Telemonitoring and hemodynamic monitoring to reduce hospitalization rates in heart failure: a systematic review and meta-analysis of randomized controlled trials and real-world studies

Gary Tse, Cynthia Chan, Mengqi Gong, Lei MENG, Jian ZHANG, Xiao-Ling SU,

Sadeq Ali-Hasan-Al-Saegh6, Abhishek C Sawant7, George Bazoukis8, Yun-Long XIA9, Ji-Chao Zhao10,Alex Pui Wai Lee1, Leonardo Roever11, Martin CS Wong12, Adrian Baranchuk13, Tong Liu3;International Health Informatics Study (IHIS) Network

1Department of Medicine and Therapeutics, Faculty of Medicine, Chinese University of Hong Kong, Hong Kong, China

2Li Ka Shing Institute of Health Sciences, Faculty of Medicine, Chinese University of Hong Kong, Hong Kong, China

3Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, the Second Hospital of Tianjin Medical University, Tianjin, China

4State Key Laboratory of Cardiovascular Disease, Heart Failure Center, Fuwai Hospital; National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China

5Department of Cardiology, Qinghai Provincial People’s Hospital, Xining, China

6Cardiovascular Research Center, Shahid Sadoughi University of Medical Sciences, Yazd, Iran

7Division of Cardiology, State University of New York at Buffalo, Buffalo, NY, USA

8The Second Department of Cardiology, Laboratory of Cardiac Electrophysiology, Evangelismos General Hospital of Athens, Athens, Greece

9Department of Cardiovascular Medicine, the First Affiliated Hospital of Dalian Medical University, Dalian, China

10Auckland Bioengineering Institute, the University of Auckland, Auckland, New Zealand

11Department of Clinical Research, Federal University of Uberlandia, Uberlandia, MG, Brazil

12JC School of Public Health and Primary Care, Faculty of Medicine, the Chinese University of Hong Kong, Hong Kong, China

13Division of Cardiology, Kingston General Hospital, Queen’s University, Kingston, Ontario, Canada

1 Introduction

Heart failure is characterized by structural abnormalities of left ventricular dysfunction and dilatation, a compensatory rise in systemic vascular resistance secondary to activation of neurohumoral pathways,[1]inflammation,[2]and metabolic adaptations to energy substrate utilization.[3]It is a major public health problem globally, causing significant mortality and morbidity and placing a significant burden on healthcare systems. Hospitalization rate, a measure of healthcare resource utilization, is estimated to be 20% at one month and 50% at 6 months.[4]A history of hospitalization is itself an independent predictor of long-term mortality.Therefore, measures to reduce hospitalization are likely beneficial in this patient population.[5]

Telemonitoring can be used to track patients’ symptoms,adherence to medications and objective parameters such as blood pressure, heart rate, body weight and urine output.[6]However, the effectiveness of body weight monitoring has been disputed, as the largest randomized controlled trials to date failed to demonstrate a reduction in heart failure-related hospitalizations. The reasons behind this are complex, but can be partly explained by the fact that body weight and symptoms may not provide sufficient warning of impending decompensation of cardiac function.[7,8]Patient data from implantable hemodynamic monitoring studies have shown that weight is not a good measure of filling pressures that may be important determinants of decompensation.[9]Moreover, hospitalization in heart failure may be related to not only abnormal physiological factors, but also social factors.[10]

In addition to tele-monitoring, recent interests have focused on the roles of implantable hemodynamic monitors.Three devices, CardioMEMS, Chronicle and HeartPOD are commercially available to monitor pulmonary arterial pressure, right ventricular pressure and left atrial pressure, respectively. Several meta-analyses have been performed on remote monitoring for heart failure. For example, in 2009,the impact of remote monitoring on mortality and hospitalization rates was examined.[11]Recently, two meta-analyses of randomized controlled trials were performed.[12,13]This study complements these previous studies by providing an updated meta-analysis of both randomized controlled trials and observational studies on hospitalization rates.

2 Methods

2.1 Search strategy, inclusion and exclusion criteria

This systematic review and meta-analysis was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement.[14]It has been registered with PROSPERO (CRD42017073934). PubMed and Cochrane Library were searched up to 1stMay 2017, with no language restriction, for studies that investigated the hospitalization rates in heart failure. The following search terms were used for PubMed and Cochrane Library: “telemonitoring heart failure hospitalization” and “hemodynamic monitoring heart failure hospitalization).

The following inclusion criteria were applied: (1) the design was a case-control, prospective or retrospective observational study or randomized controlled trial in humans, (2)patients with heart failure (both preserved and reduced ejection fraction included) were analyzed, (3) hospitalization rates, whether heart failure-specific, cardiovascular-related or all-cause, were reported or could be calculated from the published data; (3) and (4) hazard ratios (HRs) or relative risks (RRs) and their corresponding 95% CIs or data necessary to calculate these were available.

Quality assessment of case-control and cohort studies included in our meta-analysis was performed using the Newcastle–Ottawa Quality Assessment Scale (NOS) (Tables 1S and 2S for telemonitoring, Tables 3S and 4S for hemodynamic monitoring),[15]and of randomized controlled trials using the Jadad scale (Oxford quality scoring system) (Table 5S and 6S for telemonitoring and hemodynamic monitoring, respectively). The NOS evaluated the categories of study participant selection, comparability of the results, and quality of the outcomes. The following characteristics were assessed: (1) representativeness of the exposed cohort; (2)selection of the non-exposed cohort; (3) ascertainment of exposure; (4) demonstration that outcome of interest was not present at the start of study; (5) comparability of cohorts on the basis of the design or analysis; (6) assessment of outcomes; (7) follow-up period sufficiently long for outcomes to occur; and (8) adequacy of follow-up of cohorts.This scale varied from zero to nine stars, which indicated that studies were graded as poor quality if they met ＜ 5 criteria, fair if they met 5 to 7 criteria, and good if they met ＞ 8 criteria. The Jadad score assessed the quality by the following criteria of (1) randomization, (2) allocation concealment,(3) double blinding and (4) withdrawal and dropouts. The total score is 7, scores 1 to 3 indicate low quality and 4 to 7 high quality.

2.2 Data extraction and statistics

Data from the different studies were entered in pre-specified spreadsheet in Microsoft Excel. All potentially relevant reports were retrieved as complete manuscripts and assessed for compliance with the inclusion criteria. In this metaanalysis, the extracted data elements consisted of: (1) publi-cation details: last name of first author, publication year and locations; (2) study design (cohort study or randomized controlled trial); (3) follow-up duration; (4) endpoints; (5)the quality score; and (6) the characteristics of the population including sample size, gender, age and number of subjects. Meta-analyses of observational studies are challenging due to differences in study designs and inherent biases. Two reviewers independently reviewed each included study and disagreements were resolved by adjudication with input from a third reviewer.

The endpoints for this meta-analysis were hospitalization rates. Where different types of hospitalization rates were reported, heart failure-specific rates were used preferentially,followed by cardiovascular-related hospitalization rates, and finally all-cause hospitalization rates. Multivariate adjusted hazard ratios (HRs) or relative risks (RRs) with 95% CI were extracted for each study. When values from multivariate analysis were not available, those from univariate analysis were used.

When HRs were not provided, they were calculated using raw data. The pooled adjusted risk estimates from each study as the HR values with 95% CI were presented. Different types of hospitalization rates were pooled together.

Heterogeneity between studies was determined using Cochran’s Q, which is the weighted sum of squared differences between individual study effects and the pooled effect across studies, and theI2statistic from the standard chisquare test, which is the percentage of the variability in effect estimates resulting from heterogeneity.I2＞ 50% was considered to reflect significant statistical heterogeneity. A fixed effects model was used ifI2＜ 50%, otherwise the random-effects model using the inverse variance heterogeneity method was selected. To find the origin of the heterogeneity, sensitivity analysis excluding one study at a time was performed. Subgroup analyses based on time-points or type of telemonitoring or hemodynamic monitoring were performed. Short-term was defined as those occurring within 6 months, whereas long-term was defined as 12 months or longer. Where a study reported effective estimates at successive time points, the longer time point was used. Funnel plots, Begg and Mazumdar rank correlation test and Egger’s test[16]were used to assess for possible publication bias.

3 Results

Figure 1 shows a flow diagram detailing the search strategy and study selection process. For telemonitoring, a total of 120 and 111 entries were retrieved from PubMed and Cochrane Library, with 60 articles included in our final meta-analysis.[6,17–75]For hemodynamic monitoring, a total of 220 and 53 entries were retrieved from the same databases, with 12 articles included in our final meta-analysis.[4,76–86]

3.1 Telemonitoring

For telemonitoring, a total of 31,501 patients (mean age:68 ± 12 years old; 61% male) were included. The baseline characteristics of these studies are listed in Table 1. Six were cohort studies and 55 were randomized controlled trials. The mean follow-up duration was 11 ± 8 months.Telemonitoring reduced hospitalization rates with a HR of 0.73 (95% CI: 0.65-0.83;P＜ 0.0001, Figure 2). The Cochran’s Q value was greater than the degrees of freedom (994vs. 59), suggesting the true effect size was different among the various studies. Moreover,I2took a value of 94%, indicating the presence of significant heterogeneity. Sensitivity analysis by leaving out one study at a time did not significantly alter the pooled HR (Figure 1S). Funnel plot plotting standard errors or precision against the logarithms of the odds ratio are shown in Figures 2S and 3S, respectively.Begg and Mazumdar rank correlation suggested a significant publication bias (Kendal’s Tau value = -0.2,P＜ 0.05);Egger’s test demonstrated significant asymmetry (intercept:-1.4,t-value: 2.6;P＜ 0.05).

Figure 1. A flow diagram detailing the search strategy and study selection process for this systematic review and meta-analysis on the effects of telemonitoring and hemodynamic monitoring on hospitalization rates in heart failure.

Table 1. Characteristics of the 60 studies on telemonitoring included in this meta-analysis.

Table 1. Cont.

Because of the substantial heterogeneity present, we explored its possible origins. As we initially combined mortality assessed at different durations, univariate and multivariate HRs, and study design, the following subgroup analyses were performed. Firstly, we found that telemonitoring reduced hospitalization rates in the short-term (n= 27; ≤ 6 months; HR = 0.77, 95% CI: 0.65-0.89;P＜ 0.01;I2= 67%;Figure 4S) and long-term (n= 32; ≥ 12 months: HR = 0.73,95% CI: 0.62-0.87;P＜ 0.0001;I2= 97%; Figure 5S). Secondly, subgroup analysis was performed for the type of HR.Meta-analysis of univariate HRs produced a pooled effect estimate of 0.94 (95% CI: 0.93-0.95;P＜ 0.0001) without significantly affecting heterogeneity (I2= 95%,vs. 94% previously). By contrast, meta-analysis of multivariate HRs produced a similar pooled effect estimate of 0.91 (95% CI:0.84-0.99;P＜ 0.05) whilst reducingI2to 71%. Thirdly,subgroup analysis was performed for study design. Metaanalysis of randomized controlled trials (RCTs) yielded a pooled effect estimate of 0.96 (95% CI: 0.95-0.97;P＜0.0001) whilst reducingI2to 72%. By contrast, meta-analysis of cohort studies yielded a significantly lower HR of 0.38 (95% CI: 0.36-0.41;P＜ 0.0001) whilst preservingI2at 94%. Together, these findings suggest the duration over which mortality was assessed, type of HRs and study design to be possible sources of heterogeneity.

3.2 Hemodynamic monitoring

Figure 2. Pooled hazard ratios for studies examining the effects of telemonitoring on hospitalization rates in heart failure.

For wireless hemodynamic monitoring, a total of 4831 patients were included. The baseline characteristics of these studies are listed in Table 2. Four publications were cohort studies and eight publications were based on data from three randomized controlled trials (CHAMPION, COMPASS-HF and REDUCEhf). The mean follow-up duration was 13 ± 4 months. The mean age was 66 ± 18 years) of whom 66%were male. Wireless hemodynamic monitoring significantly reduced hospitalization rates with a HR of 0.60 (95% CI:0.53-0.69;P＜ 0.001). The Cochran’s Q value was greater than the degrees of freedom (36vs. 13), suggesting the true effect size was different among the various studies.I2took a value of 64%, indicating the presence of significant heterogeneity. Sensitivity analysis by leaving out one study at a time did not significantly alter the pooled HR (Figure 6S). Funnel plot plotting standard errors or precision against the logarithms of the odds ratio are shown in Figures 7S and 8S, respectively. Begg and Mazumdar rank correlation suggested a significant publication bias (Kendal’s Tau value =-0.5,P＜ 0.05). Egger’s test demonstrated significant asymmetry (intercept: -2.2,t-value = 3.2;P＜ 0.01).

Figure 3. Pooled hazard ratios for studies examining the effects of hemodynamic monitoring on hospitalization rates in heart failure.

Table 2. Characteristics of the 12 studies on hemodynamic monitoring included in this meta-analysis.

Significant reductions in hospitalization rates were observed in both short-term (HR: 0.55, 95% CI: 0.45-0.68;P＜ 0.001;I2= 72%; Figure 9S) and long-term (HR: 0.64,95% CI: 0.57-0.72;P＜ 0.001;I2= 55%; Figure 10S). For the different types of hemodynamic devices, hospitalization rates were significantly reduced using pulmonary pressure monitoring (HR: 0.58, 95% CI: 0.50-0.66;P＜ 0.001;I2=67%; Figure 11S) or left atrial pressure monitoring (HR:0.16, 95% CI: 0.04-0.68;P＜ 0.05). It was not possible to perform a meta-analysis for left atrial pressure monitoring because this was only assessed by one study. Right ventricular pressure monitoring tended to reduce hospitalization rates (HR: 0.69, 95% CI: 0.47–1.01;I2= 61%; Supplementary Figure 12S) but this did not reach statistical significance (P= 0.058).

4 Discussion

This is a systematic review and meta-analysis of randomized controlled trials and real-world studies on the effects of remote patient monitoring on hospitalization rates in heart failure, complementing previous meta-analyses.[11–13]The main findings are the following: (1) hospitalization rates can be reduced by remote patient monitoring using either telemonitoring or hemodynamic monitoring by 26%(95% CI: 17%-35%) and 40% (95% CI: 31%-47%), respectively; (2) telemonitoring reduced hospitalization rates by 24% in the short-term (≤ 6 months) and 27% in the long-term (≥ 12 months); and (3) hemodynamic monitoring reduced hospitalization rates by 45% in the short-term and 37% in the long-term.

Telemonitoring is a broad term referring to the making telephone contact with patients to enquire about symptoms,adherence to pharmacotherapy, and obtain information on clinically important parameters such as heart rate, blood pressure, body weight and urine output. This in turn enables appropriate advice to be offered to patients.[17]The benefits of home monitoring systems on hospitalization are possibly due to its good potential for detecting early signs of decompensation and reinforcement of patient's self-care education,and are especially useful for those who needs extra support,such as older and more frail patients.[87,88]Telemonitoring appears to have limited potential in early detection of worsening heart failure, but most effective when patient education toward medical adherence and patient self-care efficacy are reinforced. These different effects of telemonitoring could be attributable to the wide distribution or the disparate outcome of the effects on hospitalization, and to the heterogeneity observed. There are different vital signs that could be used to provide a warning for heart failure decompensation. These are heart rate, heart rate variability,[89]blood pressure, body weight and urine output.[6,89–91]For example,increases in body weight can predict acute decompensation requiring hospitalization.[91]However, a study found that diastolic blood pressure, systolic blood pressure x heart rate and diastolic blood pressure x heart rate, but not heart rate or systolic blood pressure by itself, predicted 3-month major adverse cardiac events.[90]

Hemodynamic monitoring refers to the continuous measurement of cardiac chamber or vascular pressures. Three devices are available: CardioMEMS (pulmonary arterial pressure),[92]Chronicle (right ventricular pressure)[93]and HeartPOD (left atrial pressure).[94]The rationale behind hemodynamic monitoring is that increases in intracardiac and pulmonary arterial pressures were detectable several weeks prior to worsening of clinical symptoms and signs.[4,9]Subgroup analyses were performed for the different hemodynamic parameter measured. The evidence for pulmonary artery pressure monitoring is the strongest, with a 42% reduction in hospitalization rates. Right ventricular pressure monitoring tended to reduce hospitalization rates by around 31% but this was not statistically significant. It was not possible to perform a meta-analysis for left atrial monitoring, as only one study has been published to date. Nevertheless The LAPTOP-HF trial is currently ongoing and when completed will provide important data for determining whether left atrial monitoring will similarly reduce hospitalization rates in heart failure.[95]

Theoretically, hemodynamic monitoring should reduce hospitalization rates to greater extents than usual care or telemonitoring if patients were offered appropriate advice to mitigate abnormal cardiac physiology, such as fluid overload or bradycardia, by altering medication regimens at home so that hospitalization would not be necessary. Our meta-analysis found that the risk reduction for hospitalization using hemodynamic monitoring was slightly higher at 40% compared to 27% using telemonitoring, but this was not significantly different. This meta-analysis provides data that less-invasive remote monitoring by telemedicine is equally effective as more invasive forms of hemodynamic monitoring. The former approach may be more cost-effective and yet able to prevent hospitalizations. Therefore,healthcare resources can be focused on the patients who do require hospital admission, who can be offered additional investigations such as quantification of blood biomarkers and echocardiography for guiding their management.[96,97]

4.1 Limitations

There are some limitations of this study that must be recognized. Firstly, we had observed a substantial heterogeneity for the HRs for the effects of telemonitoring on hospitalization rates. In our study, hazard ratios of randomized controlled trials and cohort studies, which are different study designs, were initially pooled together. A recent Cochrane review showed that there were no significant difference in the effective estimates between observational studies and randomized controlled trials, suggesting that factors other than study design are responsible for differences in outcomes.[98]However, in our subgroup analysis,we found that the pooled HR was significantly lower for cohort studies when compared to the HR for RCT. Therefore, meta-analysis should combine the effect estimates separately based on trial design. Moreover, this subgroup analysis resulted in a reduction ofI2to 72% for RCTs, suggesting that this contributed to the heterogeneity observed.Other sources, as assessed by our subgroup analyses, were the duration over which mortality was assessed (short-term versus long-term mortality) and whether the HRs were univariate or multivariate HRs. Secondly, we detected significant bias using both Begg and Mazumdar rank correlation test and Egger’s test, in that the reported HRs skewed towards reduced hospitalization by telemonitoring. In other words, fewer HRs were from the studies reporting a lack of effect on hospitalization. Therefore, this may represent publication bias in which only positive findings were published by the journals, with negative results possibly not published.Thirdly, there were only four cohort studies that assessed hemodynamic monitoring. As only three RCTs with a limited number of subjects were conducted, future RCTs are needed for different types of hemodynamic monitoring systems, especially left atrial pressure monitoring, for which the HR was only available in one study and it was therefore not possible to conduct a subgroup analysis for this system.Finally, there is a lack of studies that directly compare hemodynamic monitoring to telemonitoring, which needs to be investigated in the future, especially given the invasive nature of hemodynamic monitoring systems.

4.2 Conclusions

This meta-analysis demonstrates that both telemonitoring and hemodynamic monitoring are equally effective approaches to reduce hospitalization rates in heart failure.Telemonitoring should be used more widely, since it is less invasive than hemodynamic monitoring and may be more cost-effective. However, direct comparisons between these modes of monitoring are needed in the future.

Acknowledgments

There are no conflicts of interests to be declared.

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