Home Browse Current real world health data of telemedicine for heart failure with...
ALL Metrics
-
Views
-
Downloads
Get PDF
Get XML
Cite
Export
Track
▬
✚
Systematic Review
Revised

Current real world health data of telemedicine for heart failure with reduced ejection fraction: a systematic review and meta-analysis

[version 3; peer review: 1 approved, 1 approved with reservations]
Yohanes William
https://orcid.org/0000-0002-5580-209X
1, Tinanda Tarigan
https://orcid.org/0000-0003-3909-4465
2, Jery Chen3, Muhamad Taufik Ismail
https://orcid.org/0000-0002-8239-997X
4, Hariadi Hariawan4
Yohanes William
https://orcid.org/0000-0002-5580-209X
1, Tinanda Tarigan
https://orcid.org/0000-0003-3909-4465
2, [...] Jery Chen3, Muhamad Taufik Ismail
https://orcid.org/0000-0002-8239-997X
4, Hariadi Hariawan4
PUBLISHED 28 Apr 2025
Author details Author details
OPEN PEER REVIEW
REVIEWER STATUS

Abstract

Background

Telemedicine has improved adherence to heart failure (HF) treatment, however it has not yet been tailored specifically to address HF with reduced ejection fraction (HFrEF). Our objective is to undertake a comprehensive systematic review and meta-analysis of existing research studies that focus on telemedicine in HFrEF.

Methods

We conducted an extensive literature review encompassing trials which included outpatients with HFrEF who underwent telemedicine compared with usual care. We exclude any studies without ejection fraction data. Three bibliographic databases from PubMed, ScienceDirect, and Cochrane Library were utilized in our search from January 1999 to May 2023. The endpoints of interest included all-cause mortality, cardiovascular-related mortality, all-cause hospitalization, and HF-related hospitalization. The Cochrane risk-of-bias (RoB) and the risk of bias in non-randomized studies – of interventions (ROBINS-I) were used for non-randomized or observational studies. To quantitatively analyze the collective findings, a pooled odds ratio (OR) was computed for each outcome.

Results

Out of the initial pool of 4,947 articles, we narrowed down our analysis to 27 studies, Results showed that telemedicine significantly reduced all-cause mortality (OR: 0.65; 95% CI 0.54 – 0.78; p<0.00001), cardiovascular-related mortality (OR 0.68, 95% CI 0.58 – 0.80, p < 0.00001), and HF-related hospitalization based on number of events (OR 0.77, 95% CI 0.64 – 0.94, p = 0.009) as well as number of patients (OR 0.78, 95% CI 0.69 – 0.87, p < 0.0001).

Conclusion

Telemedicine was shown significantly beneficial in decreasing mortality and hospitalization in HFrEF patients. Future research should focus on standardizing effective telemedicine practices due to the existing variability in methods and clinical situation of the patients.

PROSPERO: CRD42023471222 registerd on October 21, 2023

Keywords

telemedicine, heart failure, reduced ejection fraction, mortality, hospitalization

Corresponding author: Muhamad Taufik Ismail
Competing interests: No competing interests were disclosed.
Grant information: The author(s) declared that no grants were involved in supporting this work.
Copyright:  © 2025 William Y 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 work is properly cited.
How to cite: William Y, Tarigan T, Chen J et al. Current real world health data of telemedicine for heart failure with reduced ejection fraction: a systematic review and meta-analysis [version 3; peer review: 1 approved, 1 approved with reservations]. F1000Research 2025, 13:616 (https://doi.org/10.12688/f1000research.146779.3) First published: 11 Jun 2024, 13:616 (https://doi.org/10.12688/f1000research.146779.1) Latest published: 28 Apr 2025, 13:616 (https://doi.org/10.12688/f1000research.146779.3)

Revised Amendments from Version 2

We added the Clinical Impact and Future Perspective part before conclusion, added some explanation in the strengths and limitations part, and emphasized more in the conclusion.

See the authors' detailed response to the review by Nisha Nambiar
See the authors' detailed response to the review by Vito Damay

Introduction

HF has been known for its major public health concern with substantial morbidity and mortality. Its 1-year mortality rate is 7.2% and 31.9% of 1-year hospitalization rate.1 In the US, approximately 1 million hospitalizations of HF, are caused by HFrEF.2 The OPTIMIZE-HF study enrolling 20,118 patients with HFrEF, reported a higher in-hospital mortality in HFrEF (3.9%) than in HF with preserved ejection fractions (HFpEF) patients (2.9%).3 In a prospective longitudinal study conducted in multiple centers in New Zealand and Singapore, 17% of patients (343 individuals) died during the two-year follow-up period. After accounting for factors such as age, sex, and clinical risk factors, it was observed that patients with HFpEF had a reduced risk of death in comparison to those with HFrEF (HR 0.62, 95% CI 0.46–0.85, p = 0.003).4 In a cohort of 4880 individuals from China, a 5-year follow-up until the end of December 2019 revealed a decrease in ejection fraction category was identified as an independent factor associated with an elevated 5-year mortality risk. Specifically, individuals with HFrEF had a mortality rate of 25.2%, while those with HFpEF exhibited a lower rate at 13.4% (adjusted hazard ratio (aHR) 1.85, CI 95% 1.45-2.35, p <0.001).5

Despite the proven efficacy of guideline-directed medical therapy (GDMT) in reducing morbidity and mortality, a significant number of eligible patients diagnosed with HFrEF do not receive one or more of the recommended medications. This shortfall is frequently attributed to suboptimal initiation and titration practices in the outpatient setting. After discharge, the continuation of progress and the safety of initiating and adjusting GDMT in the hospital relies on the successful transition of care to the outpatient setting.6

The COVID-19 pandemic had brought about a positive outcome in the form of the widespread adoption of telemedicine. This approach, involving the remote delivery of optimal diagnostic and therapeutic services, has proven particularly beneficial for patients with HF during the pandemic. The effectiveness of telemedicine is enhanced through intensive monitoring and more frequent transmission of patient data. This not only improves patient outcomes but also minimizes the risk of virus exposure for healthcare workers.7 The challenge of achieving GDMT can be alleviated by incorporating telemedicine which has proven some beneficial effects. A systematic review conducted by Yun et al. revealed a 19% reduction in all-cause mortality through telemedicine.8 Specifically, several studies have confirmed the enduring benefits of telemedicine for individuals with HFrEF, particularly in terms of reducing mortality and hospitalization rates. A RCT conducted over a 120-month period demonstrated a significant and prolonged decrease in all-cause mortality, underscoring the lasting positive effects of telemedicine.9 In the most recent systematic review and meta-analysis by Scholte et al., a noteworthy 16% decrease in all-cause mortality was observed. Additionally, there were significant reductions of 19% in first HF hospitalization and 15% in total HF hospitalization.10 It is important to note, however, that none of these studies distinguished HF based on ejection fraction.

We aim to dig deeper to answer the question of whether it is truly beneficial for patients with reduced ejection fraction, who clearly show higher frailty with a higher risk of adverse clinical outcomes.11

Methods

Literature search strategy

We conducted an extensive and systematic literature review based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, encompassing trials spanning from January 1999 to May 2023.12 Three bibliographic databases from PubMed, ScienceDirect, and Cochrane Library were utilized in our search. Our study was registered in PROSPERO: CRD42023471222 on October 21, 2023. The search terms were “heart failure”, “HF”, “reduced”, “ejection fraction”, “HFrEF”, “telemonitoring”, “telemedicine”, “telemedical”, “telecardiology”. The PRISMA flow diagram is displayed in Figure 1 in data availability section.

fef34c36-50a1-4ec1-b6a6-8a47e61164b2_figure1.gif

Figure 1. Preferred reporting items for Systematic review and meta-analysis flow-chart.

Eligibility criteria

The inclusion criteria are trials/studies which include HFrEF patients or studies with mentioned ejection fraction of ≤40%, with consistent outcome of all-cause mortality, cardiovascular-related mortality, all-cause hospitalization, or HF-related hospitalization. We included any form of telemedicine or telemonitoring such as home telemonitoring (HTM) or telephone support (TS). Any forms of abstracts without full-text articles, case reports, expert opinions, conference presentations, preclinical studies, or whether the articles were not available in English, were excluded from our study.

Study selection

All articles retrieved through the systematic search underwent initial screening of their titles and abstracts by two authors and were subsequently grouped into a single folder. The same authors then conducted a thorough examination of the full texts to determine whether the articles met the eligibility criteria or not.

Data extraction

We included all-cause mortality as our primary outcome, where HF-related mortality, all-cause hospitalization, and HF-related hospitalization as our secondary outcomes. All-cause hospitalization and HF-related hospitalization were both extracted based on number of events (per patient-year) and number of patients. Patient-year was calculated as follows: the total number of patients who participated in the study is multiplied by the total years of follow-up. Studies involving more than two telemedicine interventions were divided into separate studies within the forest plot and meta-analysis. In the table outlining baseline characteristics, we incorporated information on NYHA classification, ejection fraction, follow-up duration in months, study design, participant age, monitoring methods, telemedicine types, and usual care modalities. These data were extracted from the supplementary tables and figures provided in the articles. Two independent investigators each collected the data from the retrieved article. In cases where data interpretation was inconclusive, we sought the input of our third author and reached a consensus. Articles that satisfied the eligibility criteria but lacked the raw data necessary for meta-analysis were excluded from the study. Table 1 presents the fundamental feature of each study, while Table 2 provides a summary of the results and subgroup analysis.

Table 1. Baseline characteristics of included studies.

No First Author, Year NYHA EF (%) Time of follow-up (Months) Methods of monitoring Design Age Time of follow-up (Months) Methods of monitoring Types of telemonitoring Types of usual care
1Angermann, 2012 (INH)24I-IV ≤401.5HeartNetCare: (1) in-hospital face-to-face contact; (2) telephone-based structured monitoring using a standardized 19-item questionnaire; (3) uptitration of heart failure medication in cooperation with GPs, where possible, and teaching of patients regarding adjustment of diuretics; (4) needs-adjusted specialist care, which nurses coordinated with patients’ physician(s); (5) Measures for appropriate education and supervision of interveners to ensure high intervention quality.RCT68.6 ± 12.21.5Non InvasiveTSMIn-clinic follow up
2Angermann, 2023 (E-INH)9II-III ≤4060-120HeartNetCare: (1) in-hospital face-to-face contact; (2) telephone-based structured monitoring using a standardized 19-item questionnaire; (3) uptitration of heart failure medication in cooperation with GPs, where possible, and teaching of patients regarding adjustment of diuretics; (4) needs-adjusted specialist care, which nurses coordinated with patients’ physician(s); (5) Measures for appropriate education and supervision of interveners to ensure high intervention quality.RCT70 (60-77)60, 120Non InvasiveTSMIn-clinic follow up
3Antonicelli, 200825II-IV <4012Subjects of the control group were contacted monthly by telephone to collect data on new hospital admissions, cardiovascular complications and death.RCT78 ± 8.512Non InvasiveCombinationIn-clinic follow up and contact once a month from the team
4Bohm, 201617II-III ≤3518Intrathoracic fluid index threshold crossing (FTC).RCT66.3 ± 10.418InvasiveHTMIn-clinic follow up
5Chiu, 2021 (REMOTE-CIED)18II-III ≤3512Clinical nursing specialist-led telephone consultations 48 – 72 h after initial discharge and subsequently at 2-weekly intervals.RCT65 (59–73)12InvasiveHTMIn-clinic follow up
6Cleland, 2005 (TEN-HMS)26I-IV <4015Low-profile, electronic, weighing scales, an automated sphygmomanometer, and a single-lead electrocardiogram using wrist-band electrodes.RCT67.2 ± 11.615Non InvasiveCombinationIn-clinic follow up
7DeWalt, 200627II-IV <4012The program coordinator made scheduled follow-up phone calls (days 3, 7, 14, 21, 28, 56) and monthly during months 3–6.RCT62.5 ± 10.112Non InvasiveTSIn-clinic follow up and brochure
8Domingues, 201128not clear≤453Systematic telephone contacts for a three-month investigational period (intervention group - IG) vs out-patient visit with telephone contacts.RCT63 ± 133Non InvasiveTSIn-clinic follow up
9Gattis,1999 (PHARM)29I-IV <456Telephone follow-up at 2, 12, and 24 weeks after the initial clinic visit to identify problems with drug therapyRCTNA6Non InvasiveTSIn-clinic follow up
10Giordano, 200930II-IV <4012Home-based telemanagement by medical/nursing interventions made over the telephone, with the possibility to transmit an ECG trace to a workstation.RCT57 ± 1012Non InvasiveCombinationIn-clinic follow up
11Goldberg, 2003 (WHARF)31III-IV ≤356Electronic scale placed in patients’ homes and an individualized symptom response system (DayLink monitor) linked via a standard phone line using a toll-free telephone number to a computerized database monitored by trained cardiac nurses.RCT59.1 ± 15.36Non InvasiveHTMIn-clinic follow up and feature to contact their physicians
12Hansen, 2018 (InContact)19I-III ≤3512Implanted Cardioverter Defibrillators SysTems, quarterly automated follow-up only (telemetry group) were compared to those receiving personal physician contact.RCT63.8 ± 11.112InvasiveCombinationIn-clinic follow up or TS
13Hindricks, 2014 (IN-TIME)20II-III ≤3512Lumax dual-chamber ICD or CRT-D, Monitoring function. At a set time every day (typically 0300 h) or on detection of tachyarrhythmia, the devices transmitted cumulative and last-saved diagnostic.RCT65.5 ± 9.412InvasiveCombinationIn-clinic follow up
14Koberich, 201532II-IV ≤403Consecutive telephone follow-up over three months.RCT61.7 ± 12.03Non InvasiveTSIn-clinic follow up
15Koehler, 201134II-III ≤3512RTM using portable devices for ECG, blood pressure, and body weight measurements and sent to telemedical centre.RCT66.9 ± 10.612Non InvasiveHTMIn-clinic follow up
16Krum, 2013 (CHAT)35II-IV <4012Usual care (UC) or UC and telephone support intervention (UC+I).RCT73.0 ± 10.512Non InvasiveTSIn-clinic follow up
17Kurek, 2017 (COMMIT-HF)14not clear≤3536Implanted ICD/CRTD, The RM group included the 121 patients with varying manufacturer.OBSNA36InvasiveHTMIn-clinic follow up
18Landolina, 2012 (EVOLVO)21I-III ≤3516Data transmission through ICD, without patients intervention.RCTNA16InvasiveHTMAudible notification of ICD and in-clinic follow up
19Mo, 2021 (HHH)15II-IV ≤4012Standard care plus telehealth intervention in which participants were called by registered nurses.OBS53.1 ± 11.412Non InvasiveTSIn-clinic follow up
20Mortara, 200933I-IV ≤4012Monthly telephone contact + weekly vital signs transmission + monthly 24h recording of cardiorespiratory activity.RCT60 ± 1212Non InvasiveCombinationIn-clinic follow up
21Nunes-ferreria, 202016I-III ≤4012Prospective TM programme, prospective HF protocol follow-up programme (PFP) with no TM facilities, and retrospective propensity-matched usual care (UC).OBS65.9 ± 11.912Non InvasiveCombinationProtocol based or in-clinic follow up
22Sardu, 2016 (TELECART)22II-III <3512CRT-D as telemonitoring device and transmitted data were reviewed by independent investigators.RCT72.2 ± 7.212InvasiveCombinationIn-clinic follow up
23Soran, 200836II-III ≤406Electronic scale and an individualized symptom response system (DayLink monitor) linked via a standard phone line to a computerized database.RCT76 ± 76Non InvasiveHTMIn-clinic follow up
24Tajstra, 2020 (RESULT)23II-IV ≤3512Available ICD or CRT manufactured by four companies, equipped with remote monitoring systems.RCTNA12InvasiveCombinationIn-clinic follow up
25Villani, 201437III-IV ≤4012Telemonitoring technology comprised a patient front- end, a medical front-end and a web-based system for assisting with clinical decisions.RCT72 ± 312Non InvasiveHTMIn-clinic follow up
26Völler, 2022 (CardioBBEAT)38II-IV ≤4012Standard of Care (SoC) enriched by RTM connecting them wirelessly to the participating care providers using the telemedicine platform Motiva and transferred to the hospital.RCT63.0 ± 11.512Non InvasiveCombinationIn-clinic follow up
27Wita, 202239II-III not clear24Self-monitoring using a telemonitoring set: a blood pressure (BP) monitor, a 3-lead electrocardiogram (ECG) recorder, a weighing machine, and a personal tablet.RCT66.1 ± 10.524Non InvasiveHTMIn-clinic follow up

Table 2. Summary of the outcomes associated with both the non-invasive and invasive approach.

Subgroup analyses Total
Non-invasive Invasive
OR, 95% CIP-value I2OR, 95% CI P-value I2 OR, 95% CIP-value I2
All-cause mortality0.69, 0.59 – 0.80<0.000010%0.70, 0.42 – 1.150.1673%0.65, 0.54 – 0.78<0.0000141%
Sensitivity analyses<6 months0.56, 0.37 – 0.850.0070%(not enough studies)
6-11 months0.49, 0.29 – 0.810.0060%
≥12 months0.74, 0.62 – 0.870.00030%
Cardiovascular-related mortality0.69, 0.58 – 0.82<0.00010%0.63, 0.36 – 1.080.0951%0.68, 0.58 – 0.80<0.000017%
Sensitivity analyses<6 months0.60, 0.37 – 0.97*0.040%(not enough studies)
6-11 months0.44, 0.22 – 0.91*0.030%
≥12 months0.73, 0.60 – 0.880.0010%
All-cause hospitalizationnumber of events0.68, 0.48 – 0.940.0279%(not enough studies)0.73, 0.54 – 1.000.0578%
number of patients0.83, 0.66 – 1.040.1168%0.85, 0.69 – 1.030.166%
Sensitivity analyses (number of patients)<6 months1.11, 0.83 – 1.48*0.470%(not enough studies)
6-11 months1.19, 0.76 – 1.86*0.44NA
≥12 months0.76, 0.57 – 1.000.0572%
HF-related hospitalizationnumber of events0.77, 0.62 – 0.950.0158%(not enough studies)0.77, 0.64 – 0.940.00961%
number of patients0.77, 0.65 – 0.900.00115%0.80, 0.65 – 0.970.030%0.78, 0.69 – 0.87<0.00010%
Sensitivity analyses (number of patients)<6 months0.79, 0.49 – 1.25*0.31NA(not enough studies)
6-11 months1.26, 0.73 – 2.18*0.41NA
≥12 months0.73, 0.62 – 0.860.00027%

* =studies included in this analysis were less than 3 studies; CI=confidence interval; HF=heart failure; I2=heterogeneity; NA=not applicable; OR=odds ratio.

Quality assessment

We employed Version 2 of the Cochrane risk-of-bias tool for randomized trials (RoB 2) as the recommended tool for evaluating the risk of bias in randomized trials. Two authors independently evaluated the articles for risk-of-bias, and if any author encountered confusion, a third author was consulted to establish a consensus.

Statistical analysis

We employed a forest plot to effectively present the outcomes of our primary endpoints. Specifically, we will use forest plots for binary endpoints, accompanied by 95% confidence intervals (CI). To assess heterogeneity among the studies, we will utilize the I2 statistic. I2 values ranging from 0% to 25% will be labeled as “low,” 25% to 50% as “moderate,” and >50% as “high”.13 Subgroup analysis of non-invasive and invasive group was conducted to explore possible causes of heterogeneity. Any p-values below 0.05 will be considered statistically significant. All statistical analyses will be carried out using Review Manager (RevMan). The odds ratio was calculated to evaluate the risk of mortality and hospitalization in the available studies. Mortality was calculated based on the total number of patients, while hospitalization was calculated based on the numbef of events of total patient-year and number of patients. Sensitivity analyses were carried out based on different follow-up durations (< 6 months, 6-11 months, and ≥ 12 months) to assess the strength of the study results.

Results

Selected studies

We conducted an extensive literature review encompassing trials spanning from 1999 to the present. Out of the initial pool of 4,947 articles, we narrowed down our analysis to 53 studies, in which our two main authors each reviewed the full-text articles. We excluded 16 articles because of the numerous reasons mentioned in our PRISMA figure ( Figure 1). Finally, 27 studies were included in qualitative and quantitative synthesis. Among all of the studies which compared usual care and telemedicine, we found 24 articles reported all-cause mortality and 14 articles reported cardiovascular-related mortality. For all-cause hospitalization, there are 5 studies which reported all-cause hospitalization based on number of events while 13 studies reported with total number of patients. In HF-related hospitalization, 8 studies reported HF-related hospitalization based on number of events, and 16 studies reported with total number of patients. We also conducted subgroup analysis of non-invasive and invasive approach, as well as sensitivity analyses with the aim of attaining a lower level of heterogeneity. Table 2 provides a summary of the included studies and the outcomes associated with subgroup analyses (non-invasive and invasive approache) as well as the sensitivity analyses ( Table 2).

Risk of bias

We evaluated the quality of our 24 RCT studies using the Cochrane RoB 2 tool ( Figure 2), while the 3 observational studies were assessed using the ROBINS-I tool ( Figure 3). For RCT studies, the overall bias with the RoB 2 tool was distributed as follows: 37.5% at low risk, 50% with some concerns, and 12.5% at high risk ( Figure 4). High-risk studies were primarily attributed to poorly described randomization processes. The overall quality of the observational studies by ROBINS-I tool was assessed as moderate risk, primarily within domain 1 bias (bias due to confounding). This was because the studies did not explicitly mention confounding factors or adjustments made for them.

fef34c36-50a1-4ec1-b6a6-8a47e61164b2_figure2.gif

Figure 2. The risk of bias assessment using the version 2 of the Cochrane risk-of-bias tool for the randomized trial (RoB 2).

fef34c36-50a1-4ec1-b6a6-8a47e61164b2_figure3.gif

Figure 3. The risk of bias assessment in non-randomized studies – of intervention (ROBINS-I).

fef34c36-50a1-4ec1-b6a6-8a47e61164b2_figure4.gif

Figure 4. Summary of the risk of bias assessment using the version 2 of the Cochrane risk-of-bias tool of randomized trials (RoB 2).

General characteristics of included trials

The general characteristics of the population, type of interventions and control group were included in Table 1 along with the primary and secondary outcomes. The lowest time of follow-up was 1.5 months and the longest time of follow-up was 120 months.9,14 Of the 27 studies included, there are 3 studies which were not randomized.15–17 8 studies used implanted devices either it is implantable-cardiac-device (ICD) or cardiac-resynchronization-therapy device (CRT-D).15,18–24 The other 19 studies used non-invasive devices.9,14,16,17,25–39 While most of the usual care/control groups were categorized for in-clinic follow-up only, there were 3 studies which allowed for the patients in the control group to occasionally contact their physician/nurse.25,31,33 All of the noninvasive interventions monitored the patients using standard telephone/telemonitored/telehealth features interactively. Most reported outcomes included all-cause mortality, cardiovascular-related mortality, all-cause hospitalization, and HF-related hospitalization.

All-cause mortality and cardiovascular-related mortality

Twenty-four studies reported all-cause mortality, involving a total of 9,737 participants, while 14 studies presented data on cardiovascular-related mortality, involving a total of 7,477 participants (studies with more than 2 intervention arms were divided). The meta-analysis revealed a noteworthy decrease in the risk of all-cause mortality (OR 0.65, 95% CI 0.54 – 0.78, p < 0.00001, I2 = 41%) ( Figure 5) and a significant reduction in the risk of cardiovascular-related mortality (OR 0.68, 95% CI 0.58 – 0.80, p < 0.00001, I2 = 7%) ( Figure 6).

fef34c36-50a1-4ec1-b6a6-8a47e61164b2_figure5.gif

Figure 5. Forest plot and meta-analysis comparison of all-cause mortality based on number of incidents per total patients in telemedicine and usual care group.

(HTM=home telemonitoring; TS=telephone support).

fef34c36-50a1-4ec1-b6a6-8a47e61164b2_figure6.gif

Figure 6. Forest plot and meta-analysis comparison of cardiovascular-related mortality based on number of incidents per total patients in telemedicine and usual care group.

(HTM=home telemonitoring; TS=telephone support).

All-cause hospitalization and HF-related hospitalization

In two analyses focusing on all-cause hospitalization, one based on number of events and the other on the number of patients admitted. Findings from 5 studies totaling 7,302 patient-years reported incidents of hospitalization based on number of events ( Figure 7; Figure 8). However, these results did not show a significant reduction in the risk of all-cause hospitalization based on number of events (OR 0.73, 95% CI 0.54 – 1.00, p = 0.05, I2 = 78%) ( Figure 7). Thirteen studies with total participants of 5,731 mentioned studies of number of patients who went for admission for all-cause, also with insignificant reduction of the risk (OR 0.85, 95% CI 0.69 – 1.03, p = 0.10, I2 = 66%) ( Figure 8). Eight studies with total patient-year of 9874 reported HF-related hospitalization based on number of events with no significant reduction of the risk (OR 0.77, 95% CI 0.64 – 0.94, p = 0.009, I2 = 61%) ( Figure 9). Sixteen studies with total number of patients 7478, reported HF-related hospitalization based on total number of patients who went for admission, with significant reduction of the risk (OR 0.78, 95% CI 0.69 – 0.87, p < 0.0001, I2 = 0%) ( Figure 10).

fef34c36-50a1-4ec1-b6a6-8a47e61164b2_figure7.gif

Figure 7. Forest plot and meta-analysis comparison of all-cause hospitalization based on number of events per total patient-year in telemedicine and usual care group.

(HTM=home telemonitoring; TS=telephone support).

fef34c36-50a1-4ec1-b6a6-8a47e61164b2_figure8.gif

Figure 8. Forest plot and meta-analysis comparison of all-cause hospitalization based on number of patients per total patient in telemedicine and usual care group.

(HTM=home telemonitoring; TS=telephone support).

fef34c36-50a1-4ec1-b6a6-8a47e61164b2_figure9.gif

Figure 9. Forest plot and meta-analysis comparison of HF-related hospitalization based on number of events per patient-year in telemedicine and usual care group.

(HTM=home telemonitoring; TS=telephone support).

fef34c36-50a1-4ec1-b6a6-8a47e61164b2_figure10.gif

Figure 10. Forest plot and meta-analysis comparison of HF-related hospitalization based on number of patients as per total patients in telemedicine and usual care group.

(HTM=home telemonitoring; TS=telephone support).

fef34c36-50a1-4ec1-b6a6-8a47e61164b2_figure11.gif

Figure 11. Forest plot and subgroup meta-analysis (non-invasive and invasive) of all-cause mortality based on number of incidents per total patients in telemedicine and usual care group.

(HTM=home telemonitoring; TS=telephone support).

fef34c36-50a1-4ec1-b6a6-8a47e61164b2_figure12.gif

Figure 12. Forest plot and subgroup meta-analysis (non-invasive and invasive) of cardiovascular-related mortality based on number of incidents per total patients in telemedicine and usual care group.

(HTM=home telemonitoring; TS=telephone support).

fef34c36-50a1-4ec1-b6a6-8a47e61164b2_figure13.gif

Figure 13. Forest plot and subgroup meta-analysis (non-invasive and invasive) of all-cause hospitalization based on number of events per patient-year telemedicine and usual care group.

(HTM=home telemonitoring; TS=telephone support).

fef34c36-50a1-4ec1-b6a6-8a47e61164b2_figure14.gif

Figure 14. Forest plot and subgroup meta-analysis (non-invasive and invasive) of all-cause hospitalization based on number of patients in telemedicine and usual care group.

(HTM=home telemonitoring; TS=telephone support).

fef34c36-50a1-4ec1-b6a6-8a47e61164b2_figure15.gif

Figure 15. Forest plot and subgroup meta-analysis (non-invasive and invasive) HF-related hospitalization based on number of events per patient-year telemedicine and usual care group.

(HTM=home telemonitoring; TS=telephone support).

fef34c36-50a1-4ec1-b6a6-8a47e61164b2_figure16.gif

Figure 16. Forest plot and subgroup meta-analysis (non-invasive and invasive) HF hospitalization based on number of patients per total patients in telemedicine and usual care group.

(HTM=home telemonitoring; TS=telephone support).

The subgroup analyses

We performed a subgroup analysis comparing non-invasive and invasive approaches in terms of all-cause mortality. The non-invasive approach showed a significantly lower result (OR 0.69, 95% CI 0.59 – 0.80, p < 0.00001, I2 = 0%), whereas the invasive approach did not yield a significant result (OR 0.70, 95% CI 0.42 – 1.15, p = 0.16, I2 = 73%) ( Figure 11). In the non-invasive subgroup analysis of cardiovascular-related mortality, a lower significant outcome was observed (OR 0.69, 95% CI 0.58 – 0.82, p = 0.0001, I2 = 0%), while the invasive approach showed a similar outcome with no significant result (OR 0.63, 95% CI 0.36 – 1.08, p = 0.09, I2 = 51%) ( Figure 12). The sensitivity analyses of the all-cause mortality in non-invasive group achieved significant result, similar with the main analysis (see Extended data section for supplementary figures1-6). We were not able to conduct the same sensitivity analysis in the invasive group due to the lack of number of the studies.

We also conducted subgroup analyses for both non-invasive and invasive approaches, focusing on all-cause hospitalization events and the number of patients experiencing all-cause hospitalization and HF-related hospitalization. The non-invasive subgroup analysis of all-cause hospitalization based on the number of events yielded a significant result, albeit with high heterogeneity (OR 0.68, 95% CI 0.48 – 0.94, p = 0.02, I2 = 79%) ( Figure 13), while the non-invasive subgroup analysis of all-caused hospitalization based on the number of patients did not have any significant result (OR 0.83, 95% CI 0.66 – 1.04, p = 0.11, I2 = 68%) ( Figure 14). The non-invasive subgroup analysis of HF-related hospitalization based on the number of events showed a significant result (OR 0.77, 95% CI 0.62 – 0.95, p = 0.01, I2 = 58%) ( Figure 15). Unfortunately, the invasive subgroup analyses of all-cause hospitalization and HF-related hospitalization based on number of events could not be conducted due to a low number of studies. When considering the number of patients, the non-invasive subgroup analysis of HF-related hospitalization demonstrated a significant result (OR 0.77, 95% CI 0.65 – 0.90, p = 0.001, I2 = 15%), and a similar result was observed in the invasive subgroup analysis (OR 0.80, 95% CI 0.65 – 0.97, p = 0.03, I2 = 0%) ( Figure 16).

The sensitivity analysis

We attempted to conduct a sensitivity analysis for all-cause hospitalization by the number of patients and for HF-related hospitalization in the non-invasive group. The results indicated significance in studies with a follow-up period of ≥12 months in HF-related hospitalization, similar with the main analysis, but not in the all-cause hospitalization (see extended data: Supplementary figure 9; Supplementary figure 10). There were not enough studies with follow-up periods of < 6 and 6 – 12 months in non-invasive group in both all-cause and HF-related hospitalization, therefore the analyses were considered invalid. We also were not able to conduct the same sensitivity analysis in hospitalization based on number of events in non-invasive group, and in all of the outcomes in the invasive group, due to the same reasoning.

Discussion

Telemedicine in HFrEF

As the emphasis of contemporary healthcare is progressively transitioning from treating illnesses to proactively preventing them and supporting recovery, remote monitoring and telemedicine are poised to become pivotal areas of interest for both scientific research institutions and manufacturers in the coming years.40 The first definition was proposed by the World Health Organization (WHO) in 2007 as follows, “The delivery of health care services, where distance is a critical factor, by all health care professionals using information and communication technologies for the exchange of valid information for diagnosis, treatment and prevention of disease and injuries, research and evaluation, and for the continuing education of health care professionals, all in the interests of advancing the health of individuals and their communities”.41

HF is a common, debilitating, unpredictable, gradually worsening condition with a poor outlook, which places a substantial strain on resources and finances. Objectives in managing HF encompass enhancing symptoms, functional capabilities, quality of life, and patient self-efficacy, while also aiming to lower hospital admissions, mortality rates, and the associated logistical and financial burdens.42 Despite recent pharmacological and therapy advancements, HF, particularly with reduced ejection fraction confers a high morbidity and mortality worldwide.43 The primary objective of remote monitoring for HF is to identify early signs of HF hemodynamic deterioration, enabling timely intervention and thereby preventing HF-related hospitalizations. Therefore, telemedicine might offer more frequent monitoring of patients’ status remotely and could be used as a tool to improve outcomes.41,44

Our meta-analysis, which comprises 27 studies focused on telemedicine in HFrEF suggests that telemedicine system significantly reduced all-cause mortality and cardiovascular-related mortality, in either main analysis, subgroup analyses, and sensitivity analyses. Our analysis also showed significant result in HF-related hospitalization in the main analysis. Previous meta-analysis of RCTs which included 29 RCTs showed that telemedicine significantly reduced all-cause hospital admission (OR 0.82, 95% CI 0.73-0.91, p <0.00001) and cardiac hospital admission (OR 0.83, 95% CI 0.72-0.95, p =0.003).44 Different from the meta-analysis of Zhu et al., we included both RCT and Non-RCT studies and specifically assessed HFrEF patients. Telemedicine via remote telemedicine (RTM) offers patients a structured approach to managing their health conditions and can empower them to take control of their well-being. The primary focus of telemedicine is the timely identification of deteriorating health conditions and the swift implementation of medical interventions.45

Telemedicine’s impact on mortality in patients with HFrEF

The management of HFrEF has advanced throughout the years.46 Despite well-established evidences and guidelines regarding GDMT, its implementation is still poor in clinical practice.47,48 The major challenges are concern of side effects and inadequate patients’ follow up. Close-monitoring of vital signs, weight and patients’ symptoms, are important to guide dose titration of GDMT medications, which may be difficult to obtain during daily out-patient visits, including for patients with HFrEF. Therefore, telemedicine strategy could become useful in tackling this obstacle.45 A pilot study of DAVID-HF trial by Wong et al (2022) showed that daily ambulatory remote monitoring system using arm-band monitor may be feasible for drug dose escalation in HFrEF.49 Other meta-analyses also have indicated that telemedical monitoring for chronic HF can lead to a decrease in overall mortality over a 6 to 12-month follow-up period.50,51 Findings from the Trans-European Network-Home-Care Management System (TEN-HMS) Study demonstrated that HF patients who were assigned to telemedicine immediately after being discharged from the hospital for HF experienced lower mortality compared to patients in the usual care group.26,52 These findings align with our sensitivity analysis, revealing significant results for all-cause and cardiovascular-related mortality across different follow-up durations (< 6 months, 6 – 12 months, and ≥ 12 months) (extended data: Supplementary figure 7; Supplementary figure 8). This suggests that telemedicine is beneficial for both short-term and long-term mortality outcomes. In the period preceding active promotion of GDMT, telemedicine emerged as a valuable tool, facilitating continuous assessment of treatment efficacy. This encompassed the monitoring of essential parameters such as body weight, urine output, blood pressure, electrocardiogram (ECG), heart rate, and the potential manifestation of side effects.25

The IN-TIME trial revealed three potential mechanisms that could enhance clinical outcomes and reduce overall mortality. These include the timely identification of life-threatening tachyarrhythmia, prompt recognition of suboptimal ICD/RCT-D function, and increased awareness among patients regarding the worsening of symptoms.21 A significant majority, around 80%, of patients experiencing HFrEF will exhibit frequent and intricate ventricular arrhythmias. Particularly in cases of HFrEF, these ventricular arrhythmias can adversely impact cardiac hemodynamics, worsening the HF syndrome. The timely identification of these arrhythmias is crucial as it can lead to more favorable clinical outcomes and mortality.53

In contrast, individual studies of the TIM-HF and the Tele-HF trial did not confer similar significant results and diverged from those of the TEN-HMS study and the pooled analyses.54,55 This variation could be explained by the diverse nature of studies on RTM that include HF patients with different risk profiles, varying durations of follow-up, and variations in RTM methodologies across trials. Furthermore, the meta-analyses and trials which showed significant results primarily encompassed a combination of telephone-based monitoring (with or without personal intervention) and technology-assisted monitoring reliant on information.25,26,52

Telemedicine’s impact on hospitalization in patients with HFrEF

In principle, the use of telemedicine is anticipated to reduce hospitalization rates among patients with HFrEF. Nevertheless, the outcomes from several trials have been varied. We uncovered a substantial overall decrease in HF-related hospitalization, but this reduction was not observed in all-cause hospitalization. To further scrutinize and challenge the analysis, we attempted a more detailed examination. Interestingly, the decrease in all-cause hospitalizations only exhibited statistical significance in the non-invasive group when considering the number of events. On the contrary, HF-related hospitalization consistently demonstrated significant results in the non-invasive group, particularly in sensitivity analyses for the ≥ 12 months group based on the number of patients. Unfortunately, the most of the analysis in the invasive group lacked a sufficient number of studies, but not in the HF-related hospitalization based on number of patients. The most reliably observed trend in terms of hospitalization is the decrease in all-cause hospitalizations based on number of events within the non-invasive group.

Giordano et al. reported a 36% decrease in total hospital readmissions and a 31% decrease in hemodynamic instability, with significantly lower HF hospitalization compared to the usual care group, similar to our main analyses of HF-related hospitalization.30 Conversely, Cleland et al. found no difference in the number of events between the telemedicine and usual care groups.26 The IN-CONTACT trial, which included telemetry, remote + phone, and usual care groups, reported no significant differences in hospitalization rates across all intervention arms.20 These findings align with earlier RCTs, such as the EVOLVO trial. In the EVOLVO trial, a combination of early issue detection through telemedicine and remote management proved more effective in reducing both the duration of hospitalization and mortality, rather than simply decreasing the number of hospitalizations.22 This discrepancy may be attributed to the occurrence of multiple hospitalization events in a single individual as well as low number of studies. Additionally, variations in baseline characteristics of HF patients across studies contribute to the differences, with Giordano et al. including NYHA class IV patients, while the IN-CONTACT trial and EVOLVO trial focused on NYHA class I-III patients, potentially explaining the higher hospitalization rates observed by Giordano et al.20,22,30 In terms of all-cause hospitalization, we could only pinpoint five studies with diverse methodologies and varying risk profiles of patients, all of which considered the number of events as an outcome. This diversity might explain the non-significant results observed in terms of all-cause hospitalization based on number of events ( Figure 7).

In both main analysis and non-invasive all-cause hospitalization based on number of patients, no significant results were attained ( Figure 8; extended data: Supplementary figure 9). However, we identified a significant favorable outcome in the sensitivity analysis of HF-related hospitalization based on number of patients, especially in the context of long-term outcomes in non-invasive group (≥ 12 months) (extended data: supplementary figure 10). Our research yielded results consistent with the findings of the Scholte et al. study, showing a significant decrease in hospitalization rates among patients, referred to as first hospitalization or in our study as hospitalization based on number of patients.10 Similar to their study, we observed that most studies demonstrating significant outcomes utilized either complex telemedicine or a combination of structured telephone and HTM. In the study conducted by Giordano et al, they employed a combined strategy, allowing patients to connect with predetermined nurses anytime and anywhere.30 Notably, they reported that the readmission curve did not diverge significantly until at least 100 days, possibly indicating a learning curve among staff in managing patients with telemonitoring. This observation may contribute to the varied prognosis seen in studies with longer follow-up periods. In contrast, we firmly believe, based on the study by Nunes-Ferreira et al., that the core of a successful telemonitoring program lies in the synergy of monitoring and regularly collecting biodata. Additionally, ensuring good patient adherence and compliance emerges as a fundamental key to the success of telemonitoring initiatives.17

Certain observational studies consistently demonstrate a reduction in hospitalization rates, specifically in arrhythmia patients. Piccini et al. conducted such a study and asserted that telemonitoring could alleviate the burden of hospitalization through the early detection of arrhythmia, lead malfunction, or device failure.56 They specifically highlighted the benefits for patients previously diagnosed with atrial fibrillation (AF), noting fewer hospitalizations for stroke, improved adherence to oral anticoagulation, and prompt detection of AF resulting in fewer transient ischemic attacks or strokes.56–58 These results might explain the significant result we had in hospitalizations based on number of events in the non-invasive group, which we further explain in the non-invasive approach section.

Non-invasive approach

Non-invasive home telemonitoring includes HTM, TS, and complex telemonitoring using a combination of TS and/or 24-h call centre.

In our meta-analysis, we discovered notable benefits associated with a non-invasive approach in terms of all-cause mortality, cardiovascular-related mortality, all-cause and HF hospitalization based on the number of events, as well as HF hospitalization based on the number of patients. The sensitivity analyses also showed similar result with the main analysis (extended data: Supplementary figure 7 – 10). This study is, to our knowledge, the first comprehensive meta-analysis to showcase the advantages of a non-invasive approach for patients with HFrEF. Notably, Scholte et al. demonstrated significant results in reducing all-cause mortality, first hospitalization, and total hospitalizations in patients with HF overall.10 The E-INH trial, the latest study with the longest follow-up period of up to 120 months, yielded similar results with lower all-cause mortality and improved quality of life compared to the usual care group.9 The oldest study included in our meta-analysis, conducted by Gattis et al. in 1999, added that a straightforward evaluation by a clinical pharmacist significantly lowered all-cause mortality and HF events.29 Another study conducted in a rural area with 405 patients by Krum et al. demonstrated a reduction in the composite outcome and the number of patients hospitalized.35

Over the past two decades, even without a standardized protocol, the non-invasive approach has consistently demonstrated significant advantages through simple long-distance follow-up and evaluation. This might be due to, as previously mentioned, patients with HFrEF require improved compliance with GDMT to prevent further deterioration of the ejection fraction. Achieving this outcome necessitates continuous contact with specialists to maintain appropriate medication prescriptions, which might reduce the total number of hospitalizations required for the patients.9 We also believe that the heterogeneity within our non-invasive group is generally low, particularly regarding mortality outcomes, which aligns with findings from a previous meta-analysis by Scholte et al.10 They noted that the low heterogeneity observed in their study was attributed to the inclusion of stable HF patients (NYHA classes I-II). This observation supports the assertion that the non-invasive approach is genuinely advantageous in terms of reducing mortality across various patient profiles.

Invasive approach

In addition to medical therapy, device utilization has become increasingly essential in HF management, particularly HFrEF. Device therapy includes cardiac resynchronization therapy (CRT) and implantation of intracardiac defibrillators, both for the primary and secondary prevention of sudden cardiac death (SCD).59 As HF patients are at increased risk for life-threatening arrhythmias and SCD, ICD should be considered in particular subset of HF patients.60 ICD has been shown to improve outcomes in eligible HF patients, particularly with reduced ejection fraction. It has been endorsed by current European guidelines.43,61 Contemporary ICDs have the capability to continuously assess the functionality of the implanted system, measure clinical parameters, and document the incidence of arrhythmias or other incidents. As a result, they have the potential to offer early alerts regarding alterations in cardiac conditions or safety concerns. All device manufacturers provide remote ICD monitoring technology, allowing physicians to access patient data from a distance and, consequently, minimizing the need for unnecessary routine and interim appointments.61

At present, studies on the invasive approach to telemonitoring are limited and exhibit diversity. Despite our efforts to perform subgroup and sensitivity analysis focusing on invasive devices, low number of studies, and the lack of standardized methodologies as well as substantial heterogeneity in baseline characteristics among the studies hindered the identification of any meaningful results. The only significant result in the invasive group was the HF-related hospitalizations based on number of patients, with only 4 studies included ( Figure 16). Additionally, multiple studies have confirmed the advantages of telemonitoring in invasive devices. The IN-TIME study has yielded promising randomized data that demonstrates the advantages of invasive remote monitoring for implanted devices in improving clinical outcomes. The combined clinical assessment, which considered factors such as overall mortality, HF hospitalizations, changes in the NYHA class, and shifts in patient self-assessment, showed better results in the group utilizing invasive remote monitoring.30 As previously mentioned, Hindricks et al. outlined a potential function of invasive telemonitoring, highlighting its ability to identify suboptimal ICD/RCT-D performance.21 One of the potential harm is that these devices may trigger unnecessary and inappropriate defibrillation shocks, leading to hemodynamic consequences, and associated with excess mortality.62 In this context, telemonitoring can promptly detect such issues and enable early intervention.30 These results align with the TELECART study, where telemonitoring was identified as a predictive factor for hospitalization in HFrEF patients undergoing treatment with invasive devices.23 The RESULTS study similarly supported this finding, revealing that the group with invasive devices exhibited a lower primary endpoint outcome (comprising all-cause mortality or cardiovascular-related hospitalization) and a 50% reduction in in-clinic visits.24 The utilization of invasive techniques is also able to detect imminent deterioration, such as an increase in pulmonary artery pressure.9 While not included in our study, findings from the PREFER study and CONNECT trial suggested that remote monitoring with automatic notifications significantly shortened the time to a clinical decision in response to clinical events, particularly atrial arrhythmias—a prevalent complication in patients with HFrEF.63,64 Scholte et al. reached a similar conclusion with our study, possibly influenced by the limited number of studies included. The analysis indicated that the subgroup analysis focusing on invasive approaches did not reveal significant benefits in terms of mortality and hospitalization.10 Nevertheless, their findings highlighted a specific positive outcome related to IHM, which was associated with a reduction in hospitalizations related to HF by enabling the modification of diuretic dosages to prevent decompensation. This is particularly important, as decompensation stands out as a primary factor contributing to recurrent hospitalizations in HF patients.10 The DOT-HF trial and EVOLVO study also validated this outcome, showing a substantial decrease in HF hospitalizations.22,65 Additional studies on the invasive approach to telemonitoring are necessarily required to validate and confirm the current theory.

Clinical impact and future perspectives

The incorporation of telemonitoring into clinical practice offers considerable promise for the management of HFrEF. Telemonitoring enables the continuous observation of key health parameters, such as body weight, urine output, blood pressure, electrocardiogram (ECG), heart rate, and the identification of potential side effects, facilitating early detection of clinical deterioration and enabling timely and appropriate adjustments to treatment regimens. This proactive approach has the potential to mitigate hospitalization rates and reduce the need for emergency interventions, thereby improving patient outcomes and overall quality of care. In our study, we observed a statistically significant reduction in both all-cause mortality and HFrEF-related hospitalizations, emphasizing the potential clinical benefits of telemonitoring for this patient population. Moreover, the flexibility in follow-up durations, including < 6 months, 6–12 months, and ≥ 12 months, allows for the customization of care to meet the individual needs of patients. Non-invasive home telemonitoring systems—such as home telemonitoring (HTM), telemedicine services (TS), and more complex telemonitoring systems that combine TS with 24-hour call centers—empower patients to take an active role in managing their health while providing healthcare providers with real-time data to inform clinical decision-making. From a clinical perspective, the implementation of telemonitoring can improve patient adherence to prescribed therapies, reduce healthcare costs, and enhance access to care, particularly for patients in remote or underserved areas. Therefore, the integration of telemonitoring into standard clinical practice could serve as a valuable strategy for optimizing the management of HFrEF and advancing the delivery of efficient, patient-centered healthcare.

The primary factors contributing to the costs of telemedicine include the allocation of specialized human resources, the provision of appropriate technology, and the interaction between the medical team and the patient.1 Healthcare providers must carefully consider the potential limitations of these technologies, as they can incur significant costs, particularly for smaller healthcare facilities or hospitals. Additionally, telemedicine may not be suitable for patients who prefer face-to-face consultations with their physicians. Therefore, it is crucial for healthcare professionals to establish a strong rapport with patients to facilitate their comfort and acceptance of these emerging technologies. Moreover, it is important to recognize that no service is flawless, and telemedicine is no exception. Despite these challenges, numerous studies have consistently demonstrated that telemedicine can result in considerable savings in time and costs, as well as improvements in patient outcomes.

Strengths and limitations

To the best of our knowledge, there are already multiple studies of HF patients with telemedicine/telemonitoring, but our study is one of the first telemedicine studies with HFrEF patients.

There are a couple of limitations which should be mentioned. Please consider that our meta-analysis identifies substantial concerns related to bias and heterogeneity. This may stem from the considerable diversity and lack of standardization in the interventions studied. Several of the included studies exhibited considerable bias risks due to inadequate descriptions of the randomization process and the absence of adjustments for confounding factors. Additionally, both subgroup and sensitivity analyses were conducted as part of our efforts to enhance the quality of our analysis. Furthermore, there was a discrepancy in the approaches used among the included studies, with some employing invasive strategies and others non-invasive ones. For instance, in 1999, Gattis et al. employed a clinical pharmacist to evaluate the overall health of patients, which included monitoring vital signs and making adjustments to medications.29 In contrast, the majority of other studies relied on a physician or a combination of a physician and a nurse for these assessments. The criteria for including patients with reduced EF also varied, with most invasive studies involving patients with EF less than 35%, often using ICD or RCT-D. There are also multiple studies which include or exclude chronic stable HF patients (NYHA I-II) or with clinically severe HF (NYHA IV). Additionally, several studies featured more than two intervention arms.20,26,33 The differences in follow-up times were also responsible for the high heterogeneity. We were not able to analyze the differences in subgroup analysis as most of the studies do not always have the preferable outcome of interest.

Subsequently, variations in the definition of “usual care” across these trials may contribute to the absence of a significant reduction in hospitalization. Another factor contributing to the challenge in our meta-analysis is the variation in rehospitalization definitions across multiple articles. Some studies report rehospitalizations based on the total number of events, while others use the total number of patients. The lack of standardized measurements makes it difficult for us to incorporate these diverse data into the meta-analysis.

Conclusion

In summary, we filtered the initial 4,947 articles down to 27 that were thoroughly reviewed. We found that telemedicine has demonstrated its effectiveness in patients with HFrEF. However, the high heterogeneity among the articles due to the variations in how telemedicine and usual care were conducted, the lack of invasive studies and standardized method and definition, as well as the insufficient assessment of confounding bias, limit our study from achieving its highest quality and potential. We expect that future researchers will explore invasive approaches further and more on standardizing effective telemedicine practices due to the existing variability in methods and clinical situation of the patients.

Data availability

Underlying data

No data are associated with this article.

Extended data

Reporting guidelines

Figshare: Preferred Reporting Items for Systematic Reviews and Meta-Analyses Flow-chart (PRISMA) flowchart for “Current real world health data of telemedicine for heart failure with reduced ejection fraction: A systematic review and meta-analysis”. https://doi.org/10.6084/m9.figshare.24995804.v366

Data are available under the terms of the Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication).

Figshare: Preferred Reporting Items for Systematic Reviews and Meta-Analyses Flow-chart (PRISMA) checklist for “Current real world health data of telemedicine for heart failure with reduced ejection fraction: A systematic review and meta-analysis”. https://doi.org/10.6084/m9.figshare.24995780.v367

Data are available under the terms of the Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication).

Supplementary figures

Figshare: Supplementary figures of “Current real world health data of telemedicine for heart failure with reduced ejection fraction: A systematic review and meta-analysis”. https://doi.org/10.6084/m9.figshare.25712379.v1.68

Data are available under the terms of the Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication).

Acknowledgements

The authors express their gratitude to Hariadi from the Department of Cardiology and Vascular Medicine, Faculty of Medicine, Public Health, and Nursing at Gadjah Mada University for providing updated insights into systematic review and meta-analysis.

References

  • 1.  Maggioni AP, Dahlström U, Filippatos G, et al.: EURObservational Research Programme: regional differences and 1-year follow-up results of the Heart Failure Pilot Survey (ESC-HF Pilot). Eur. J. Heart Fail. 2013 Jul; 15(7): 808–817. PubMed Abstract | Publisher Full Text
  • 2.  Shah KS, Xu H, Matsouaka RA, et al.: Heart Failure With Preserved, Borderline, and Reduced Ejection Fraction: 5-Year Outcomes. J. Am. Coll. Cardiol. 2017 Nov 14; 70(20): 2476–2486. Publisher Full Text
  • 3.  Fonarow GC, Stough WG, Abraham WT, et al.: Characteristics, treatments, and outcomes of patients with preserved systolic function hospitalized for heart failure: a report from the OPTIMIZE-HF Registry. J. Am. Coll. Cardiol. 2007 Aug 21; 50(8): 768–777. Publisher Full Text
  • 4.  Lam CSP, Gamble GD, Ling LH, et al.: Mortality associated with heart failure with preserved vs. reduced ejection fraction in a prospective international multi-ethnic cohort study. Eur. Heart J. 2018 May 21; 39(20): 1770–1780. PubMed Abstract | Publisher Full Text
  • 5.  Chen S, Huang Z, Liang Y, et al.: Five-year mortality of heart failure with preserved, mildly reduced, and reduced ejection fraction in a 4880 Chinese cohort. ESC Heart Fail. 2022 Apr 18; 9(4): 2336–2347. PubMed Abstract | Publisher Full Text | Free Full Text
  • 6.  Dixit NM: Optimizing Guideline-directed Medical Therapies for Heart Failure with Reduced Ejection Fraction During Hospitalization.2021 Jul 3 [cited 2023 Nov 19]. Reference Source
  • 7.  Yusuf S, Rangarajan S, Teo K, et al.: Cardiovascular risk and events in 17 low-, middle-, and high-income countries. N. Engl. J. Med. 2014 Aug 28; 371(9): 818–827. PubMed Abstract | Publisher Full Text
  • 8.  Yun JE, Park JE, Park HY, et al.: Comparative Effectiveness of Telemonitoring Versus Usual Care for Heart Failure: A Systematic Review and Meta-analysis. J. Card. Fail. 2018 Jan; 24(1): 19–28. PubMed Abstract | Publisher Full Text
  • 9.  Angermann CE, Sehner S, Faller H, et al.: Longer-Term Effects of Remote Patient Management Following Hospital Discharge After Acute Systolic Heart Failure: The Randomized E-INH Trial. JACC Heart Fail. 2023 Feb; 11(2): 191–206. PubMed Abstract | Publisher Full Text
  • 10.  Scholte NTB, Gürgöze MT, Aydin D, et al.: Telemonitoring for heart failure: a meta-analysis. Eur. Heart J. 2023 Aug 14; 44(31): 2911–2926. PubMed Abstract | Publisher Full Text | Free Full Text
  • 11.  Reyes EB, Ha JW, Firdaus I, et al.: Heart failure across Asia: Same healthcare burden but differences in organization of care. Int. J. Cardiol. 2016 Nov 15; 223: 163–167. PubMed Abstract | Publisher Full Text
  • 12.  Page MJ, McKenzie JE, Bossuyt PM, et al.: The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021 Mar 29; 372: n71. Publisher Full Text
  • 13.  Higgins JPT, Thompson SG, Deeks JJ, et al.: Measuring inconsistency in meta-analyses. BMJ. 2003 Sep 6; 327(7414): 557–560. PubMed Abstract | Publisher Full Text | Free Full Text
  • 14.  Angermann C, Störk S, Gelbrich G, et al.: Mode of action and effects of standardized collaborative disease management on mortality and morbidity in patients with systolic heart failure: the Interdisciplinary Network for Heart Failure (INH) study. Circ. Heart Fail. 2012; 5(1): 25–35. PubMed Abstract | Publisher Full Text
  • 15.  Kurek A, Tajstra M, Gadula-Gacek E, et al.: Impact of Remote Monitoring on Long-Term Prognosis in Heart Failure Patients in a Real-World Cohort: Results From All-Comers COMMIT-HF Trial. J. Cardiovasc. Electrophysiol. 2017 Apr; 28(4): 425–431. PubMed Abstract | Publisher Full Text
  • 16.  Mo Y, Chu M, Hu W, et al.: Association between the nurse-led program with mental health status, quality of life, and heart failure rehospitalization in chronic heart failure patients. Medicine (Baltimore). 2021 Mar 12; 100(10): e25052. PubMed Abstract | Publisher Full Text | Free Full Text
  • 17.  Nunes-Ferreira A, Agostinho JR, Rigueira J, et al.: Non-invasive telemonitoring improves outcomes in heart failure with reduced ejection fraction: a study in high-risk patients. ESC Heart Failure. 2020 Dec; 7(6): 3996–4004. PubMed Abstract | Publisher Full Text | Free Full Text
  • 18.  Böhm M, Drexler H, Oswald H, et al.: Fluid status telemedicine alerts for heart failure: a randomized controlled trial. Eur. Heart J. 2016 Nov 1; 37(41): 3154–3163. PubMed Abstract | Publisher Full Text
  • 19.  Chiu CSL, Timmermans I, Versteeg H, et al.: Effect of remote monitoring on clinical outcomes in European heart failure patients with an implantable cardioverter-defibrillator: secondary results of the REMOTE-CIED randomized trial. Europace. 2022 Feb 2; 24(2): 256–267. PubMed Abstract | Publisher Full Text | Free Full Text
  • 20.  Hansen C, Loges C, Seidl K, et al.: INvestigation on Routine Follow-up in CONgestive HearT FAilure Patients with Remotely Monitored Implanted Cardioverter Defibrillators SysTems (InContact). BMC Cardiovasc. Disord. 2018 Jun 28; 18: 131. PubMed Abstract | Publisher Full Text | Free Full Text
  • 21.  Hindricks G, Taborsky M, Glikson M, et al.: Implant-based multiparameter telemonitoring of patients with heart failure (IN-TIME): a randomised controlled trial. Lancet. 2014 Aug 16; 384(9943): 583–590. PubMed Abstract | Publisher Full Text
  • 22.  Landolina M, Perego GB, Lunati M, et al.: Remote monitoring reduces healthcare use and improves quality of care in heart failure patients with implantable defibrillators: the evolution of management strategies of heart failure patients with implantable defibrillators (EVOLVO) study. Circulation. 2012 Jun 19; 125(24): 2985–2992. PubMed Abstract | Publisher Full Text
  • 23.  Sardu C, Santamaria M, Rizzo MR, et al.: Telemonitoring in heart failure patients treated by cardiac resynchronisation therapy with defibrillator (CRT-D): the TELECART Study. Int. J. Clin. Pract. 2016 Jul; 70(7): 569–576. PubMed Abstract | Publisher Full Text | Free Full Text
  • 24.  Tajstra M, Sokal A, Gadula-Gacek E, et al.: Remote Supervision to Decrease Hospitalization Rate (RESULT) study in patients with implanted cardioverter-defibrillator. Europace. 2020 May 1; 22(5): 769–776. PubMed Abstract | Publisher Full Text
  • 25.  Antonicelli R, Testarmata P, Spazzafumo L, et al.: Impact of telemonitoring at home on the management of elderly patients with congestive heart failure. J. Telemed. Telecare. 2008; 14(6): 300–305. PubMed Abstract | Publisher Full Text
  • 26.  Cleland JGF, Louis AA, Rigby AS, et al.: 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. J. Am. Coll. Cardiol. 2005 May 17; 45(10): 1654–1664. PubMed Abstract | Publisher Full Text
  • 27.  DeWalt DA, Malone RM, Bryant ME, et al.: A heart failure self-management program for patients of all literacy levels: A randomized, controlled trial [ISRCTN11535170]. BMC Health Serv. Res. 2006 Dec; 6(1): 30. PubMed Abstract | Publisher Full Text | Free Full Text
  • 28.  Domingues FB, Clausell N, Aliti GB, et al.: Education and Telephone Monitoring by Nurses of Patients with Heart Failure: Randomized Clinical Trial.
  • 29.  Gattis W, Hasselblad V, Whellan D, et al.: Reduction in heart failure events by the addition of a clinical pharmacist to the heart failure management team: results of the Pharmacist in Heart Failure Assessment Recommendation and Monitoring (PHARM) Study. Arch. Intern. Med. 1999; 159(16): 1939–1945. PubMed Abstract | Publisher Full Text
  • 30.  Giordano A, Scalvini S, Zanelli E, et al.: Multicenter randomised trial on home-based telemanagement to prevent hospital readmission of patients with chronic heart failure. Int. J. Cardiol. 2009 Jan 9; 131(2): 192–199. PubMed Abstract | Publisher Full Text
  • 31.  Goldberg LR, Piette JD, Walsh MN, 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. Am. Heart J. 2003 Oct; 146(4): 705–712. PubMed Abstract | Publisher Full Text
  • 32.  Köberich S, Lohrmann C, Mittag O, et al.: Effects of a hospital-based education programme on self-care behaviour, care dependency and quality of life in patients with heart failure – a randomised controlled trial. J. Clin. Nurs. 2015 Jun; 24(11–12): 1643–1655. PubMed Abstract | Publisher Full Text
  • 33.  Mortara A, Pinna GD, Johnson P, et al.: Home telemonitoring in heart failure patients: the HHH study (Home or Hospital in Heart Failure). Eur. J. Heart Fail. 2009 Mar; 11(3): 312–318. PubMed Abstract | Publisher Full Text | Free Full Text
  • 34.  Koehler F, Winkler S, Schieber 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(17): 1873–1880. PubMed Abstract | Publisher Full Text
  • 35.  Krum H, Forbes A, Yallop J, et al.: Telephone support to rural and remote patients with heart failure: the Chronic Heart Failure Assessment by Telephone (CHAT) study. Cardiovasc. Ther. 2013 Aug; 31(4): 230–237. PubMed Abstract | Publisher Full Text
  • 36.  Soran O, Piña I, Lamas G, et al.: A randomized clinical trial of the clinical effects of enhanced heart failure monitoring using a computer-based telephonic monitoring system in older minorities and women. J. Card. Fail. 2008; 14(9): 711–717. PubMed Abstract | Publisher Full Text
  • 37.  Villani A, Malfatto G, Compare A, et al.: Clinical and psychological telemonitoring and telecare of high risk heart failure patients. J. Telemed. Telecare. 2014; 20(8): 468–475. PubMed Abstract | Publisher Full Text
  • 38.  Voller H, Bindl D, Nagels K, et al.: The First Year of Noninvasive Remote Telemonitoring in Chronic Heart Failure Is not Cost Saving but Improves Quality of Life: the Randomized Controlled CardioBBEAT Trial. Telemed. J. E Health. 2022; 28(11): 1613–1622. PubMed Abstract | Publisher Full Text | Free Full Text
  • 39.  Wita M, Orszulak M, Szydło K, et al.: The usefulness of telemedicine devices in patients with severe heart failure with an implanted cardiac resynchronization therapy system during two years of observation. Kardiol. Pol. 2022; 80(1): 41–48. PubMed Abstract | Publisher Full Text
  • 40.  Han X, Chen W, Gao Z, et al.: Effectiveness of telemedicine for cardiovascular disease management: systematic review and meta-analysis. Ann. Palliat. Med. 2021 Dec; 10(12): 12831–12844. Publisher Full Text
  • 41.  Takahashi EA, Schwamm LH, Adeoye OM, et al.: An Overview of Telehealth in the Management of Cardiovascular Disease: A Scientific Statement From the American Heart Association. Circulation. 2022 Dec 20; 146(25): e558–e568. PubMed Abstract | Publisher Full Text
  • 42.  Silva-Cardoso J, Juanatey JRG, Comin-Colet J, et al.: The Future of Telemedicine in the Management of Heart Failure Patients. Card. Fail. Rev. 2021 Mar; 7: e11. PubMed Abstract | Publisher Full Text | Free Full Text
  • 43.  McDonagh TA, Metra M, Adamo M, et al.: 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure. Eur. Heart J. 2021 Sep 21; 42(36): 3599–3726. Publisher Full Text
  • 44.  Zhu Y, Gu X, Xu C: Effectiveness of telemedicine systems for adults with heart failure: a meta-analysis of randomized controlled trials. Heart Fail. Rev. 2020 Mar; 25(2): 231–243. PubMed Abstract | Publisher Full Text | Free Full Text
  • 45.  Anker SD, Koehler F, Abraham WT: Telemedicine and remote management of patients with heart failure. Lancet. 2011 Aug 20; 378(9792): 731–739. Publisher Full Text
  • 46.  Bozkurt B, Coats AJS, Tsutsui H, et al.: Universal definition and classification of heart failure: a report of the Heart Failure Society of America, Heart Failure Association of the European Society of Cardiology, Japanese Heart Failure Society and Writing Committee of the Universal Definition of Heart Failure: Endorsed by the Canadian Heart Failure Society, Heart Failure Association of India, Cardiac Society of Australia and New Zealand, and Chinese Heart Failure Association. Eur. J. Heart Fail. 2021 Mar; 23(3): 352–380. PubMed Abstract | Publisher Full Text
  • 47.  Haydock PM, Flett AS: Management of heart failure with reduced ejection fraction. Heart. 2022 Sep 12; 108(19): 1571–1579. PubMed Abstract | Publisher Full Text | Free Full Text
  • 48.  Romero E, Yala S, Sellers-Porter C, et al.: Remote monitoring titration clinic to implement guideline-directed therapy for heart failure patients with reduced ejection fraction: a pilot quality-improvement intervention. Front. Cardiovasc. Med. 2023; 10: 1202615. PubMed Abstract | Publisher Full Text | Free Full Text
  • 49.  Wong CK, Un KC, Zhou M, et al.: Daily ambulatory remote monitoring system for drug escalation in chronic heart failure with reduced ejection fraction: pilot phase of DAVID-HF study. Eur. Heart J. Digit. Health. 2022 Jun; 3(2): 284–295. PubMed Abstract | Publisher Full Text | Free Full Text
  • 50.  Klersy C, De Silvestri A, Gabutti G, et al.: A Meta-Analysis of Remote Monitoring of Heart Failure Patients. J. Am. Coll. Cardiol. 2009 Oct 27; 54(18): 1683–1694. Publisher Full Text
  • 51.  Inglis SC, Clark RA, McAlister FA, et al.: Structured telephone support or telemonitoring programmes for patients with chronic heart failure. Cochrane Database Syst. Rev. 2010 Aug 4; 8: CD007228. Publisher Full Text
  • 52.  Koehler F, Winkler S, Schieber M, et al.: Telemedicine in heart failure: Pre-specified and exploratory subgroup analyses from the TIM-HF trial. Int. J. Cardiol. 2012 Nov 29; 161(3): 143–150. PubMed Abstract | Publisher Full Text
  • 53.  Masarone D, Limongelli G, Rubino M, et al.: Management of Arrhythmias in Heart Failure. J. Cardiovasc. Dev. Dis. 2017 Feb 28; 4(1): 3. Publisher Full Text
  • 54.  Chaudhry S, Mattera J, Curtis J, et al.: Telemonitoring in patients with heart failure. N. Engl. J. Med. 2010; 363(24): 2301–2309. PubMed Abstract | Publisher Full Text | Free Full Text
  • 55.  Koehler F, Winkler S, Schieber M, et al.: Telemedical Interventional Monitoring in Heart Failure (TIM-HF), a randomized, controlled intervention trial investigating the impact of telemedicine on mortality in ambulatory patients with heart failure: study design. Eur. J. Heart Fail. 2010 Dec; 12(12): 1354–1362. PubMed Abstract | Publisher Full Text
  • 56.  Piccini JP, Mittal S, Snell J, et al.: Impact of remote monitoring on clinical events and associated health care utilization: A nationwide assessment. Heart Rhythm. 2016 Dec; 13(12): 2279–2286. PubMed Abstract | Publisher Full Text
  • 57.  Sanna T, Diener HC, Passman RS, et al.: Cryptogenic stroke and underlying atrial fibrillation. N. Engl. J. Med. 2014 Jun 26; 370(26): 2478–2486. Publisher Full Text
  • 58.  Mabo P, Victor F, Bazin P, et al.: A randomized trial of long-term remote monitoring of pacemaker recipients (the COMPAS trial). Eur. Heart J. 2012 May; 33(9): 1105–1111. PubMed Abstract | Publisher Full Text | Free Full Text
  • 59.  Cabac-Pogorevici I, Muk B, Rustamova Y, et al.: Ischaemic cardiomyopathy. Pathophysiological insights, diagnostic management and the roles of revascularisation and device treatment. Gaps and dilemmas in the era of advanced technology. Eur. J. Heart Fail. 2020 May; 22(5): 789–799. PubMed Abstract | Publisher Full Text
  • 60.  Hussein AA, Wilkoff BL: Cardiac Implantable Electronic Device Therapy in Heart Failure. Circ. Res. 2019 May 24; 124(11): 1584–1597. Publisher Full Text
  • 61.  De Simone A, Leoni L, Luzi M, et al.: Remote monitoring improves outcome after ICD implantation: the clinical efficacy in the management of heart failure (EFFECT) study. Europace. 2015 Aug; 17(8): 1267–1275. PubMed Abstract | Publisher Full Text
  • 62.  Koneru JN, Swerdlow CD, Wood MA, et al.: Minimizing Inappropriate or “Unnecessary” Implantable Cardioverter-Defibrillator Shocks. Circ. Arrhythm. Electrophysiol. 2011 Oct; 4(5): 778–790. Publisher Full Text
  • 63.  Crossley GH, Boyle A, Vitense H, et al.: The CONNECT (Clinical Evaluation of Remote Notification to Reduce Time to Clinical Decision) trial: the value of wireless remote monitoring with automatic clinician alerts. J. Am. Coll. Cardiol. 2011 Mar 8; 57(10): 1181–1189. PubMed Abstract | Publisher Full Text
  • 64.  Chen J, Wilkoff BL, Choucair W, et al.: Design of the Pacemaker REmote Follow-up Evaluation and Review (PREFER) trial to assess the clinical value of the remote pacemaker interrogation in the management of pacemaker patients. Trials. 2008 Apr 3; 9: 18. PubMed Abstract | Publisher Full Text | Free Full Text
  • 65.  Braunschweig F, Ford I, Conraads V, et al.: Can monitoring of intrathoracic impedance reduce morbidity and mortality in patients with chronic heart failure? Rationale and design of the Diagnostic Outcome Trial in Heart Failure (DOT-HF). Eur. J. Heart Fail. 2008 Sep; 10(9): 907–916. PubMed Abstract | Publisher Full Text
  • 66.  William Y, Tarigan T, Chen J, et al.: Figure 1. Preferred Reporting Items for Systematic Reviews and Meta-Analyses Flow-chart (PRISMA) flowchart for “Current real world health data of telemedicine for heart failure with reduced ejection fraction: A systematic review and meta-analysis”.jpg. figshare. 2024 [cited 2024 Apr 29]. Reference Source
  • 67.  William Y, Tarigan T, Chen J, et al.: PRISMA checklist of “Current real world health data of telemedicine for heart failure with reduced ejection fraction: A systematic review and meta-analysis”.docx. figshare. 2024 [cited 2024 Apr 29]. Reference Source
  • 68.  William Y, Tarigan T, Chen J, et al.: Supplementary figures of “Current real world health data of telemedicine for heart failure with reduced ejection fraction: A systematic review and meta-analysis”. figshare. 2024 [cited 2024 Apr 29]. Reference Source

Comments on this article Comments (0)

Version 3
VERSION 3 PUBLISHED 11 Jun 2024
Comment
Author details Author details
Competing interests
Grant information
Article Versions (3)
Copyright
Download
 
Export To
metrics
Views Downloads
F1000Research - -
PubMed Central
Data from PMC are received and updated monthly.
 - -
Citations
SEE MORE DETAILS
CITE
how to cite this article
William Y, Tarigan T, Chen J et al. Current real world health data of telemedicine for heart failure with reduced ejection fraction: a systematic review and meta-analysis [version 3; peer review: 1 approved, 1 approved with reservations]. F1000Research 2025, 13:616 (https://doi.org/10.12688/f1000research.146779.3)
NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article.
track
receive updates on this article
Track an article to receive email alerts on any updates to this article.

Open Peer Review

Current Reviewer Status: ?
Key to Reviewer Statuses VIEW
ApprovedThe paper is scientifically sound in its current form and only minor, if any, improvements are suggested
Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.
Not approvedFundamental flaws in the paper seriously undermine the findings and conclusions
Version 2
VERSION 2
PUBLISHED 25 Oct 2024
Revised
Views
13
Cite
Reviewer Report 28 Jan 2025
Vito Damay, Department of Cardiology, Faculty of Medicine, Pelita Harapan University, Tangerang, Indonesia 
Approved with Reservations
VIEWS 13
https://doi.org/10.5256/f1000research.173755.r361033
1. Author should mention in the discussion that some of included studies had significant risks of bias (e.g., poorly described randomization, lack of confounding adjustments). The impact of these biases on the overall results should be more elaborated at least ... Continue reading
CITE
CITE
HOW TO CITE THIS REPORT
Damay V. Reviewer Report For: Current real world health data of telemedicine for heart failure with reduced ejection fraction: a systematic review and meta-analysis [version 3; peer review: 1 approved, 1 approved with reservations]. F1000Research 2025, 13:616 (https://doi.org/10.5256/f1000research.173755.r361033)
The direct URL for this report is:
https://f1000research.com/articles/13-616/v2#referee-response-361033
NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article.
Report a concern
  • Author Response 26 Apr 2025
    Yohanes William, Faculty of Medicine Public Health and Nursing Gadjah Mada University, Sleman, 55281, Indonesia
    26 Apr 2025
    Author Response
    Dear Vito Damay, MD

    on behalf of our team, i would like express our gratitude to you for accepting our peer review request. I apologize for the extremely late ... Continue reading
    Report a concern
  • Respond or Comment
COMMENTS ON THIS REPORT
  • Author Response 26 Apr 2025
    Yohanes William, Faculty of Medicine Public Health and Nursing Gadjah Mada University, Sleman, 55281, Indonesia
    26 Apr 2025
    Author Response
    Dear Vito Damay, MD

    on behalf of our team, i would like express our gratitude to you for accepting our peer review request. I apologize for the extremely late ... Continue reading
    Report a concern
Views
10
Cite
Reviewer Report 27 Nov 2024
Nisha Nambiar, Lincoln University Collage, Malaysia, Malaysia;  Faculty of Applied Science, Lincoln University College, Petaling Jaya, Selangor, Malaysia 
Approved
VIEWS 10
https://doi.org/10.5256/f1000research.173755.r334837
The authors had prepared a manuscript with a well explained methodologies, findings and discussion. ... Continue reading
CITE
CITE
HOW TO CITE THIS REPORT
Nambiar N. Reviewer Report For: Current real world health data of telemedicine for heart failure with reduced ejection fraction: a systematic review and meta-analysis [version 3; peer review: 1 approved, 1 approved with reservations]. F1000Research 2025, 13:616 (https://doi.org/10.5256/f1000research.173755.r334837)
The direct URL for this report is:
https://f1000research.com/articles/13-616/v2#referee-response-334837
NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article.
Report a concern
Version 1
VERSION 1
PUBLISHED 11 Jun 2024
Views
34
Cite
Reviewer Report 03 Sep 2024
Nisha Nambiar, Lincoln University Collage, Malaysia, Malaysia;  Faculty of Applied Science, Lincoln University College, Petaling Jaya, Selangor, Malaysia 
Approved with Reservations
VIEWS 34
https://doi.org/10.5256/f1000research.160897.r310189
In general, the authors had produced a good review paper. Researchers fulfilled the minimum requirements in reporting a systematic review and meta-analyses. The rationale and the research objectives were clearly stated in the manuscript. The methods were clearly discussed, allowing readers ... Continue reading
CITE
CITE
HOW TO CITE THIS REPORT
Nambiar N. Reviewer Report For: Current real world health data of telemedicine for heart failure with reduced ejection fraction: a systematic review and meta-analysis [version 3; peer review: 1 approved, 1 approved with reservations]. F1000Research 2025, 13:616 (https://doi.org/10.5256/f1000research.160897.r310189)
The direct URL for this report is:
https://f1000research.com/articles/13-616/v1#referee-response-310189
NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article.
Report a concern
  • Author Response 19 Sep 2024
    Yohanes William, Faculty of Medicine Public Health and Nursing Gadjah Mada University, Sleman, 55281, Indonesia
    19 Sep 2024
    Author Response
    Dear Nisha Nambiar,

    We are grateful and thank you for your thorough review for our article. Generally, we tried to include the latest articles as many as we could, ... Continue reading
    Report a concern
  • Respond or Comment
COMMENTS ON THIS REPORT
  • Author Response 19 Sep 2024
    Yohanes William, Faculty of Medicine Public Health and Nursing Gadjah Mada University, Sleman, 55281, Indonesia
    19 Sep 2024
    Author Response
    Dear Nisha Nambiar,

    We are grateful and thank you for your thorough review for our article. Generally, we tried to include the latest articles as many as we could, ... Continue reading
    Report a concern

Comments on this article Comments (0)

Version 3
VERSION 3 PUBLISHED 11 Jun 2024
Comment

Open Peer Review

Reviewer Status

Alongside their report, reviewers assign a status to the article:

Approved
The paper is scientifically sound in its current form and only minor, if any, improvements are suggested
Approved with reservations
A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.
Not approved
Fundamental flaws in the paper seriously undermine the findings and conclusions

Reviewer Reports

Invited Reviewers
1 2
Version 3
(revision)
 28 Apr 25
Version 2
(revision)
 25 Oct 24 		read read
Version 1
11 Jun 24 		read
  1. Nisha Nambiar, Lincoln University Collage, Malaysia, Malaysia; Lincoln University College, Petaling Jaya, Malaysia
  2. Vito Damay, Pelita Harapan University, Tangerang, Indonesia

Comments on this article

All Comments(0)

Add a comment
Sign up for content alerts

Browse by related subjects

    keyboard_arrow_left Back to all reports
    keyboard_arrow_left Back to all reports
    keyboard_arrow_left Back to all reports
    Alongside their report, reviewers assign a status to the article:
    Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested
    Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.
    Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions
    Sign In
    If you've forgotten your password, please enter your email address below and we'll send you instructions on how to reset your password.

    The email address should be the one you originally registered with F1000.
    Email address not valid, please try again

    You registered with F1000 via Google, so we cannot reset your password.

    To sign in, please click here.

    If you still need help with your Google account password, please click here.

    You registered with F1000 via Facebook, so we cannot reset your password.

    To sign in, please click here.

    If you still need help with your Facebook account password, please click here.

    Code not correct, please try again
    Email us for further assistance.
    Server error, please try again.