Medical Center and Brown
University, UNITED STATES

Received: May 23, 2014; Accepted: January 6, 2015; Published: February 25, 2015

Copyright: © 2015 Kotb et al. This is an open access article distributed under
the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the
original author and source are credited

Data Availability: All relevant data are within the paper and its Supporting
Information files.

Funding: Ahmed Kotb is the recipient of the Ontario Graduate Scholarship. Chris
Cameron is the recipient of a Vanier Canada Graduate Scholarship from the
Canadian Institutes of Health Research (CIHR). Ahmed Kotb and Chris Cameron have
received funding from the Canadian Network and Centre for Trials Internationally
(CANNeCTIN).

Competing interests: The authors have declared no competing interests exist.


INTRODUCTION

Heart Failure (HF) is a complex debilitating syndrome that results from a
cardiac dysfunction that impairs the ability of the ventricle to fill with or
eject blood. Living with this disease, individuals with HF experience a
substantially high rate of deaths and further cardiac illness. This has led many
of those who care for HF patients to believe that they may benefit from more
frequent monitoring and follow-up than would otherwise be possible under the
current standard of care. This has, in turn, led to the introduction of
telemedicine as a potential means for reducing the likelihood of worsening
cardiac illness or the prospect of repeated and lengthy hospital readmissions.

Earlier research comparing usual care to a variety of telemedicine interventions
demonstrated that the use of telemedicine significantly reduced all-cause
mortality and hospital admissions related to heart failure[1–4]. It was
suggested as well that while such interventions will require an initial
financial investment, they will likely lead to substantially reduced costs in
the long term particularly by reducing the costs associated with readmission and
hospital stay[5].

Despite these potential benefits, the evidence has been somewhat limited in that
studies generally focused on the impact of very broadly defined multifaceted
telemedicine interventions as they compared to usual care only rather than on
how these inherently different technologies compared to one another. This
inconsistency may in part be due to the fact that the definition of telemedicine
has varied substantially in their intensity, invasiveness and complexity across
studies. Earlier findings regarding the impact of telemedicine for the
management of HF have also been relatively inconsistent with more recent ones.

In 2013, a systematic review and network meta-analysis found no significant
reductions in all-cause mortality or all-cause hospital readmissions associated
with interventions of remote monitoring when compared to usual care for recently
discharged patients with heart failure[6]. This review sought to examine the
impact of four well-defined forms of telemedicine to determine whether remote
monitoring strategies can improve outcomes for adults who have been recently
discharged (<28 days) following an unplanned admission due to acute heart
failure. As such, there review excluded individuals with stable and chronic
heart failure. Most of the studies included in this review followed participants
for 6 months or less making it difficult to determine whether or not these
interventions can have a lasting effect. They also only included studies with a
contemporaneous control group and focused on telephone support interventions
(delivered between human to human or human to machine) and telemonitoring
(delivered 24 hours a day and 7 days a week or during office hours) making it
rather likely that other forms of currently available telemedicine interventions
were not included.

An overview of reviews and network meta-analysis of the literature were
conducted to quantify, summarize and compare the rates of death, hospitalization
and hospitalization due to heart failure for individuals with chronic heart
failure who received standard care after discharge or other forms of
telemedicine. Network meta-analysis, also known as mixed-treatment comparisons
meta-analysis or multiple-treatments meta-analysis would allow the integration
of data from direct (when treatments are compared within a randomized trial) and
indirect comparisons (when treatments are compared between trials by combining
results on how effective they are compared with a common comparator treatment)
[7–9]. Simultaneously integrating data using this method can result in greater
precision when calculating effect estimates.

Unlike previous reviews, this analysis will not only examine the potential
impact of telemedicine against usual care, it will also examine the comparative
effectiveness of these different interventions against one another.
Interventions compared included usual care and the following five forms of
telemedicine: Structured telephone support (STS) which involves regular
follow-up calls between the health professional and the patient; Telemonitoring
systems which involve the transmission of information on symptoms and signs
(TM); Telemonitoring systems and regular telephone follow-up combined;
Telemedicine systems involving video monitoring (VIDEO); and Telemedicine
systems involving electrocardiographic transmissions (ECG).


METHODS


DATA SOURCES AND SEARCHES

Relevant systematic reviews of randomized controlled trials were identified by
searching the following databases until December 2012: The Cochrane Library,
Medline, Embase and CINAHL. The search strategies are described in S1 File.
There were no restrictions on language, publication year, or type of
publication. References of included studies and narrative reviews were searched
as well.


STUDY SELECTION

Study selection was conducted by two independent reviewers. All abstracts were
examined and reviews were included if they considered the impact of telemedicine
interventions in adult heart failure patients. Randomized controlled trials from
identified reviews were reviewed in full length and included in the network
meta-analysis if they satisfied all of the following criteria:

 * Prospective enrolment of consecutive patients with objectively confirmed
   coronary artery disease with symptomatic heart failure (New York Heart
   Association [NYHA] Class I-IV) characterized by impaired left ventricular
   function (Left Ventricular Ejection Fraction ≤ 45%).
 * Patients randomized to receive telemedicine (telephone support,
   telemonitoring, telephone support and telemonitoring together, video
   monitoring or monitoring by ECG) or usual care. The aforementioned
   telemedicine strategies are briefly described in Table A in S1 File.
 * One or more of the primary outcome or secondary outcomes were reported. The
   primary outcome measure was all-cause mortality. Secondary outcome measures
   included all-cause hospitalization (defined as an admission to a health care
   facility for > 24 hours due to any cause) or hospitalization due to heart
   failure (defined as an admission to a health care facility for > 24 hours due
   to worsening heart failure).



Studies were excluded if patients met any of the following exclusion criteria:

 * Coronary artery disease patients with preserved left ventricular function
   (left ventricular ejection fraction >45%)
 * Had an acute coronary syndrome or coronary artery bypass surgery within 12
   weeks
 * Have rheumatic heart disease, severe aortic or mitral valvular heart disease
 * Have a medical condition likely to limit survival to < 1 year
 * Reside in a nursing facility or receive home visits
 * Are unable or unwilling to provide informed consent




DATA EXTRACTION AND QUALITY ASSESSMENT

Two reviewers (AK and SH) independently assessed the eligibility of the studies
identified in the initial search for inclusion in the review and independently
extracted the data from papers considered potentially eligible using a
standardized data abstraction form. Both reviewers independently assessed the
methodological quality of included reviews using the AMSTAR tool [10] and the
quality of included randomized controlled trials using the SIGN-50 checklist
[11].


DATA SYNTHESIS AND ANALYSIS

Bayesian network meta-analyses and direct frequentist pairwise meta-analyses
were conducted for all outcomes. For the primary analysis, the frequency data
from each trial were used in the network meta-analysis using WinBUGS (MRC
Biostatistics Unit, Cambridge, UK)[12,13]. Bayesian network meta-analysis (NMA)
using a binomial likelihood model, which allows for the use of multi-arm trials,
were conducted. Random effects network meta-analyses with vague priors were
assigned for basic parameters were conducted for the analyses. The WinBUGS code
used for the Random Effects Model is available in S1 File [14]. Three chains
were fit in WinBUGS for each analysis, with 40,000 iterations, and a burn-in of
40,000 iterations. Odds ratios and 95% credible intervals were modelled using
Markov chain Monte Carlo methods. We constructed all evidence networks using
NodeXL[15].

Assessment of model fit for NMA comprised of assessment of the deviance
information criterion (DIC) and the residual deviance in comparison with the
number of unconstrained datapoints [16]. Models with smaller DIC were preferred
to models with larger DIC. Similarly, the total value for the residual deviance
should be lower than the number of unconstrained data points. To ensure
convergence was reached, Brooks-Gelman-Rubin plots were assessed [17]. Model
convergence is evident when the Gelman-Rubin statistic approaches 1.

A network meta-analysis also requires that studies are sufficiently similar in
order to pool their results. We assessed available study and patient
characteristics to ensure similarity and to investigate the potential effect of
heterogeneity on effect estimates. Inconsistency was assessed by comparing
statistics for the deviance and deviance information criterion in fitted
consistency and inconsistency models. Additionally, fixed effects models with
vague priors were conducted.

When considered appropriate, pair-wise meta-analyses were conducted by combining
studies that compared the same interventions using a random-effects model.
Heterogeneity was investigated by examining both forest plots and the
inconsistency index (I2). [18] I2 values of less than 25% represented mild
heterogeneity, between 25% and 50% represented moderate heterogeneity, and
greater than 50% represented considerable heterogeneity. Results having a
p-value of less than 0.05 and 95% confidence intervals (CIs) that excluded 1
were considered to be statistically significant. These analyses were carried out
using Comprehensive Meta-Analysis and Review Manager of the Cochrane
Collaboration. The results from our network meta-analysis were qualitatively
compared with direct, frequentist, pairwise estimates.


RESULTS

Of the 757 citations identified from our literature search, 700 were excluded
after examining their titles and abstracts. The full manuscripts of the
remaining 57 were assessed. From those, we identified 8 eligible systematic
reviews [1,3,4,18–28]. Fig. 1 shows a modified PRISMA diagram describing the
selection of studies. Of the included reviews, 5 included a meta-analysis of the
data. Six out of the 8 reviews were found to have met the following criteria:
duplicate study selection and data extraction; comprehensive literature search;
provided characteristics of their included studies; and assessed their included
studies’ scientific quality. Characteristics of these reviews and their AMSTAR
quality assessment are detailed in Tables B and C in S1 File.

Download:
 * PPT
   PowerPoint slide
 * PNG
   larger image
 * TIFF
   original image

Fig 1. Flow chart for the identification of studies used in the network
meta-analysis of telemedicine interventions for heart failure patients



https://doi.org/10.1371/journal.pone.0118681.g001

Thirty eligible randomized controlled trials[29–59], with a total of 10,193
patients, were then identified from the systematic reviews and included in the
network meta-analysis. In most randomized controlled trials, a single
telemedicine intervention was compared with usual care. For most studies (21 out
of 30) patients mean age was greater than 65 and in all but one study the
patients were mostly males. In 27 of 30 trials, participants were followed for 6
or more months and in 25 trials the intervention was delivered for 6 or more
months. However, the frequency of delivering the intervention did vary
considerably. In most trials, the health professional that typically delivered
the intervention was a nurse. A more detailed description of included trials is
provided in Table 1.

Download:
 * PPT
   PowerPoint slide
 * PNG
   larger image
 * TIFF
   original image

Table 1. Description of included studies.



https://doi.org/10.1371/journal.pone.0118681.t001

Using the SIGN-50 assessment tool, 17 of the 30 randomized controlled trials
were judged to be of high quality, 10 were acceptable and 3 were considered of
poor quality. Generally, trials were judged to have appropriately randomized
patients, adequately concealed allocation, similar groups at baseline, and had
few losses to follow-up and analyzed patients according to the intention to
treat. A summary of the quality of the trials is provided in Table 1 and further
details on the assessment of trials using the SIGN-50 tool are provided in Table
D in S1 File.


DIRECT COMPARISONS

Twenty-nine trials contributed to the analysis of the outcome of death, twenty
to the analysis of hospitalization and sixteen for the analysis of
hospitalization due to heart failure. Of the 15 possible pairwise comparisons
that can be made across the 6 interventions, the evidence available was found to
have only examined 8 comparisons directly for death as well as for
hospitalization. Six out of 10 possible pairwise comparisons were available for
hospitalization due to heart failure. Fig. 2 shows the evidence network for the
outcome of death. For the outcomes of hospitalization and heart failure
hospitalization, the evidence networks are provided in Figs. A and B in S1 File.

Download:
 * PPT
   PowerPoint slide
 * PNG
   larger image
 * TIFF
   original image

Fig 2. Evidence network for interventions included in the analysis of all-cause
mortality.



Each node represents an intervention and the size of each node indicates how
many patients received it of the total number of patients included in the
network (N = 10,193). The solid lines connecting the nodes together indicate the
existence of this comparison of interventions in the literature. The thickness
of the lines represents how many studies of the total number of studies (30
studies) include a particular comparison.



https://doi.org/10.1371/journal.pone.0118681.g002

Direct comparisons (Fig. 3, S1 and S2 Figs.) show that telemonitoring was found
to be more effective than usual care in reducing the numbers of death (Odds
ratio (OR) 0.52 95% Confidence Intervals (CI) [0.37, 0.72]), hospitalization
(0.70 [0.51, 0.96]), and hospitalization resulting from heart failure (0.70
[0.51, 0.98]). Fewer patients receiving structured telephone support
interventions were hospitalized for all causes (0.86 [0.77, 0.97)] and due to
heart failure (0.76 [0.65, 0.89)] than patients who received usual care.
Similarly, fewer patients who received telemedicine interventions that involved
the use of ECG data transmission were hospitalized than patients who received
usual care (0.70 [0.55, 0.91]). No other comparisons were found to suggest a
significant benefit across the outcomes of death, hospitalization and heart
failure related hospitalization for one intervention over the other. For all
outcomes, heterogeneity was found to be either low or moderate. Forest plots of
each pairwise meta-analysis conducted can be found in Fig. C in S1 File.

Download:
 * PPT
   PowerPoint slide
 * PNG
   larger image
 * TIFF
   original image

Fig 3. The impact of different forms of telemedicine on the outcome of all-cause
mortality.



Effect estimates from the network meta-analysis occupy the bottom left part of
the diagram, the estimates from the pairwise meta-analyes occupy the top right
part of the diagram and the diagonal corresponds to the comparison. The odds
ratios and 95% Credible Intervals for the comparisons in this diagram should be
read from left to right (e.g. Patients receiving structured telephone support
had a 0.80 [0.66, 0.96] reduced odds of death compared to those receiving usual
care). Significant results are underlined and in bold.



https://doi.org/10.1371/journal.pone.0118681.g003


INCORPORATION OF DATA FROM THE INDIRECT COMPARISONS OF INTERVENTIONS

Across all outcomes the Brooks-Gelman-Rubin plots demonstrated model
convergence. An assessment of model fit and inconsistency is described in the
Tables E-H and Figs. D-F in S1 File.

All-cause mortality. Compared with usual care, the only interventions that
significantly reduced the odds of death were structured telephone support [OR
0.80 95% Credible Intervals (CrI) (0.66, 0.96)] and telemonitoring [0.53 (0.36,
0.80)]. No other significant differences were observed across treatment
comparisons (Fig. 3). In terms of potentially reducing the odds of death,
telemonitoring ranked first, followed by structured telephone support delivered
in combination with telemonitoring, electrocardiographic data transmission,
structured telephone support, usual care and video monitoring.

All-cause hospitalization. For the most part, the odds of hospitalization did
not significantly vary across interventions (S1 Fig.) making these results
relatively consistent with the results from the direct pairwise analyses. The
main difference however, was that in this analysis, both structured telephone
support and telemonitoring were no longer found to significantly reduce
all-cause hospitalization compared to usual care. However, these results are
considered more robust given their incorporation of all available data from both
direct and indirect comparisons. According to their relative potential for
reducing hospitalizations, telemonitoring was ranked first, followed by video
monitoring, structured telephone support, electrocardiographic data
transmission, usual care and structured telephone support and telemonitoring.

Heart failure hospitalization. The incorporation of indirect evidence confirmed
that structured telephone support interventions [OR 0.69 95% CrI (0.56, 0.85)],
telemonitoring interventions [0.64 (0.39, 0.95)] and telemedicine that included
the transmission of electrocardiographic data [0.71 (0.52, 0.98)] all
significantly reduced hospitalizations due to heart failure compared to usual
care. The remaining comparisons did not show favor for one intervention over the
other (S2 Fig.). Once again, telemonitoring interventions ranked first, followed
by structured telephone support, telemedicine that involved electrocardiographic
data transmission, structured telephone support and telemonitoring interventions
delivered together and usual care.

Sensitivity analyses. To examine other potential sources of heterogeneity in the
network, the following areas were examined across all included studies in
subsequent sensitivity analyses: randomization, concealment of allocation,
degree of loss to follow-up, and the inclusion of all randomized participants in
the analysis according to the intention to treat principle. Additional
sensitivity analyses were conducted by repeating the primary analysis using a
fixed-effect method. The results of these analyses are in Tables I-N in S1 File.


DISCUSSION

Telemonitoring as well as structured telephone support interventions were both
found to be significantly better than usual care in reducing deaths and heart
failure related hospitalizations. Telemedicine interventions that involved the
use of electrocardiographic (ECG) data transmission were also significantly more
effective in reducing hospitalizations due to heart failure when compared with
usual care. There were no other significant differences found across the
interventions compared. This review and network meta-analysis adheres to PRISMA
reporting standards (S1 PRISMA Checklist) and is also the only one to date that
compares the aforementioned forms of telemedicine against one another as well as
standard post-discharge care. The advantage of conducting this multiple
treatment comparison (MTC) meta-analysis was that it made possible the
incorporation data from both indirect and direct comparisons.

Most of the evidence that is currently available on the impact of telemedicine
interventions involves the comparison of an active form of telemedicine to
standard care. As such, findings from this network meta-analysis are unique in
that they examine the various comparisons across five main forms of active
telemedicine interventions. For the first time, the currently available
interventions were ranked according to their effectiveness in reducing the
outcomes of death, hospitalization and hospitalization due to heart failure.
When these different interventions were ranked, telemonitoring was always ranked
first.

Findings from this review confirm the findings of previous systematic reviews
and meta-analysis [1,3,4] and extend beyond them. Previous studies have
concentrated on the effectiveness of telemedicine compared to usual care. A
recent review by Xiang (2013) demonstrated that interventions such as
telemonitoring or nurse administered telephone-based management programs were
clinically effective in patients with chronic heart failure when compared with
usual care. The estimates obtained from their meta-analysis and meta-regression
were very similar for the outcomes of mortality and heart failure-related
hospitalization to the outcomes obtained in this network meta-analysis [60].
Additionally, they examined the outcome of heart failure-related hospital length
of stay and found that telehealth programs were associated with as significant
reduction in this outcome when compared with usual care. However, this
meta-analysis is limited in that it does not examine how these various forms of
currently available telemedicine interventions perform when compared against one
another and not just against usual care.

A recent network meta-analysis of telemedicine interventions for individuals
with acute heart failure demonstrated that telephone support delivered from
human to human and telemonitoring delivered during office hours showed
beneficial trends compared to usual care, particularly in reducing all-cause
mortality [6]. Despite both results trending in the same direction, the results
described in this previous review, did not reach statistical significance. This
difference may suggest that chronic heart failure patients, who are more stable,
may stand to benefit more from telemedicine. To definitively determine this,
further research is warranted. The difference in results may also be due to the
inclusion of a smaller amount of evidence. In this review, 30 studies were
included if they compared five forms of telemedicine to usual care or to each
other. In the previous network meta-analysis, 21 studies were included if they
included a control group and examined the impact of telephone support or
telemonitoring. Finally, only 9 out of 21 studies were followed participants for
more than 6 months. In this review, 19 out of 30 studies had longer than 6
months of follow-up. This may suggest that the potential benefits of
telemedicine require longer periods of follow-up before they are observed.

This review demonstrated that the amount of evidence available in the literature
for directly comparing across the active forms of telemedicine was limited.
Since much of the currently available evidence has focused more on telephone
support and telemonitoring interventions, other widely available forms of
telemedicine such as video monitoring and monitoring by ECG remain relatively
understudied. Further research is still needed before more definitive
conclusions can be made regarding their effectiveness. However, the ability to
integrate evidence from direct and indirect comparisons as a result of
conducting this network meta-analysis allowed for a gain of statistical
precision compared with previous reviews and allowed for the comparison of
interventions that had not been previously compared in the literature and. This
network analysis was limited to only including randomized controlled trials.
This was deemed appropriate, however, given the availability of a substantial
amount of evidence and the reduced likelihood of bias and confounding associated
with this study design. As is the case with any meta-analysis, the strength of
the analysis depends on the quality and the completeness of the available
evidence. For the most part, the risk of bias associated with included studies
was found to be either low or acceptable and further sensitivity analyses did
not significantly differ from the study’s main analysis.

In summary, this analysis has demonstrated that structured telephone support and
telemonitoring interventions and may be of significant benefit for
rehabilitating heart failure patients. This work represents the first
application of network meta-analysis to examine the comparative effectiveness of
five telemedicine interventions in improving heart failure patient outcomes.
Findings from this network meta-analysis confirm the findings of previous
reviews [1,3,4] that telemedicine may significantly improve patient outcomes
beyond usual care and extend beyond them by comparing these different forms of
telemedicine against each other. Further research is needed to examine the
long-term impact and cost-effectiveness of telemonitoring and structured
telephone support interventions on specific subsets of heart failure patients
considered most likely to benefit.


SUPPORTING INFORMATION

Comparative Effectiveness of Different Forms of Telemedicine for Individuals
with Heart Failure (HF): A Systematic Review and Network Meta-Analysis

Showing 1/4: S1_PRISMA_Checklist.docx

Skip to figshare navigation

1 / 4
ShareDownload

figshare


S1 PRISMA CHECKLIST. PRISMA CHECKLIST.

https://doi.org/10.1371/journal.pone.0118681.s001

(DOCX)


S1 FIG. THE IMPACT OF DIFFERENT FORMS OF TELEMEDICINE ON THE OUTCOME OF
ALL-CAUSE HOSPITALIZATION.

Effect estimates from the network meta-analysis occupy the bottom left part of
the diagram, the estimates from the pairwise meta-analyes occupy the top right
part of the diagram and the diagonal corresponds to the comparison. The odds
ratios and 95% Credible Intervals for the comparisons in this diagram should be
read from left to right (e.g. Patients receiving structured telephone support
had a 0.86 [0.77, 0.97] reduced odds of all-cause hospitalization compared to
those receiving usual care). Significant results are underlined and in bold.

https://doi.org/10.1371/journal.pone.0118681.s002

(TIF)


S2 FIG. THE IMPACT OF DIFFERENT FORMS OF TELEMEDICINE ON THE OUTCOME OF
HOSPITALIZATION DUE TO HEART FAILURE.

Effect estimates from the network meta-analysis occupy the bottom left part of
the diagram, the estimates from the pairwise meta-analyes occupy the top right
part of the diagram and the diagonal corresponds to the comparison. The odds
ratios and 95% Credible Intervals for the comparisons in this diagram should be
read from left to right (e.g. Patients receiving structured telephone support
had a 0.69 [0.56, 0.85] reduced odds of hospitalization due to heart failure
compared to those receiving usual care). Significant results are underlined and
in bold.

https://doi.org/10.1371/journal.pone.0118681.s003

(TIF)


S1 FILE. APPENDIX FILE.

https://doi.org/10.1371/journal.pone.0118681.s004

(DOCX)


ACKNOWLEDGMENTS

The authors would like to acknowledge Dr. Alaa Kotb for providing his expertise
in Cardiology and Ms. Agnieszka Szczotka from the University of Ottawa Heart
Institute for her help in devising the search strategy.


AUTHOR CONTRIBUTIONS

Analyzed the data: AK CC GW. Wrote the paper: AK GW. Conception and design: AK
GW. Interpretation of the data: AK CC GW. Drafting of the article: AK GW.
Critical revision of the article for important intellectual content: AK CC SH
GW. Final approval of the article: AK CC SH GW. Statistical expertise: AK CC GW.
Collection and assembly of data: AK SH.


REFERENCES

 1.  1. Clark R, Inglis S, McAlister F, Cleland J, Stewart S. Telemonitoring or
     structured telephone support programmes for patients with chronic heart
     failure: systematic review and meta-analysis. BMJ: British Medical Journal
     (International Edition). 2007; 334(7600): 942–945. pmid:17426062
     * View Article
     * PubMed/NCBI
     * Google Scholar
 2.  2. Polisena J, Tran K, Cimon K, Hutton B, McGill S, Palmer K, et al. Home
     telemonitoring for congestive heart failure: a systematic review and
     meta-analysis. Journal of Telemedicine and Telecare. 2010; 16: 68–76.
     pmid:20008054
     * View Article
     * PubMed/NCBI
     * Google Scholar
 3.  3. Clarke M, Shah A, Sharma U. Systematic review of studies on
     telemonitoring of patients with congestive heart failure: A meta-analysis.
     Journal of Telemedicine and Telecare. 2011; 17(1): 7–14. pmid:21097564
     * View Article
     * PubMed/NCBI
     * Google Scholar
 4.  4. Inglis SC, Clark RA, McAlister FA, Ball J, Lewinter C, Cullington D, et
     al. Structured telephone support or telemonitoring programmes for patients
     with chronic heart failure. Cochrane database of systematic reviews. 2011;
     6.
     * View Article
     * Google Scholar
 5.  5. Seto E. Cost Comparison Between Telemonitoring and Usual Care of Heart
     Failure: A Systematic Review. TELEMEDICINE and e-HEALTH. 2008; 14(7):
     679–686. pmid:18817497
     * View Article
     * PubMed/NCBI
     * Google Scholar
 6.  6. Pandor A, Gomersall T, Stevens JW, Wang J, Al-Mohammad A, Bakhai A, et
     al. Remote monitoring after recent hospital discharge in patients with
     heart failure: a systematic review and network meta-analysis Heart 2013;99:
     1717–1726. pmid:23680885
     * View Article
     * PubMed/NCBI
     * Google Scholar
 7.  7. Song F, Harvey I, Lilford R. Adjusted indirect comparison may be less
     biased than direct comparison for evaluating new pharmaceutical
     interventions. J Clin Epidemiol. 2008; 61(5): 455–63. pmid:18394538
     * View Article
     * PubMed/NCBI
     * Google Scholar
 8.  8. Salanti G, Higgins J, Ades A, Ioannidis JP. (2008) Evaluation of
     networks of randomized trials. Stat Methods Med Res. 2008; 17(3): 279–301.
     pmid:17925316
     * View Article
     * PubMed/NCBI
     * Google Scholar
 9.  9. Ades A, Sculpher M, Sutton A, Abrams K, Cooper N, Welton N, et al.
     Bayesian methods for evidence synthesis in cost-effectiveness analysis.
     Pharmacoeconomics. 2006; 24(1): 1–19. pmid:16445299
     * View Article
     * PubMed/NCBI
     * Google Scholar
 10. 10. Shea BJ, Grimshaw JM, Wells GA, Boers M, Andersson N, Hamel C, et al.
     Development of AMSTAR: a measurement tool to assess the methodological
     quality of systematic reviews. BMC Med Res Methodol. 2007; 7(10):1–7.
     * View Article
     * Google Scholar
 11. 11. Scottish Intercollegiate Guidelines Network (SIGN) 50. A Guideline
     Developer’s Handbook. Available from
     http://www.sign.ac.uk/guidelines/fulltext/50/index.html
 12. 12. Lu G, Ades AE. Mixed treatment comparisons: combination of direct and
     indirect evidence. Statistics in Medicine. 2004; 23(20): 3105–3124.
     pmid:15449338
     * View Article
     * PubMed/NCBI
     * Google Scholar
 13. 13. Lunn DJ, Thomas A, Best N, Spiegelhalter D. WinBUGS—a Bayesian modeling
     framework: concepts, structure, and extensibility. Statistics and
     Computing. 2000; 10: 325–337.
     * View Article
     * Google Scholar
 14. 14. Dias S, Welton NJ, Sutton AJ, Ades AE. NICE DSU Technical support
     document 2: a generalised linear modelling framework for pairwise and
     network meta-analysis of randomized controlled trials. 2011; Last updated
     April 2014. Available from http://www.nicedsu.org.uk.
 15. 15. Smith M, Milic-Frayling N, Shneiderman B, Mendes Rodrigues E, Leskovec
     J, Dunne C. NodeXL: a free and open network overview, discovery and
     exploration add-in for Excel 2007/2010. 2010; Available from
     http://nodexl.codeplex.com/ from the Social Media Research Foundation,
     http://www.smrfoundation.org
 16. 16. Spiegelhalter DJ, Best NG, Carlin BP, Van Der Linde A. Bayesian
     measures of model complexity and fit. Journal of the Royal Statistical
     Society: Series B (Statistical Methodology). 2002; 64(4): 583–639.
     * View Article
     * Google Scholar
 17. 17. Brooks SP, Gelman A. General Methods for Monitoring Convergence of
     Iterative Simulations. Journal of Computational and Graphical Statistics.
     1997; 7: 434–455.
     * View Article
     * Google Scholar
 18. 18. Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency
     in meta-analyses. BMJ. 2003; 327(7414): 557–60. pmid:12958120
     * View Article
     * PubMed/NCBI
     * Google Scholar
 19. 19. Chaudhry SI, Phillips CO, Stewart SS, Riegel B, Mattera JA, Jerant AF,
     et al. Telemonitoring for patients with chronic heart failure: a systematic
     review. Journal of Cardiac Failure. 2007; 13(1): 56–62. pmid:17339004
     * View Article
     * PubMed/NCBI
     * Google Scholar
 20. 20. Clark RA, Inglis SC, McAlister FA, Ball J, Lewinter C, Cullington D, et
     al. Remote (non-invasive) monitoring in heart failure: effect on length of
     stay, quality of life, knowledge, adherance and satisfaction in 8,323 heart
     failure patients: a systematic review. 2010; 944–945 (Abstract).
 21. 21. Clark RA, Inglis SC, Mcalister FA, Stewart S, Cleland JG, Cochrane
     Heart Group. Complex Telemonitoring or a Simple Telephone Call, Which Is
     More Effective in Post-Discharge Heart Failure (HF) Management? Journal of
     Cardiac Failure. 2011; 17(8S): S100.
     * View Article
     * Google Scholar
 22. 22. Dang S, Dimmick S, Kelkar G. Evaluating the evidence base for the use
     of home telehealth remote monitoring in elderly with heart failure.
     Telemedicine and e-Health. 2009; 15 (8): 783–796. pmid:19831704
     * View Article
     * PubMed/NCBI
     * Google Scholar
 23. 23. Giamouzis G, Mastrogiannis D, Koutrakis K, Karayannis G, Parisis C,
     Rountas C, et al. Telemonitoring in chronic heart failure: A systematic
     review. Cardiology Research and Practice. 2012; 1–7.
 24. 24. Holland R, Battersby J, Harvey I, Lenaghan E, Smith J, Hay L.
     Systematic review of multidisciplinary interventions in heart failure.
     Heart. 2005; 91(7): 899–906. pmid:15958358
     * View Article
     * PubMed/NCBI
     * Google Scholar
 25. 25. Inglis SC, Clark RA, McAlister FA, Ball J, Lewinter C, Cullington D et
     al. Benefits of structured telephone support or telemonitoring in heart
     failure on mortality, hospitalisation and cost: a meta-analysis of 8,323
     heart failure patients. 2010; 878 (Abstract).
     * View Article
     * Google Scholar
 26. 26. Inglis SC, Clark RA, McAlister FA, Ball J, Lewinter C, Cullington D, et
     al. A meta-analysis of 8,323 heart failure patients receiving structured
     telephone support or non-invasive telemonitoring to reduce mortality,
     hospitalisation and cost. European Journal of Heart Failure Supplements.
     2010; 9: S179
     * View Article
     * Google Scholar
 27. 27. Inglis SC, Clark RA, McAlister FA, Stewart S, Cleland JG. Which
     components of heart failure programmes are effective? A systematic review
     and meta-analysis of the outcomes of structured telephone support or
     telemonitoring as the primary component of chronic heart failure management
     in 8323 patients: Abridged Cochrane Review. European Journal of Heart
     Failure. 2011; 13: 1028–1040. pmid:21733889
     * View Article
     * PubMed/NCBI
     * Google Scholar
 28. 28. Inglis SC, Clark RA, Stewart S, McAlister FA, Cleland JG. Are
     Telemonitoring and Structured Telephone Support More Effective in Younger
     or Older Heart Failure Patients? A Sensitivity Analysis from a Cochrane
     Review. Journal of Cardiac Failure. 2011; 17(8S): S90.
     * View Article
     * Google Scholar
 29. 29. Klersy C, De Silvestri A, Gabutti G, Raisaro A, Curti M, Regoli F, et
     al. Economic impact of remote patient monitoring: an integrated economic
     model derived from a meta-analysis of randomized controlled trials in heart
     failure. European Journal of Heart Failure. 2011; 13:450–459. pmid:21193439
     * View Article
     * PubMed/NCBI
     * Google Scholar
 30. 30. Angermann CE, Stork S, Gelbrich G, Faller H, Jahns R, Frantz S, et al.
     A Prospective Randomized Controlled Trial Comparing the Efficacy of a
     Standardized, Supraregionally Transferable Program for Monitoring and
     Education of Patients with Systolic Heart Failure with Usual Care—The
     Interdisciplinary Network for Heart Failure (INH) Study. Circulation. 2007;
     116:11_601.
     * View Article
     * Google Scholar
 31. 31. Balk AH, Davidse W, Dommelen P, Klaassen E, Caliskan K, van der Burgh
     P, et al. Tele-guidance of chronic heart failure patients enhances
     knowledge about the disease. A multi-centre, randomised controlled study.
     European Journal of Heart Failure. 2008; 10: 1136–1142. pmid:18790668
     * View Article
     * PubMed/NCBI
     * Google Scholar
 32. 32. Blum K, Gottlieb S. Morbidity and Mortality Benefits of Reliable
     Instrumental Support. Journal of Cardiac Failure 2007; 13 (6): S164
     * View Article
     * Google Scholar
 33. 33. Capomolla S, Pinna G, La Rovere MT, Maestri R, Ceresa M, Ferrari M, et
     al.Heart failure case disease management program: A pilot study of home
     telemonitoring versus usual care. European Heart Journal. 2004; 6: F91–F98.
     * View Article
     * Google Scholar
 34. 34. Cleland JG, Louis AA, Rigby AS, Janssens U, Balk AH, TEN-HMS
     Investigators. Noninvasive home telemonitoring for patients with heart
     failure at high risk of recurrent admission and death: The Trans-European
     Network-Home-Care Management System (TEN-HMS) study. Journal of the
     American College of Cardiology. 2005; 45(10): 1654–1664. pmid:15893183
     * View Article
     * PubMed/NCBI
     * Google Scholar
 35. 35. De Lusignan S, Wells S, Johnson P, Meredith K, Leatham E. Compliance
     and effectiveness of 1 year's home telemonitoring. The report of a pilot
     study of patients with chronic heart failure. European Journal of Heart
     Failure. 2001; 3: 723–730. pmid:11738225
     * View Article
     * PubMed/NCBI
     * Google Scholar
 36. 36. DeBusk RF, Miller NH, Parker KM, Bandura A, Kraemer HC, Cher DJ, et
     al.Care management for low-risk patients with heart failure: A randomized,
     controlled trial. Annals of Internal Medicine. 2004; 141(8): 606–613.
     pmid:15492340
     * View Article
     * PubMed/NCBI
     * Google Scholar
 37. 37. Dendale P, Keulenaer G, Troisfontaines P, Weytjens C, Mullens W,
     Elegeert I, et al. Effect of a telemonitoring-facilitated collaboration
     between general practitioner and heart failure clinic on mortality and
     rehospitalization rates in severe heart failure: The TEMA-HF 1
     (telemonitoring in the management of heart failure) study. European Journal
     of Heart Failure. 2012; 14: 330–340.
     * View Article
     * Google Scholar
 38. 38. DeWalt DA, Malone RM, Bryant ME, Kosnar MC, Corr KE, Rothman RL, et al.
     A heart failure self-management program for patients of all literacy
     levels: A randomized, controlled trial [ISRCTN11535170]. BMC Health
     Services Research. 2006; 6:30. pmid:16533388
     * View Article
     * PubMed/NCBI
     * Google Scholar
 39. 39. Ekman I, Andersson B, Ehnfors M, Matejka G, Persson B, Fagerberg B.
     Feasibility of a nurse-monitored, outpatient-care programme for elderly
     patients with moderate-to-severe, chronic heart failure. European Heart
     Journal. 1998; 19: 1254–1260. pmid:9740348
     * View Article
     * PubMed/NCBI
     * Google Scholar
 40. 40. Galbreath AD, Krasuski RA, Smith B, Stajduhar KC, Kwan MD, Ellis R, et
     al. Long-term healthcare and cost outcomes of disease management in a
     large, randomized, community-based population with heart failure.
     Circulation. 2004; 110(23): 3518–3526. pmid:15531765
     * View Article
     * PubMed/NCBI
     * Google Scholar
 41. 41. Giordano A, Scalvini S, Zanelli E, Corrà U, Longobardi GL, Ricci VA, et
     al. Multicenter randomised trial on home-based telemanagement to prevent
     hospital readmission of patients with chronic heart failure. International
     Journal of Cardiology. 2009; 131: 192–199. pmid:18222552
     * View Article
     * PubMed/NCBI
     * Google Scholar
 42. 42. Goldberg LR, Piette JD, Norine MW, Frank TA, Jaski BE, Smith AL, et al.
     Randomized trial of a daily electronic home monitoring system in patients
     with advanced heart failure: The Weight Monitoring in Heart Failure (WHARF)
     trial. American Heart Journal. 2003; 146(4): 705–712. pmid:14564327
     * View Article
     * PubMed/NCBI
     * Google Scholar
 43. 43. GESICA Investigators. Randomised trial of telephone intervention in
     chronic heart failure: DIAL trial. British Medical Journal. 2005;
     331(7514): 425. pmid:16061499
     * View Article
     * PubMed/NCBI
     * Google Scholar
 44. 44. Kielblock B, Frye Ch, Kottmair S, Hudler Th, Siegmund-Schultze E,
     Middeke M. Impact of telemetric management on overall treatment costs and
     mortality rate among patients with chronic heart failure. 2007; 132:
     417–422. pmid:17315117
     * View Article
     * PubMed/NCBI
     * Google Scholar
 45. 45. Koehler F, Winkler S, Schieber M, Sechtem U, Stangl K, Böhm M, et al.
     Impact of remote telemedical management on mortality and hospitalizations
     in ambulatory patients with chronic heart failure: The telemedical
     interventional monitoring in heart failure study. Circulation. 2011; 123:
     1873–1880. pmid:21444883
     * View Article
     * PubMed/NCBI
     * Google Scholar
 46. 46. Tonkin A, Yallop J, Driscoll A, Forbes A, Croucher J, Chan B, et al.
     Does telephone support of the rural and remote patient with heart failure
     improve clinical outcomes? Results of the chronic heart failure assistance
     by telephone (CHAT) study. Heart, Lung and Circulation. 2009; 18S: S105.
     * View Article
     * Google Scholar
 47. 47. Krumholz HM, Amatruda J, Smith GL, Mattera JA, Roumanis SA, Radford MJ,
     et al. Randomized trial of an education and support intervention to prevent
     readmission of patients with heart failure. Journal of the American College
     of Cardiology. 2002; 39: 83–89. pmid:11755291
     * View Article
     * PubMed/NCBI
     * Google Scholar
 48. 48. Laramee AS, Levinsky SK, Sargent J, Ross R, Callas P. Case management
     in a heterogeneous congestive heart failure population: A randomized
     controlled trial. Archives of Internal Medicine. 2003; 163: 809–817.
     pmid:12695272
     * View Article
     * PubMed/NCBI
     * Google Scholar
 49. 49. Mortara A, Pinna GD, Johnson P, Maestri R, Capomolla S, La Rovere MT,
     et al. Home telemonitoring in heart failure patients: The HHH study (Home
     or Hospital in Heart Failure). European Journal of Heart Failure. 2009; 11:
     312–318. pmid:19228800
     * View Article
     * PubMed/NCBI
     * Google Scholar
 50. 50. Ramachandran K, Husain N, Maikhuri R, Seth S, Vij A, Kumar M, et al.
     Impact of a comprehensive telephonebased disease management programme on
     quality-of-life in patients with heart failure. Natl Med J India. 2007;
     20:67–73. pmid:17802984
     * View Article
     * PubMed/NCBI
     * Google Scholar
 51. 51. Riegel B, Carlson B, Kopp Z, LePetri B, Glaser D, Unger A. Effect of a
     standardized nurse case-management telephone intervention on resource use
     in patients with chronic heart failure. Archives of Internal Medicine.
     2002; 162: 705–712. pmid:11911726
     * View Article
     * PubMed/NCBI
     * Google Scholar
 52. 52. Riegel B, Carlson B, Glaser D, Romero T. Randomized Controlled Trial of
     Telephone Case Management in Hispanics of Mexican Origin With Heart
     Failure. Journal of Cardiac Failure. 2006; 12 (3): 211–219. pmid:16624687
     * View Article
     * PubMed/NCBI
     * Google Scholar
 53. 53. Schwarz KA, Mion LC, Hudock D, Litman G. Telemonitoring of heart
     failure patients and their caregivers: A pilot randomized controlled trial.
     Progress in Cardiovascular Nursing. 2008; 23(1): 18–26. pmid:18326990
     * View Article
     * PubMed/NCBI
     * Google Scholar
 54. 54. Sisk JE, Hebert PL, Horowitz CR, McLaughlin MA, Wang JJ, Chassin MR.
     Effects of nurse management on the quality of heart failure care in
     minority communities: A randomized trial. Annals of Internal Medicine.
     2006; 145: 273–283. pmid:16908918
     * View Article
     * PubMed/NCBI
     * Google Scholar
 55. 55. Villani A, Malfatto G, Della Rosa F, Branzi G, Boarin S, Borghi C.
     Disease management for heart failure patients: Role of wireless
     technologies for telemedicine. The ICAROS project. G Ital Cardiol. 2007;
     8(2): 107–114. pmid:17402355
     * View Article
     * PubMed/NCBI
     * Google Scholar
 56. 56. Wade MJ, Desai AS, Spettel CM, Snyder AD, McGowan-Stackewicz V, Kummer
     PJ, et al. Telemonitoring with case management for seniors with heart
     failure. American Journal of Managed Care. 2011; 17(3): e71–e79.
     pmid:21504262
     * View Article
     * PubMed/NCBI
     * Google Scholar
 57. 57. Wakefield BJ, Ward MM, Holman JE, Ray A, Scherubel M, Burns TL, et al.
     Evaluation of home telehealth following hospitalization for heart failure:
     A randomized trial. Telemedicine and e-Health. 2008; 14(8): 753–761.
     pmid:18954244
     * View Article
     * PubMed/NCBI
     * Google Scholar
 58. 58. Woodend KA, Sherrard H, Fraser M, Stuewe L, Cheung T, Struthers C.
     Telehome monitoring in patients with cardiac disease who are at high risk
     of readmission. Heart and Lung: Journal of Acute and Critical Care. 2008;
     37: 36–45. pmid:18206525
     * View Article
     * PubMed/NCBI
     * Google Scholar
 59. 59. Zugck C, Nelles M, Frankenstein L, Schultz C, Helms T, Korb H, et al.
     Telemonitoring in chronic heart failure patients—Which diagnostic finding
     prevents hospital readmission? Herzschr Elektrohphys. 2005; 16: 176–182.
     * View Article
     * Google Scholar
 60. 60. Xiang R, Li L, Liu SX. Meta-analysis and meta-regression of telehealth
     programmes for patients with chronic heart failure. J Telemed Telecare.
     2013; 19(5):249–59. pmid:24163234
     * View Article
     * PubMed/NCBI
     * Google Scholar


Download PDF
 
 * Citation
 * XML

Print
 * Print article
 * Reprints

Share
 * Reddit
 * Facebook
 * LinkedIn
 * Mendeley
 * Twitter
 * Email
   

 

Advertisement



SUBJECT AREAS
?

FOR MORE INFORMATION ABOUT PLOS SUBJECT AREAS, CLICK HERE.

WE WANT YOUR FEEDBACK. DO THESE SUBJECT AREAS MAKE SENSE FOR THIS ARTICLE? CLICK
THE TARGET NEXT TO THE INCORRECT SUBJECT AREA AND LET US KNOW. THANKS FOR YOUR
HELP!

 * Heart failure  
   
   Is the Subject Area "Heart failure" applicable to this article? Yes No
   
   Thanks for your feedback.

 * Telemedicine  
   
   Is the Subject Area "Telemedicine" applicable to this article? Yes No
   
   Thanks for your feedback.

 * Metaanalysis  
   
   Is the Subject Area "Metaanalysis" applicable to this article? Yes No
   
   Thanks for your feedback.

 * Hospitalizations  
   
   Is the Subject Area "Hospitalizations" applicable to this article? Yes No
   
   Thanks for your feedback.

 * Electrocardiography  
   
   Is the Subject Area "Electrocardiography" applicable to this article? Yes No
   
   Thanks for your feedback.

 * Network analysis  
   
   Is the Subject Area "Network analysis" applicable to this article? Yes No
   
   Thanks for your feedback.

 * Randomized controlled trials  
   
   Is the Subject Area "Randomized controlled trials" applicable to this
   article? Yes No
   
   Thanks for your feedback.

 * Systematic reviews  
   
   Is the Subject Area "Systematic reviews" applicable to this article? Yes No
   
   Thanks for your feedback.



 * Publications
 * PLOS Biology
 * PLOS Medicine
 * PLOS Computational Biology
 * PLOS Genetics
 * PLOS Pathogens
 * PLOS ONE
 * PLOS Neglected Tropical Diseases

 *  
 * PLOS Climate
 * PLOS Digital Health
 * PLOS Global Public Health
 * PLOS Sustainability and Transformation
 * PLOS Water

 * Home
 * Blogs
 * Collections
 * Give feedback
 * LOCKSS

 * Privacy Policy
 * Terms of Use
 * Advertise
 * Media Inquiries
 * Contact

PLOS is a nonprofit 501(c)(3) corporation, #C2354500, based in San Francisco,
California, US

 




COOKIE PREFERENCE CENTER

Our website uses different types of cookies. Optional cookies will only be set
with your consent and you may withdraw this consent at any time. Below you can
learn more about the types of cookies PLOS uses and register your cookie
preferences.

Accept All Cookies


CUSTOMIZE YOUR COOKIE PREFERENCE

+Strictly Necessary

Always On
+Functional

OnOff
+Performance and Analytics

OnOff
+Marketing

OnOff

Save Selected Preferences and Close
For more information about the cookies and other technologies used by us, please
read our Cookie Policy.