Clinical Outcomes in Heart Failure Monitoring: A Pooled Rates Analysis

ABSTRACT

Objective: To understand clinical and health economic outcomes in patients receiving standard-of-care (SOC), out-of-hospital management for recently diagnosed heart failure (HF) in the US.

Study Design: Systematic literature review with a subsequent pooled rates analysis.

Methods: Researchers reviewed randomized controlled trials (RCTs) indexed in PubMed and EMBASE between 2008 and 2023. RCTs were selected as the data sources because of the standardized reporting on outcomes and prospective data. Studies included in the analysis reported on US patients recently diagnosed with HF who underwent watchful waiting after discharge. The study followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, with details reported in the PROSPERO study protocol (No. CRD42023410084). The pooled estimates of all-cause and HF-specific hospital readmissions, length of hospital stay, emergency department visits, and mortality at 3, 6, and 12 months were calculated using R software’s meta and metafor packages.

Results: There were 31 studies that met the inclusion criteria and reported data for 6916 patients with HF receiving SOC. The proportions of patients with a readmission and an emergency department visit at 3 months were 32.55% (95% CI, 24.03%-41.63%) and 13.83% (95% CI, 8.21%-20.49%), respectively. Mortality over the same period was 3.46%. Quality-of-life and cost data were heterogeneous and infrequently reported, preventing pooled analyses of these data. Length of stay had a pooled value of 7.12 days (95% CI, 5.78-8.46).

Conclusion: HF with SOC monitoring is associated with substantial health care burden. Improvements in SOC monitoring, potentially through remote monitoring and management, could be beneficial to patients, clinicians, and payers.

Am J Manag Care. 2025;31(7):In Press

Takeaway Points

• The burden of care in the US for patients with heart failure (HF) after hospital discharge is high.
• In the first 3 months after HF, 13.83% of patients have an emergency department visit.
• In the first 12 months after HF, approximately 50% of patients are readmitted to the hospital.
• Improved remote monitoring and management of patients with HF may reduce the burden of care.

Heart failure (HF), a disease associated with substantial clinical and economic burden in the US, continues to escalate in impact, with an increasing number of deaths and hospitalizations in recent years.^1,2 Current management of HF cases after patients’ initial discharge involves the patients monitoring their symptoms through watchful waiting and potentially body weight and/or blood pressure monitoring (hereafter referred to as standard-of-care [SOC] monitoring). The success of SOC monitoring relies on patients adhering to their self-monitoring program and informing their health care provider of changes in a timely and accurate manner.

Advances in medical technology have introduced novel approaches for monitoring patients with HF, including remote monitoring systems. Remote monitoring systems can take various forms; for this article, we define them as devices that continuously and objectively monitor physiological parameters. Because HF is a condition marked by periods of exacerbations,² continuous remote monitoring could provide information that would potentially decrease clinical events by allowing timely intervention.³ Decreasing hospitalizations in patients with HF would have both patient health and economic benefits, as hospitalization is one of the main cost drivers in the treatment of these patients.^1,3

To be able to quantify the value of any advanced and/or remote monitoring approach, outcomes associated with SOC monitoring need to be understood. Understanding the frequency of clinical events can inform decisions and enhance the management of the disease. To capture the clinical event rates in SOC monitoring for HF, we turned to published randomized clinical trials (RCTs). RCTs often include a current SOC in the control arm and are a credible information source because they adhere to rigorous study designs. The differing comparator interventions and the varying ways they are implemented make a comparative meta-analysis of outcomes of limited benefit. This study aimed to pool comparable data and estimate the rates of clinical outcomes in the SOC monitoring arms of published RCTs. This was done as a pooled rates analysis, also known as a meta-analysis of proportions.

METHODS

We conducted a systematic review of published RCTs. The methodology and reporting were done according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement checklist⁴ (eAppendix [available at ajmc.com]). The study protocol was registered with PROSPERO prior to conducting the review (No. CRD42023410084).

Eligibility Criteria

We included RCTs that reported the effect of monitoring on adult patients with HF in the US. Studies were excluded if the patient population was not in the US or did not have HF. Studies were also excluded if there was no monitoring done in the population. Details on the inclusion and exclusion criteria can be found in the eAppendix.

Search Strategy

The searches were conducted in 2 databases, PubMed and EMBASE, in March 2023. The detailed search strings were published in the PROSPERO protocol. Because this review aimed to identify recently published literature, the search string included studies published from 2008 until March 2023. The key search terms were heart failure, telemonitoring, wearable devices, and randomized controlled trial. The searches were restricted to RCTs that were peer-reviewed, written in English, and with human participants only.

All the identified records were screened on the platform PICO Portal. Details of the study selection, data extraction, and risk of bias assessment (Downs and Black) process can be found in the eAppendix.

Quantitative Synthesis

Primary analysis. A pooled rates analysis was carried out to estimate the pooled rate of dichotomous outcomes. It involves the synthesis of a 1-dimensional binomial measure known as (weighted) average proportions, estimated by pooling the results (proportions) from various studies and weighted by the inverse of their sampling variances.⁵ The heterogeneity of each analysis was assessed using the I² statistic. The random-effects model considers the heterogeneity within the study and among the pooled studies.⁶ For this analysis, the random-effects model was used when reporting all results for consistency. The analysis was completed in R 2.3.3 (R Foundation for Statistical Computing) using the metafor and meta packages.^7,8 Dichotomous outcomes used in the pooled analysis were the number of events per group.

Because follow-up periods varied across studies, 3 follow-up periods (3, 6, and 12 months) were analyzed for primary outcomes. If the study rates were reported outside of these follow-up periods, then a conversion was done to the closest follow-up period using the standard rate to probability conversion formula⁹:

Rate_t1 = 1 − e^{([ln(1 − ratet2) / t1] ∙ t2)}

For continuous outcomes, the pooled estimate was calculated using a meta-analysis of quantitative variable approach, analyzed with a variation of the metafor R package. However, SD or variance was required, and if neither was reported, the data were not included for further analysis. There was no restriction on the number of papers needed to be able to pool the data; however, definitions of the outcomes had to be the same.

Subgroup and sensitivity analyses. Ad hoc subgroup analysis was done on the primary variables depending on whether the outcomes were reported to be directly related to HF.

Sensitivity analysis was done to assess the robustness of the synthesized results. Studies with a high risk of bias, a disproportionately large sample size (defined as studies with sample size of 100% more than other studies in each group), or extreme outcomes were excluded.

RESULTS

Study Selection

The screening process is summarized in the PRISMA protocol flow diagram (Figure 1). EMBASE returned 1500 records, and PubMed returned 1436 records. There were 2248 unique records after deduplication. Title and abstract screening excluded 2165 records. Full-text review was done on 83 articles, and 31 articles were selected for data extraction.

Study Characteristics

The characteristics for each study are summarized in the Table.^10-40 Of the included studies, 52% (n = 16) were published between 2008 and 2012, and 81% (n = 25) were multicenter.

The 31 included studies had an overall sample size of 13,917 patients with HF, of whom 6916 received SOC monitoring. The interventions included telemonitoring, telehealth, telemedicine, telehome, computer-based telephonic monitoring devices, insertable cardiac monitoring devices with hemodynamic monitoring, telephone coaching, electronic pill bottles, and electronic disease management systems (Table). The SOC monitoring groups were most often described as usual care. However, for 6 studies, patients in the control group used a device but did not share data with the health care team.^10-13,28,35 As neither patients nor clinicians derived any actionable information from the device, this was also considered to be SOC monitoring.

Risk of Bias

The quality scoring for each study per the Downs and Black checklist can be found in the Table. Seventeen of the studies had quality ratings of good and excellent. Twelve studies were rated as fair, and 2 studies were rated as poor.

Primary Outcomes

Hospital readmission. The number of patients with at least 1 hospital readmission was reported in 20 studies, with a total of 4006 patients in the SOC monitoring arm. The pooled proportions for hospital readmissions (all-cause) at 3, 6, and 12 months are shown in Figure 2. The pooled proportion for 3-month hospital readmission was 32.55% (95% CI, 24.03%-41.63%), taken from 4 studies. Nine studies reported hospital readmissions at 6 months, and the pooled proportion was 38.58% (95% CI, 32.61%-44.72%). The proportion of hospital readmissions at 12 months was 52.21% (95% CI, 39.28%-64.99%), taken from 7 studies.

Emergency department (ED) visits. A total of 12 studies with 1515 patients in the SOC monitoring arm reported ED visits. ED visits described to be for any reason (all-cause) were reported in 7 studies (eAppendix). The 3-month ED visit estimate was 13.83% (95% CI, 8.21%-20.49%), taken from 2 studies. The 6-month estimate was 27.81% (95% CI, 14.03%-44.09%), taken from 4 studies, and the 12-month estimate was 50% (95% CI, 29.03%-70.07%), taken from 1 study.

Mortality. A total of 12 studies with a total of 3708 patients in the SOC monitoring arm reported on mortality (Figure 3). The 3-month mortality was reported most often (12 studies), giving a 3-month pooled estimate of 3.46% (95% CI, 2.12%-5.06%). The 6-month mortality was estimated as 8.57% (95% CI, 5.29%-12.51%), taken from 6 studies. The 12-month mortality, estimated from 5 studies, was 9.13% (95% CI, 6.49%-12.14%).

HF-specific events. HF-specific hospital readmissions were reported in 13 studies (Figure 4). The estimated pooled proportions were 33.19% (95% CI, 24.16%-42.83%), 28.84% (95% CI, 19.06%-39.69%), and 51.70% (95% CI, 34.41%-68.79%) for 3, 6, and 12 months, respectively. There were 2 definitions of ED visits, specifically HF and HF requiring intervention (eAppendix). The pooled proportion with HF-specific ED visits was 32% (95% CI, 14.91%-51.78%) at 6 months, which was reported in 1 study. Four studies reported ED visits with HF requiring intervention. The pooled proportion of ED visits with HF requiring intervention was 7.73% (95% CI, 3.92%-12.53%) for 6 months and 4.79% (95% CI, 3.31%-6.52%) for 12 months. Additionally, HF-specific mortality was reported by 2 studies at 6 months (eAppendix). The pooled proportion was estimated to be 5.92% (95% CI, 2.82%-9.92%).

Secondary Outcomes

The secondary outcomes were not as frequently reported. Six studies (19.3%) reported on the length of hospital stay for readmissions.^{10,16,19,20,35,36} The pooled estimate of length of hospital stay was 7.12 days (95% CI, 5.78-8.46) for readmissions (eAppendix).

Ten studies (32.2%) reported on quality of life (QOL), with the Minnesota Living with Heart Failure Questionnaire and the Kansas City Cardiomyopathy Questionnaire (KCCQ) being used equally.^{10,15,16,23-25,30,31,33,34} EuroQol (EQ-5D) and the 36-item Short Form Survey (SF-36) QOL measures were each reported once. As our registered protocol included only meta-analysis of KCCQ, no pooled analyses of other QOL measures were performed. The KCCQ was reported without an SD, making it impossible to pool the values. The changes from baseline ranged from 6.80 to 9.30, 6.14 to 10.00, and 4.12 to 16.70 for 3, 6, and 12 months, respectively (details in the eAppendix).

Four studies (12.9%) reported on costs, with the most commonly collected being total cost of care, which ranged from $11,000 to $52,000 (2022 US$) per patient.^16,34,37,38 However, substantial heterogeneity in the studies prevented pooled analysis.

There was only 1 study that reported time to readmission; the mean (SD) time was 41.2 (24) days.³⁴ Similarly, time to death was also reported in only 1 study; the mean (SD) time was 724 (433) days.¹⁶ One study included the previous 2 outcomes as a composite end point and could therefore not be included in further analysis.³¹

Sensitivity Analysis

The sensitivity analysis for study size had a significant impact on the ED visit results and relatively minimal impact on results for mortality and readmissions. The general impact of the study quality on the pooled results was minimized owing to only 2 studies with a poor quality rating being included. Studies that had outliers had a low to medium effect on the results, with the largest impact being on the ED visit result. The graphs for the sensitivity analyses can be found in the eAppendix.

DISCUSSION

The adverse outcomes and events in the HF population represent a substantial health care burden, and methods that alleviate these would be beneficial to patients and payers alike. Our analysis of hospital readmission rates revealed that approximately 30% of adult patients with HF receiving SOC monitoring were readmitted within 3 months, 40% within 6 months, and 50% within 12 months after initial HF hospitalization. HF-specific readmissions mirrored all-cause readmissions, except for the 6-month readmission rate (28.84%), which was lower than that of all-cause readmissions and does not follow the pattern of increasing over follow-up time. A previously performed systematic literature review reported that rehospitalization rates at 30 days ranged from 21.4% to 22.3%.⁴¹ These data reaffirm the substantial rate of readmission within the first year of follow-up. Each readmission may result in an estimated 7.12 days in hospital. The length of stay is consistent across the studies. Readmission to hospital may cause distress for patients and additional burden on health care resources.

To help prioritize appropriate care and minimize the cost of preventable readmissions, CMS launched the Hospital Readmissions Reduction Program (HRRP) in 2012.⁴² Under this program, CMS reduced payments to hospitals with higher-than-expected readmission rates.⁴² The findings of our study show that there is room for improvement in reducing hospital readmissions following an HF diagnosis. There are potential avenues for HF-specific interventions to explore to facilitate more effective patient monitoring protocols post hospital discharge. One of the potential avenues may be the use of automation and passive monitoring as opposed to the self-reporting used in SOC. Automated passive remote monitoring can collect multiple data points simultaneously without the need for patient action. This may allow for collection of more data points⁴³ and facilitate easier identification of incremental changes and patient deterioration. The months after HF diagnoses are linked to an increased risk of adverse events and have been called the vulnerable phase of HF.⁴⁴ Our results support this, with 50% of patients having an ED visit for any reason in the first year after index hospitalization. Definitions of ED visits varied substantially by study, preventing large, pooled analyses but allowing for identification of trends. Of potential interest is that ED visits with an HF intervention are substantially less frequent than all-cause ED visits. This contrasts with readmissions, the vast majority of which are linked to HF. In the work of Chiu et al, congestive HF was shown to be significantly overrepresented (12.6%) in persistent frequent users of the ED compared with non–persistent frequent users of the ED (5.8%).⁴⁵ Patients with HF may be at increased risk of other-cause ED visits due to comorbidities, although it seems unlikely that these could account for the 45–percentage point differential between all-cause and HF-intervention ED visits. Potentially, these patients are visiting the ED out of an abundance of caution because of their health status. If this is the case, providing improved at-home/remote monitoring to make them feel more secure could result in a substantial decrease in ED visits. Studies to investigate this link should be considered. Understanding the reasons why people come to the ED is nuanced and multifactorial. One study looked at this in more detail, and results showed that many other factors such as socioeconomic factors, personal health behaviors, and access also may lead to patients visiting the ED more regularly.⁴⁶

The heterogeneity of the data among the studies we reviewed is still evident, as the cumulative counts of events decreased over time for the ED visits involving HF intervention. The 2 studies that reported this outcome had a very specific definition of ED visits that included “intravenous diuretic therapy.” One study reported ED visits for either HF or diabetes, which we classified as all-cause because we could not be certain that the visit was specific to HF.

Regarding mortality, the pooled estimate more than doubled between 3 months (3.46%) and 6 months (8.57%). The change was small between 6 months (8.57%) and 12 months (9.13%). That all-cause mortality is higher than HF-specific mortality is no surprise, as these patients also have a high burden of comorbidities.^44,47 Mortality for the subpopulation who require an inpatient readmission has been reported to be higher than 30% at 12 months.⁴⁸ In a review of global HF studies, the mortality for all adults was 24% at 12 months.⁴⁹ Our pooled value was lower, which might indicate that these trial participants are generally healthier than the broader population of patients with HF.

Our review reveals that although almost one-third of the analyzed RCTs reported on patient QOL, no single QOL scale dominated, making comparisons among studies difficult. Alongside the use of general QOL questionnaires, the more standardized use of one of the disease-specific QOL measures would increase the utility of the information and allow for some analysis between studies. Furthermore, the standard measures used in cost-utility analysis for a health technology assessment submission, EQ-5D and SF-36, were rarely used. This complicates placing the burden of HF and the impact of interventions in the context of other population health problems. Such analyses have gained importance in the decision-making processes of policy makers and governments. Considering the increasing value of QOL information, including disease-specific and generic QOL measurements as an outcome in clinical trials for monitoring would be beneficial. In addition to QOL, clinical trials could include more information on the cost of care. Alongside clinical trials, real-world evidence such as that available for pharmaceuticals in registries and databases would be advantageous. Unfortunately, nonimplanted medical devices, including many monitoring devices, are generally not recorded. More data in this area would support decision-making regarding monitoring devices. Costs of care were rarely collected in the analyzed RCTs. From other literature, it is known that the annual cost per patient for HF in the US is reported to be $30,000.⁴⁸ The total cost of HF is projected to rise due to increasing health care costs and the growing prevalence of heart disease.⁴⁸ Reducing hospital readmissions is a key strategy to lower the cost of HF—for example, via the HRRP. This, however, is focused on hospitals, and it is crucial that the focus on patient safety is not lost. Although monitoring of physiological parameters may increase patient awareness and empowerment, the RCTs reviewed did not indicate whether there is a potential causal relationship between monitoring and adherence to guideline-directed medical therapy. Investigating this was outside of the scope of this review, but it could be an interesting area for future study. Education needs to be at the forefront of the discharge process, empowering patients to monitor their health and react appropriately to changes in their health status. Suitable education post discharge has been found to increase patients’ ability to care for themselves and to manage their symptoms.⁵⁰ Education could complement the benefits that patients receive from monitoring devices, as they may have a better understanding of what the changes in the values mean.

Although RCTs are a more controlled environment, there have been some policy changes in the time frame of this review that may have influenced the results. The HRRP was implemented and penalties started in 2012, which may have reduced the readmissions.⁴² Furthermore, the COVID-19 pandemic dramatically influenced the health landscape.⁵¹ During the early months of the pandemic, there was a decrease in HF readmissions, which may be linked to the hesitancy of the general population to go to hospitals.⁵¹ The impact of these upheavals on the RCTs may not be as noticeable, but this is dependent on the study design itself.

This study looked at the SOC only, but it would be interesting to investigate the effect of the remote monitoring and management. However, considering the variability in what is measured by remote monitoring devices, any evaluation of the devices would need to pool devices accordingly.

Limitations

The results here are taken exclusively from RCTs from the US, and the results may not be generalizable to other countries. As almost half of the studies were rated fair or poor, potential risk of bias in the RCT findings should be considered when interpreting the individual results and, therefore, the pooled results as reported here. Another potential limitation is the high level of heterogeneity in studies included. Some of this heterogeneity may have been driven by varying definitions of SOC monitoring. In some studies, the SOC arm included a monitoring device being worn by the patient but the data being neither available to patient nor clinicians nor used in the decision-making process for HF management.^10,12,13 Still, the placebo effect of wearing a monitoring device could have facilitated changes in patient behavior. Patients may have been more aware of changes in their symptoms, or they may have felt more secure with the device. The definitions of ED visits varied such that it was not possible to have a pooled rate for each time point for all definitions. The lack of consistent definitions makes it challenging to compare results among clinical trials. There is room for the development of clear consistent definitions for primary outcomes in HF.

Standard guideline processes were followed to try to minimize the impact of the above limitations on our analysis. The random-effects model was used to counteract the high heterogeneity in the data, and in the case of ED visits, the data were pooled by definition. Sensitivity analyses were also performed to quantify the impact of uncertainty on the estimates presented here. Results of our analyses were stable to changes incorporated as part of the sensitivity analyses, and it is clear that many patients receiving SOC monitoring are readmitted to hospital within 12 months of discharge. Any unplanned readmission is one too many, and there is substantial scope to improve monitoring for patients with HF.

CONCLUSIONS

This review provides objective numbers of readmissions, ED visits, and mortality in patients with HF in the US. A readmission rate at 32.55% in 3 months is high and demonstrates the scope for improving patient care. Values reported here could help guide further research and analyses on health policy and health economic impact. We encourage researchers undertaking RCTs to consider collecting data on costs and QOL. 

Acknowledgments

The authors thank Dr Marco Caterino, Ms Kim Seemann, and Ms Friederike Madea for support with the screening of abstracts and/or full texts and data extraction. They thank Dr Virginie Mittard for proofreading the manuscript.

Author Affiliations: Coreva Scientific GmbH & Co KG (US, JH, ABS, RS), Koenigswinter, North Rhine-Westphalia, Germany; ZOLL Medical (AV), Pittsburgh, PA; Mayo Clinic Arizona (DES), Phoenix, AZ.

Source of Funding: Funding for the development of this systematic review and meta-analysis was provided by ZOLL Medical.

Author Disclosures: Mr Silas, Dr Hafermann, and Ms Bosworth Smith are employees of Coreva Scientific GmbH & Co KG, which received consultancy fees for performing, analyzing, and communicating the work presented here. Mr Veloz is an employee of ZOLL Medical, which manufactures HFMS, a heart failure monitoring system. Dr Saunders is the founder and CEO of Coreva Scientific GmbH & Co KG. Dr Steidley reports no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (US, JH, AV, RS, DES); acquisition of data (JH, ABS); analysis and interpretation of data (US, ABS, AV, RS); drafting of the manuscript (ABS, AV, RS, DES); critical revision of the manuscript for important intellectual content (US, JH, AV, RS, DES); and statistical analysis (US).

Address Correspondence to: Rhodri Saunders, PhD, Coreva Scientific GmbH & Co KG, Im Muehlenbruch 1, 53639 Koenigswinter, Germany. Email: peer-review@coreva-scientific.com.

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