It has been assumed that less intense levels of care for managing heart failure result in a lowering of the overall costs for this care in the United States. The objective of this review was to determine whether this assumption is correct. A systematic review was performed using Medline, technology assessment Web sites, and relevant cardiovascular and heart failure journals from the year 2000 to the present. US randomized controlled trials where costs were evaluated as one of the endpoints were included. Data were collected using Cochrane Review characteristics of included studies and risk of bias assessment forms. Cost data from each trial were converted to a uniform cost definition and year. Meta-analysis was performed where appropriate. Ten trials were identified evaluating costs at various time points (3, 6, and 12 months). Meta-analysis of trials demonstrated no difference in costs for care, no matter the patient condition or settings. In high-quality trials examining costs, there may be a shifting in costs from more expensive care settings to less expensive care settings without savings to the healthcare system. Larger and longer-term trials should be undertaken to understand this issue.
Heart failure (HF) affects approximately 5.7 million people in the United States. Annual direct costs for treating this condition (when it is considered in isolation) are estimated to be $35.1 billion, which cause a significant drain on healthcare resources. However, the costs for HF care are likely much higher when considered as part of other comorbid conditions. HF is a condition that results from or contributes to other comorbid conditions such as coronary atherosclerosis, cardiac dysrhythmias, cardiac valvular disease, hypertension, diabetes, chronic obstructive pulmonary disease (COPD), and other circulatory disorders. As such, the condition(s) are debilitating and ultimately result in poor quality of life. HF as a condition is associated with an increased number of hospitalizations and these hospitalizations comprise the majority (approximately 60%) of the direct annual costs for care. Further, medical advances (eg, more advanced cardiac rhythm management via cardiac electronic implantable devices and mechanical support devices such as left ventricular assist devices) are extending the life expectancy of these patients and adding costs resulting in an additional drain on healthcare resources.
Remote monitoring and care (eg, home care) for patients with HF may be a cost-effective solution to HF care provided in the inpatient setting. Remote monitoring and care can consist of simple interventions such as periodic telephone conversations between an HF patient and a caregiver (eg, disease manager). It may also consist of more advanced remote monitoring technologies such as 2-way video conferencing equipment, which measures vital signs (blood pressure, pulse oximetry, weight, electrocardiography) and transmits them wirelessly, and fluid measurement within the respiratory system. In all of the above cases, the intention was to intervene in order to prevent admission to more expensive care settings such as the hospital. These interventions can consist of adjustment of medications remotely, adjustments to diet, and physical therapy.
It is important to perform this review for the simple reason that there is conflicting evidence in high-quality studies (ie, randomized controlled trials [RCTs]) as to the cost-effectiveness of any kind of remote monitoring for HF. As examples, conflicting evidence on costs savings exists as a result of studies combining complex comorbidities (HF, chronic lung disease, and/or diabetes mellitus without an ability to breakout HF costs), studies having small sample sizes, studies examining costs from different countries with different care delivery systems,[5, 6] and studies in which costs could not be broken out for HF care only (combined all HF only and all-cause care). Further, RCTs examining remote monitoring have been evaluated for different time assessments (3 months to more than 1 year), different types of costs (eg, hospital only costs for HF care; hospital only costs for all-cause admissions; all costs [hospital, outpatient, physician care, home care] for HF care; all costs for all-cause care sought), and different types of remote monitoring interventions. It is the intention of this review to combine US trials with similar timeframes that evaluated costs to determine whether remote monitoring (as defined below) is cost-effective. In addition, since costs have been evaluated in various ways in the literature and in RCTs identified (eg, hospital charges, reimbursed services, and cost to charge ratios in other countries), it would be important to standardize costs in a way where trials can be combined in order to make costs and savings easier to understand. A method of standardizing costs is per the methodology described below.
RCTs evaluating remote monitoring of congestive heart failure (CHF) patients vs the standard of care, which included the cost for care as one of the outcomes (primary or secondary) were included in this analysis. The cost analysis had to be for at least 3 months' duration.
The definitions used for remote monitoring included any type of remote monitoring employed, whether it included telemonitoring equipment, which provided transmission via wireless, audiovisual, or phone lines, and/or disease management programs, which included periodic phone or live contact with the patient. The definition of “remote” was that the patient was in a remote location outside of a provider facility (hospital, outpatient clinic, or clinician office).
Costs were defined as the direct costs for either for all-cause care or for HF only care. Further, where possible, costs by care setting were combined (eg, hospital inpatient, hospital outpatient, physician office, and home) to obtain overall total cost. Some costs, however, could not be combined and resulted in an analysis of that setting only (eg, hospital inpatient). Studies were combined when the costs for care were examined for the same condition(s), in similar care settings, and for the same timeframe of evaluation (eg, 3, 6, and 12 months). Costs and associated statistics (standard deviation) were adjusted to obtain a common definition based on the following:
All costs were reflected in US currency for the year 2012
US charges were adjusted to reflect costs based on specific institution Medicare cost to charge (C/C) ratios for the base year
Costs are reflective of costs for care for the HF condition only or for all-cause care where noted
The following assumption for the estimation of costs was made: costs=charges adjusted for C/C=cost from hospital cost accounting system=Medicare reimbursement.
The above resulted in a 2012 $US estimated cost (plus standard deviation) in caring for HF patients during the timeframes of 3, 6, or 12 months (as reported on). This unit of analysis was examined for each study and reported as a dollar figure along with the standard deviation of that cost.
Patients with confirmed HF based on the following criterion were included in the analysis: confirmed diagnosis of HF by a clinician in the hospital setting. All patients with HF had to be eligible for discharge from the hospital and be cared for subsequent to discharge in the home setting.
The types of remote diagnostic assessment and care included the following:
The comparison/study groups consisted of the following:
Usual care for HF typically consisted of education of patient on HF condition prior to discharge and follow-up care by a primary care specialist vs predischarge education, primary care, plus disease management (typically consisting of regularly scheduled telephone/face-to-face visits by a caregiver).
Usual care was defined as education prior to hospital discharge and follow-up as care by a primary care specialist vs disease management plus a remote monitoring system. The remote monitoring system consisted of simple measures such as regular weight assessment and self-monitoring of symptoms telephoned in by the patient to disease management team to 2-way video conferencing and/or portable monitoring equipment that measured electrocardiography, blood pressure, and pulse oximetry and transmitted the results via wireless or phone lines. These transmitted findings were then assessed by disease management and therapy was adjusted accordingly (change in medications, emergency visits by disease management, patient visit to a caregiver). The disease management team was in some cases assisted in their patient assessments with decision support software/tools, which aided their decision making for care administration.
The outcome measure of interest was cost for care. Studies were included whether the cost outcome was either a primary or secondary outcome. Costs were reported by care setting and by either HF care or all care. Some studies analyzed costs only in the hospital setting and for HF and other studies captured and analyzed all costs for care (HF and all other costs) by all care settings (eg, hospital, outpatient, physician office, and home). The studies are reported as such (separately) and combined where appropriate for meta-analysis purposes. See Appendix 1 for search methods used.
Data Collection and Analysis
Data collection and analysis utilized a data collection form (available upon request). Data collection was first collected and analyzed by one of the authors (JV). It was further adjudicated by a second author (MM) independently. Any disagreements were discussed between the 2 author and final agreement on including/excluding a trial were made by consensus after discussion. Studies were selected for further evaluation if they were randomized to some sort of remote monitoring system/program vs a standard of care for monitoring patients with confirmed CHF and evaluated the costs for care in a randomized fashion for each arm of the trial (ie, the costs for care had to be either a primary or secondary endpoint of the trial). Risk of bias was evaluated using Cochrane methodology.
Statistical analysis was performed using the statistical package that accompanies Cochrane Review Manager version 5.1. For continuous data (ie, cost data), we used the mean difference. Further, if pooling of data was not possible, we used the statistical data utilized in the study for analyzing the cost effect separately.
In cases where data were missing, we attempted to contact authors. If data remained missing, however, we left it as missing and did not attempt to impute values. We assumed the data to be missing at random and therefore no bias would be introduced. In the case of abstracts, we attempted to contact authors to see whether a paper had been published in a peer-reviewed journal. If a paper had been generated from an abstract but was unpublished, we attempted to obtain it from the author.
If trials could be combined, assessment of statistical heterogeneity was made using the I2 statistic in order to determine appropriateness for meta-analysis. If the I2 statistic was at or below 60%, the heterogeneity was considered moderate and meta-analysis was appropriate. If the value was greater than 60%, sensitivity analyses was undertaken in an attempt to identify which studies were most likely causing the problem. If there were only a few such studies and they could be identified, the reasons for their difference were explored and the appropriateness of removing these studies was determined. When appropriate, the meta-analysis was performed excluding any such studies. In examining small size studies and heterogeneity, a comparison of fixed- and random-effects models were employed. If the estimates were similar, it would be concluded that any small study effects would have little effect on the intervention effect estimate.
We used a funnel plot to assess reporting bias for those studies combined for meta-analysis purposes. Each cost outcome was reported separately. Furthermore, an assessment was made of publication bias (including a review of unpublished studies), location bias (types of journals), and language bias.
A PRISMA flow diagram of study inclusion/exclusion criteria is shown in Figure 1.
Ten studies were included in the analysis.[10-19] The summary of the risk of bias assessment can be seen in Figure 2.
Thirty one trials were excluded for the following reasons: the study was randomized but did not measure HF costs as an outcome[3, 20-38]; studies were outside the United States, United Kingdom, Germany, Italy, and Canada; the study was randomized and examined some costs but costs could not be identified for HF and nursing costs/time only was evaluated (Finkelstein); and an earlier trial of one already included (Kimmelstein, Scalvini, Scalvini, Smith, Smith). Lastly, Gregory was excluded after repeated attempts to contact the author to determine the sample size and standard deviations with no additional data were received.
As it relates to inclusion and exclusion of studies, there were no disagreements for which studies should have been included and excluded.
In all cases, the rate of heterogeneity was ≤60%. Therefore, sensitivity analysis was not required. Further, due to several studies containing small numbers of patients, both fixed- and random-effects models were evaluated. Estimates were similar using both models. It was therefore concluded that any small study effects would have little effect on the intervention effect estimate.
Effects of Interventions on Costs
HF-Related Costs and All-Cause Hospital Costs at 3, 6, and 12 months
For all time points (3, 6, and 12 months), no statistical difference in the costs for care for HF hospital-related care and for all-cause admission were found (Table 1 and Table 2). This finding existed even though there were nonsignificant findings of lower costs for care in settings outside the hospital (eg, remote or lower intensity care). This lowering of cost ranged in each analysis from a savings of (realized with remote care vs usual care): $1170 (Table 2), −$1540 (Table 1) at 3 months, $590 (Table 2) −$8100 (Table 1) at 6 months, and $1480 (Table 2) −$17,440 (Table 1) at 12 months.
Table 1. Hospital Costs for All-Cause Readmissions at 3, 6, 12 Months: Meta-Analysis Findingsa
Total Sample Size
Meta-Analysis Results (Remote Care – Standard Care
IV random-effects model for continuous data. Abbreviation: CI, confidence interval.
Mean difference=−$1480; 95% CI=−$6890–$3930; P=.59; I2=21% (see Figure 5 for forest plot)
Studies that also examined hospital costs for HF care and could not be pooled for the following reasons are reported below:
Benatar 2003 examined 2 active interventions vs each other: Home nurse visit (HNV) vs nurse telemanagement (NTM). At 3 and 6 months, NTM (n=108) significantly reduced total aggregate hospital costs for group vs HNV (N=108); $65,023 vs $177,365 (P≤.02); and $223,638 vs $500,343 (P<.03), respectively. However, at 12 months the difference in the total aggregate hospital costs between the 2 groups was no longer significant: $541,378 vs $677,710 (P≤.16).
All-Cause Costs for Care: 3, 6, and 12 Months
In examining the 3, 6, and 12 months costs for all-cause costs in all care settings, there was no statistically significant difference between remote care and usual care at 3 months (mean difference, $580; 95% confidence interval [CI], −$5000 to $6160; P=0.84; I2=48%); 6 months (mean difference, $150; 95% CI=−$970 to $670; P=0.72), and 12 months (mean difference, −$560; 95% CI=−$2220 to $1110; P=0.51; I2=0%) (Table 3). Conversions to calendar year 2012 costs for various cost and charge data from each trial can be viewed in Appendix 2.
Table 3. All Care Setting Costs for Care at 3, 6, and 12 Months: Meta-Analysis Findingsa
Total Sample Size
IV Random-effects model for continuous data. Abbreviation: CI, confidence interval.
Mean difference=−$560; 95% CI=−$2220–$1110; P=0.51; I2=0% (see Figure 6 for forest plot)
When both fixed- and random-effects models were evaluated there was no difference in the findings. Random-effects models were chosen based on the relatively small sample sizes in each trial.
Of note, the Galbreath trial in 2004 did not include the costs for the disease management program in its analysis.
In the current analysis, it was examined in a more granular fashion where and when the cost savings in caring for HF from remote monitoring might be realized. What we found is that there appears. to be no cost savings early on (at 12 months) for HF-specific or all-cause treatment in either high- or lower-intensity care settings (eg, hospital, home, and physician office) What likely strengthens (further supports) this finding of no real cost savings with remote care is that the costs for the disease management program in the largest trial Galbreath were not included in the analysis, which would increase the costs for this intervention.
Much of the remote monitoring examined in this review was relatively simple and similar in nature, eg, telemonitoring, patient measurements of weight, phone contact. All related to periodic contact with the patient via personal or impersonal means were not terribly sophisticated. Since patients were intervened upon only when overt symptoms appeared, it may have been too late to realize any costs savings. There are more advanced implantable remote monitoring systems (non-CHF and HF), which monitor and alert patients (intracardiac and pulmonary artery pressures) prior to symptoms developing and have demonstrated an ability to intervene earlier.[48, 49] These types of remote systems may aid in earlier intervention (such as the titration of medications based on physiologic data), reduce costs, and improve patient satisfaction.
Differences With Other Published Reports
We have demonstrated dissimilar findings to Klersy in that remote monitoring does not reduce the overall costs for inpatient care related to HF. Dissimilar with Klersy, we did not find that remote monitoring reduces the overall costs for inpatient care related to all-cause readmissions. In the Klersy article it was found, based on modeling at 1 year, that costs were lower (ranging from ∈300 to ∈1000 [$395 to $1315 US]), favoring remote monitoring. What we found, unlike findings from Klersy, is that costs may be shifted to other care settings, resulting in no actual cost savings to the system. Further, in another prior analysis, Seto examined published trials and also found cost savings but did not perform meta-analyses. In addition, it only examined Scopus and PubMed databases, which were searched on April 10, 2007. This current review examines additional sources of information that were not examined in Seto (ie, specific journals related to CHF as per the search methodology outlined above) and performs a meta-analysis of like trials, which Seto did not perform. Lastly, this review examined RCTs only, where direct costs for care were evaluated, whereas Seto examined randomized and nonrandomized trials and direct and indirect costs (such as decreased work productivity, absenteeism, unpaid care, lost leisure time). As it relates to prior analyses, a recent Cochrane systematic review and meta-analysis published by Inglis and colleagues found that the use of remote monitoring (examining structured telephone support and telemonitoring programs only) had a positive effect on reducing the risk of all-cause mortality and CHF-related hospitalizations in patients with CHF and that it improved the quality of life, reduced costs, and evidence-based prescribing. However, with the Inglis review, two studies were included that we did not: Barth and Wakefield and associates. We found that the study by Barth and colleagues was not an RCT and that the Wakefield and coworker study did not examine costs. Further, we examined several other studies that were not examined by Inglis and colleagues, even though they were RCTs. These studies included Benatar (nurse telemanagement), Dunagan (telephonic monitoring), Gregory (nurse manager), Jerant (telecare system), and Krumholz (nurse telemanagement)..
While we did not examine other study outcomes, remote monitoring appears to improve the quality of life of people with HF (eg, as defined by improvement in New York Heart Association functional class,[10, 12, 13, 16, 38] reduce hospitalizations (for HF, reduce mortality,[12, 17] and improve evidence-based prescribing.[12, 21] However, there were also other studies in which there were no differences seen in the outcomes of: cardiovascular mortality, all-cause mortality, functional status, treatment satisfaction, depressive symptoms, or quality of life (which was a combination of functional status, depressive symptoms, and treatment satisfaction).[18, 19] Lastly, there were no studies identified that showed patients were statistically worse off in any of the above non-cost outcomes under remote care. Thus, outcomes such as quality of life (if costs are equal in all care settings) may be more relevant to decision makers (than overall costs) in deploying resources in order to care for these types of patients.
One of the issues that is of importance when examining HF is its presence in other comorbid conditions such as chronic obstructive pulmonary disease, renal impairment/disease, cardiac dysrhythmias, ischemic heart disease, diabetes, hypertension, cardiac valve disorders, hyperlipidemia, and other circulatory diseases. Due to costs associated with these conditions, an analysis that incorporates all-cause care, in all care settings (such as performed here and as others have done) is likely appropriate and useful to understand.
As noted above, we did not include RCTs from outside of the United States. The reason they were excluded had to do with their different (from the United States) cost structures and resource use patterns. However, even if these trials were included in the analyses above, there still remained no statistical difference in overall costs, no matter the setting or patient's disease state (ie, HF only or HF plus comorbid conditions) (data on file).
Limitations of this analysis include the assumption that costs as calculated by cost accounting systems are the same as costs as reflected by Medicare reimbursement and costs as converted via Medicare C/C ratios. Further, based on American Hospital Association data, the Medicare reimbursement amounts provided for care have been averaging/tracking very closely to the estimated costs for care. Most US researchers use Medicare reimbursement and C/C ratios as means of calculating costs. Therefore, we believe that the overall estimates that were derived from these costing methods are a reasonable way for calculating costs. While there were 10 studies included in this analysis, with costs examined at various time points, longer-term costs should (>1 year) should also be undertaken and larger numbers of patients should also be evaluated. Lastly, as it relates to missing data, it was assumed that data were missing at random and thus no further analysis was undertaken (imputing missing data with replacement values). As shown in Figure 2, there were trials where data were missing or it was not clear why the data were missing. We have therefore assumed that the available data for evaluation were unbiased.
Disclosures/Conflicts of Interest
All costs for the research, analysis, and preparation of this manuscript were borne solely by the authors.