Use of mHealth Devices to Screen for Atrial Fibrillation: Cost-Effectiveness Analysis

Health Economic Research Assessing the Value of Early Detection of Cardiovascular Disease: A Systematic Review

INTRODUCTION

Cardiovascular disease (CVD) is the most prominent cause of death worldwide and has a major impact on healthcare budgets. While early detection strategies may reduce the overall CVD burden through earlier treatment, it is unclear which strategies are (most) efficient.

AIM

This systematic review reports on the cost effectiveness of recent early detection strategies for CVD in adult populations at risk.

METHODS

PubMed and Scopus were searched to identify scientific articles published between January 2016 and May 2022. The first reviewer screened all articles, a second reviewer independently assessed a random 10% sample of the articles for validation. Discrepancies were solved through discussion, involving a third reviewer if necessary. All costs were converted to 2021 euros. Reporting quality of all studies was assessed using the CHEERS 2022 checklist.

RESULTS

In total, 49 out of 5552 articles were included for data extraction and assessment of reporting quality, reporting on 48 unique early detection strategies. Early detection of atrial fibrillation in asymptomatic patients was most frequently studied (n = 15) followed by abdominal aortic aneurysm (n = 8), hypertension (n = 7) and predicted 10-year CVD risk (n = 5). Overall, 43 strategies (87.8%) were reported as cost effective and 11 (22.5%) CVD-related strategies reported cost reductions. Reporting quality ranged between 25 and 86%.

CONCLUSIONS

Current evidence suggests that early CVD detection strategies are predominantly cost effective and may reduce CVD-related costs compared with no early detection. However, the lack of standardisation complicates the comparison of cost-effectiveness outcomes between studies. Real-world cost effectiveness of early CVD detection strategies will depend on the target country and local context.

REGISTRATION OF SYSTEMATIC REVIEW

CRD42022321585 in International Prospective Registry of Ongoing Systematic Reviews (PROSPERO) submitted at 10 May 2022.

Cost-Effectiveness of Screening to Identify Patients With Atrial Fibrillation: A Systematic Review

BACKGROUND

Screening for Atrial Fibrillation (AF) is recommended for people aged above 65 years. Screening for AF in asymptomatic individuals can be beneficial by enabling earlier diagnosis and the commencement of interventions to reduce the risk of early events, thus improving patient outcomes. This study systematically reviews the literature about the cost-effectiveness of various screening methods for previously undiagnosed AF.

METHODS

Four databases were searched to identify articles that are cost-effectiveness studies conducted on screening for AF published from January 2000 to August 2022. The Consolidated Health Economic Evaluation Reporting Standards 2022 checklist was used to assess the quality of the selected studies. A previously published approach was used to assess the usefulness of each study for health policy makers.

RESULTS

The database search yielded 799 results, with 26 articles meeting the inclusion criteria. Articles were categorised into four subgroups: (i) population screening, (ii) opportunistic screening, (iii) targeted, and (iv) mixed methods of screening. Most of the studies screened adults ≥65 years of age. Most studies were performed from a 'health care payer perspective' and almost all studies used 'not screening' as a comparator. Almost all screening methods assessed were found to be cost-effective in comparison to 'not screening'. The reporting quality varied between 58% to 89%. The majority of the studies were found to be of limited usefulness for health policy makers, as none of the studies made any clear statements about policy change or implementation direction.

CONCLUSION

All approaches of AF screening were found to be cost-effective compared with no screening, while opportunistic screening was found to be the optimal approach in some studies. However, screening for AF in asymptomatic individuals is context specific and likely to be cost-effective depending on the population screened, screening approach, frequency, and the duration of screening.

Cost-Effectiveness of Atrial Fibrillation Screening Strategies: A Systematic Review

Background

Atrial fibrillation (AF) is the most common cardiac arrhythmia. AF is associated with an increased risk of stroke. We aimed to review systematically the cost-effectiveness of screening strategies for patients with AF.

Methods

To find related research and articles, articles published in Iranian and international databases by using a combination of MeSH (Medical Subject Headings) terms and based on inclusion and exclusion criteria were searched and reviewed until Dec 2020. The main outcome measures of the final articles were incremental cost-effectiveness ratios (ICER) per gained or additional quality-adjusted life years (QALYs), additional case detected, and avoided stroke.

Results

Out of 3,360 studies found, finally, fifteen studies were included in the research. The lowest ICER numerical value was 78.39 for AF screening using ECG for 65-85 yr old Japanese women. The highest value of this index is equal to 70864.31 for performing ECG monitoring for more than 60 d for Canadians over 80 yr without AF history. In two studies, the results were expressed with the years of life gained (YLG measure. Of course, in one study, the results were not reported with this measure, and in one study, the results were reported with ICER.

Conclusion

Most of the studies acknowledged the cost-effectiveness of different AF screening strategies. However, studies that confirmed the cost-effectiveness of population-based screening were more than studies that confirmed the cost-effectiveness of other screening strategies.

Cost-Effectiveness of Mobile Health-Based Integrated Care for Atrial Fibrillation: Model Development and Data Analysis

BACKGROUND

Mobile health (mHealth) technology is increasingly used in disease management. Using mHealth tools to integrate and streamline care has improved clinical outcomes of patients with atrial fibrillation (AF).

OBJECTIVE

The aim of this study was to investigate the potential clinical and health economic outcomes of mHealth-based integrated care for AF from the perspective of a public health care provider in China.

METHODS

A Markov model was designed to compare outcomes of mHealth-based care and usual care in a hypothetical cohort of patients with AF in China. The time horizon was 30 years with monthly cycles. Model outcomes measured were direct medical cost, quality-adjusted life years (QALYs), and incremental cost-effectiveness ratio (ICER). Sensitivity analyses were performed to examine the robustness of the base-case results.

RESULTS

In the base-case analysis, mHealth-based care gained higher QALYs of 0.0730 with an incurred cost of US $1090. Using US $33,438 per QALY (three times the gross domestic product) as the willingness-to-pay threshold, mHealth-based care was cost-effective, with an ICER of US $14,936 per QALY. In one-way sensitivity analysis, no influential factor with a threshold value was identified. In probabilistic sensitivity analysis, mHealth-based care was accepted as cost-effective in 92.33% of 10,000 iterations.

CONCLUSIONS

This study assessed the expected cost-effectiveness of applying mHealth-based integrated care for AF according to a model-based health economic evaluation. The exploration suggested the potential cost-effective use of mHealth apps in streamlining and integrating care via the Atrial fibrillation Better Care (ABC) pathway for AF in China. Future economic evaluation alongside randomized clinical trials is highly warranted to verify the suggestion and investigate affecting factors such as geographical variations in patient characteristics, identification of subgroups, and constraints on local implementation.

Will Apple devices' passive atrial fibrillation detection prevent strokes? Estimating the proportion of high-risk actionable patients with real-world user data

OBJECTIVE

Utilizing integrated electronic health record (EHR) and consumer-grade wearable device data, we sought to provide real-world estimates for the proportion of wearers that would likely benefit from anticoagulation if an atrial fibrillation (AFib) diagnosis was made based on wearable device data.

MATERIALS AND METHODS

This study utilized EHR and Apple Watch data from an observational cohort of 1802 patients at Cedars-Sinai Medical Center who linked devices to the EHR between April 25, 2015 and November 16, 2018. Using these data, we estimated the number of high-risk patients who would be actionable for anticoagulation based on (1) medical history, (2) Apple Watch wear patterns, and (3) AFib risk, as determined by an existing validated model.

RESULTS

Based on the characteristics of this cohort, a mean of 0.25% (n = 4.58, 95% CI, 2.0-8.0) of patients would be candidates for new anticoagulation based on AFib identified by their Apple Watch. Using EHR data alone, we find that only approximately 36% of the 1802 patients (n = 665.93, 95% CI, 626.0-706.0) would have anticoagulation recommended even after a new AFib diagnosis.

DISCUSSION AND CONCLUSION

These data suggest that there is limited benefit to detect and treat AFib with anticoagulation among this cohort, but that accessing clinical and demographic data from the EHR could help target devices to the patients with the highest potential for benefit. Future research may analyze this relationship at other sites and among other wearable users, including among those who have not linked devices to their EHR.

Cost-effectiveness analysis of screening for first-degree relatives of patients with bicuspid aortic valve

AIMS

Bicuspid aortic valve (BAV) is the commonest congenital heart valve malformation, and is associated with life-threatening complications. Given the high heritability index of BAV, many experts recommend echocardiography screening for first-degree relatives (FDRs) of an index case. Here we aim to evaluate the cost-effectiveness of such cascade screening for BAV.

METHODS

Using a decision-analytic model, we performed a cost-effectiveness analysis of echocardiographic screening for FDRs of BAV index case. Data on BAV probabilities and complications among FDRs were derived from our institution's BAV familial cohort and from the literature on population-based BAV cohorts with long-term follow-up. Health gain was measured as quality-adjusted life years (QALYs). Cost inputs were based on list prices and literature data. One-way and probabilistic sensitivity analyses were performed to account for uncertainty in the model's variables.

RESULTS

Screening of FDRs was found to be the dominant strategy, being more effective and less costly than no screening, with savings of €644 and gains of 0.3 QALYs. Results were sensitive throughout the rang of the model's variables, including the full range of reported BAV rates among FDRs across the literature. A gradual decrease of the incremental effect was found with the increase in screening age.

CONCLUSIONS

This economic evaluation model found that echocardiographic screening of FDRs of BAV index case is not only clinically important but also cost-effective and cost-saving. Sensitivity analysis supported the model's robustness, suggesting its generalization.

Impact of Mobile Health Devices for the Detection of Atrial Fibrillation: Systematic Review

Background

Atrial fibrillation (AF) is the most common arrhythmia, and its prevalence is increasing. Early diagnosis is important to reduce the risk of stroke. Mobile health (mHealth) devices, such as single-lead electrocardiogram (ECG) devices, have been introduced to the worldwide consumer market over the past decade. Recent studies have assessed the usability of these devices for detection of AF, but it remains unclear if the use of mHealth devices leads to a higher AF detection rate.

Objective

The goal of the research was to conduct a systematic review of the diagnostic detection rate of AF by mHealth devices compared with traditional outpatient follow-up. Study participants were aged 16 years or older and had an increased risk for an arrhythmia and an indication for ECG follow-up—for instance, after catheter ablation or presentation to the emergency department with palpitations or (near) syncope. The intervention was the use of an mHealth device, defined as a novel device for the diagnosis of rhythm disturbances, either a handheld electronic device or a patch-like device worn on the patient’s chest. Control was standard (traditional) outpatient care, defined as follow-up via general practitioner or regular outpatient clinic visits with a standard 12-lead ECG or Holter monitoring. The main outcome measures were the odds ratio (OR) of AF detection rates.

Methods

Two reviewers screened the search results, extracted data, and performed a risk of bias assessment. A heterogeneity analysis was performed, forest plot made to summarize the results of the individual studies, and albatross plot made to allow the P values to be interpreted in the context of the study sample size.

Results

A total of 3384 articles were identified after a database search, and 14 studies with a 4617 study participants were selected. All studies but one showed a higher AF detection rate in the mHealth group compared with the control group (OR 1.00-35.71), with all RCTs showing statistically significant increases of AF detection (OR 1.54-19.16). Statistical heterogeneity between studies was considerable, with a Q of 34.1 and an I² of 61.9, and therefore it was decided to not pool the results into a meta-analysis.

Conclusions

Although the results of 13 of 14 studies support the effectiveness of mHealth interventions compared with standard care, study results could not be pooled due to considerable clinical and statistical heterogeneity. However, smartphone-connectable ECG devices provide patients with the ability to document a rhythm disturbance more easily than with standard care, which may increase empowerment and engagement with regard to their illness. Clinicians must beware of overdiagnosis of AF, as it is not yet clear when an mHealth-detected episode of AF must be deemed significant.