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Heart failure (HF) is a major public health issue in Canada that is associated with high prevalence, morbidity, and mortality rates and high financial and social burdens. Telemonitoring (TM) has been shown to improve all-cause mortality and hospitalization rates in patients with HF. The
This study aims to conduct a cost-utility analysis of the
Using a microsimulation model, individual patient data were simulated over a 25-year time horizon to compare the costs and quality-adjusted life years (QALYs) between the
The
The
Heart failure (HF) is a major public health issue with a worldwide prevalence of 26 million and 669,600 in Canada [
It has been recommended that disease management interventions that enable patient empowerment, education, and clinical follow-up should be integrated within the system of care for patients with HF because these interventions have been associated with reduced hospitalization rates and improved quality of life (QoL) and survival [
The decision to implement interventions are often dependent on cost-effectiveness. However, there is a lack of economic evidence for TM stemming from the challenges associated with conducting economic evaluations owing to the heterogeneity and complexity of TM [
The objective of this study was to evaluate the long-term cost-effectiveness of TM for patients with HF within a Canadian context from a public health care payer perspective, referencing costing data and concepts from a TM program,
The methods used in this study follow the Consolidated Health Economic Evaluation Reporting Standards [
A CUA was performed. A CUA was chosen because it allows for the effectiveness outcome to be comparable with that of other disease groups and across interventions, making it the gold standard for economic evaluations. Furthermore, there is utility evidence available for patients with HF, allowing for the use of quality-adjusted life years (QALYs) [
The target population was a cohort of ambulatory patients with HF from the UHN in Toronto, Canada, enrolled in the
Baseline patient characteristics of the Medly Program Evaluation cohort (n=315; number of patients unless specified otherwise).
Characteristics | Overall | Missing, n |
Age (years), mean (SD) | 58.23 (15.43) | 2 |
Proportion of females, n (%) | 69 (22.0) | 2 |
Proportion of ischemic etiology, n (%) | 65 (28.5) | 87 |
Proportion of beta blockers, n (%) | 270 (89.4) | 13 |
Proportion of aldosterone blockers, n (%) | 215 (71.2) | 13 |
Proportion of ARBsa, n (%) | 82 (27.2) | 13 |
Proportion of ACEb inhibitors, n (%) | 137 (45.5) | 13 |
Proportion of allopurinol, n (%) | 41 (13.6) | 13 |
Percentage LVEFc, mean (SD) | 32.07 (13.62) | 7 |
New York Health Association (average class), mean (SD) | 2.36 (0.59) | 13 |
Systolic blood pressure (mm Hg), mean (SD) | 110.36 (17.91) | 53 |
Percentage of lymphocytes, mean (SD) | 22.18 (9.07) | 52 |
Sodium (mEq/L), mean (SD) | 137.73 (3.06) | 33 |
Cholesterol (mg/dL), mean (SD) | 154.77 (52.71) | 83 |
Hemoglobin (g/dL), mean (SD) | 13.33 (1.99) | 52 |
Urate (mg/dL), mean (SD) | 7.97 (2.70) | 86 |
Weight (kg), mean (SD) | 83.39 (20.04) | 44 |
Furosemide-equivalent dose (mg/day), mean (SD) | 99.57 (123.93) | 16 |
Proportion of implantable cardioverter-defibrillators, n (%) | 165 (56.5) | 23 |
aARB: angiotensin II receptor blocker.
bACE: angiotensin-converting-enzyme.
cLVEF: left ventricular ejection fraction.
In August 2016, the
The main component of the program is the
Medly app showing instructions for required readings (screen 1), the symptoms questionnaire (screen 2), and personalized self-care feedback (screen 3).
This study referenced the ongoing
In this analysis, the intervention group was a cohort of patients with HF enrolled in the
This analysis was conducted from the perspective of the public payer, namely the Ontario Ministry of Health because Medly is currently implemented in a publicly funded health care system.
A time horizon of 25 years was adopted to determine the long-term cost and outcomes associated with
All analysis and model construction were conducted using RStudio [
The modeling technique chosen was a patient-level state-transition model, also known as a first-order Monte Carlo microsimulation. This model was appropriate because it can capture patient heterogeneity that is common in patients with HF and is also the preferred option for modeling chronic disease [
Conceptual representation of the microsimulation model structure. States 1 to 4 represent the transitions between New York Health Association classes. States 5 and 6 show transitions into and between hospitalization states. State 7 is an absorbing state representing death, where all states can transition to. NYHA: New York Health Association.
The values used in the model were based on a literature review. The values inputted into the model are conditional on patient characteristics. Patients with a more limited functional capacity by NYHA class have a higher risk of hospitalization [
To generate virtual patient profiles, a Cholesky decomposition was performed on a correlation matrix that describes the interdependence between patient characteristics [
The 2 primary outcomes that TM interventions for patients with HF aim to improve are all-cause mortality and all-cause hospitalization rates. The risk of all-cause hospitalization was based on evidence from the
Each state in the model has an associated utility value between 0 and 1. Utility values for each health state were derived on the basis of values from other health economic evaluations of HF interventions. NYHA classes are commonly used to categorize patients with HF based on severity of symptoms, and studies have estimated utility values for each class [
Model parameters conditional on New York Health Association (NYHA) class including living with heart failure costs (in Can currency), utilities, probability of hospitalization, and transitions between NYHA classes.
Description | NYHAa I | NYHA II | NYHA III | NYHA IVb | Source | Distribution | |
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Emergency department costs, Can $ (US $) | 0.00 | 0.00 | 62.83 (47.20) | 62.83 (47.20) | Medly Program Evaluation | Gamma |
|
General practitioner visit costs, Can $ (US $) | 0.00 | 0.00 | 12.87 (9.67) | 12.87 (9.67) | Medly Program Evaluation | Fixed |
|
Drug costs (only if patient age is ≥65), Can $ (US $) | 52.00 (39.06) | 52.00 (39.06) | 79.43 (59.67) | 208.16 (156.38) | Kaul et al (2011) [ |
Gamma |
|
Outpatient costs, Can $ (US $) | 97.00 (72.87) | 97.00 (72.87) | 97.00 (72.87) | 97.00 (72.87) | Medly Program Evaluation | Gamma |
|
Total monthly cost of living with heart failure, Can $ (US $) | 149.00 (111.94) | 149.00 (111.94) | 252.13 (189.42) | 380.86 (286.14) | OCCIc[ |
N/Ae |
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Living with heart failure | 0.81 (0.81-0.90) | 0.72 (0.72-0.83) | 0.59 (0.59-0.74) | 0.508 (0.508-0.59) | Yao et al (2008) [ |
Beta |
|
Probability of all-cause hospitalization | 0.0152 (0.008-0.023) | 0.024 (0.012-0.036) | 0.024 (0.012-0.036) | 0.154 (0.077-0.231) | Ford et al (2012) [ |
Beta |
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NYHA I | 0.977 | 0.019 | 0.004 | 0 | Flather et al (2005) [ |
Dirichlet |
|
NYHA II | 0.008 | 0.981 | 0.01 | 0.001 | Flather et al (2005) [ |
Dirichlet |
|
NYHA III | 0 | 0.034 | 0.96 | 0.006 | Flather et al (2005) [ |
Dirichlet |
|
NYHA IV | 0 | 0 | 0.055 | 0.945 | Flather et al (2005) [ |
Dirichlet |
aNYHA: New York Health Association.
bNYHA IV assumed same as NYHA III, except drug cost.
cOCCI: Ontario Case Costing Initiative.
dSOB: Schedule of Benefits for Physician Services.
eN/A: not applicable.
Health care utilization was based on data from the
Of note, the number of outpatient visits for both the intervention and comparator-simulated cohorts was limited to those that occurred at UHN because information outside of UHN’s services was unavailable at the time of the study. Furthermore, self-reported ED visits were used because UHN patient records may underreport the true number of ED visits as patients may live at some distance from UHN and may instead visit a community hospital for an emergency. Self-reported GP visits were used because UHN data do not record this information.
Median health care utilization over 6 months before using Medly, unit costs per service (in Can currency), and associated distribution stratified by New York Health Association (NYHA) classes. N is the number of patients in each NYHA class.
Type of resource | Unit cost, mean (SD), Can $ | Source for unit cost | NYHA I health care utilization (n=44) | NYHA II health care utilization (n=166) | NYHA III health care utilization (n=93) | NYHA IV health care utilization (n=1) | Distribution |
Emergency department (self-reported) | 377.00 (374.00) | Ontario Case Costing Initiative | 0 | 0 | 1 | —a | Negative binomial |
Outpatient visit | 291.33 (161.11) | Kaul et al (2011) [ |
2 | 1 | 2 | — | Negative binomial |
General practitioner visit (self-reported) | 77.20 | Schedule of Benefits | 0 | 1 | 2 | — | Negative binomial |
Drug costs over 6 months | 1248.96 (2233.52) | Kaul et al (2011) [ |
— | — | — | — | Gamma |
aNo data available.
Costs related to implementation and maintenance of
The variable cost was based on a mix of models where users can fall into 1 of the 3 categories: Full Kit (FK), Bring Your Own Phone (BYOP), and Bring Your Own Everything (BYOE). An FK user refers to a user who was provided with all necessary equipment for the technology, which is currently funded by the
Parameter estimates not conditional on the New York Health Association class including hospitalization costs (in Can currency) and disutility, readmission rates, Medly costs (in Can currency), and Medly effectiveness estimates.
Parameters | Value | Source | Distribution | |
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Hospitalization cost per admission (Can $), mean (SD) | 8908 (16,867) | OCCIa | Gamma |
|
Hospitalization length of stay days, mean (SD) | 5.9 (11.2) | OCCI | Log normal |
|
Medly fixed costs for site implementation | 102,500 | Medly program | Fixed |
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Medly operational cost per patient (cost per month), Can $ | 44.67 | Medly program | Fixed |
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Medly Full Kit cost per patient (cost per month), Can $ | 67.56 | Medly program | Fixed |
|
Medly Bring-Your-Own-Phone cost per patient (cost per month), Can $ | 18.87 | Medly program | Fixed |
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Medly Bring-Your-Own-Everything cost per patient (cost per month), Can $ | 3.80 | Medly program | Fixed |
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30-day readmission probability (95% CI) | 0.159 (0.089-0.159) | Yeung et al (2012) [ |
Beta |
|
Disutility for hospitalization (95% CI) | 0.059 (0-0.11) | Sandhu et al (2015) [ |
Beta |
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RRb for hospitalization | 0.857 (0.703-1.014) | Medly | Log normal |
|
RR for morality | 0.81 (0.70-0.94) | Yun et al (2018) [ |
Log normal |
aOCCI: Ontario Case Costing Initiative.
bRR: relative risk.
The expected values for all model parameters were used for the deterministic analysis. The cohort size was assumed to be 1000 patients. Each simulated patient progressed through the model twice until death; once as a patient using
Monte Carlo standard errors were also reported to show how the results vary owing to patient heterogeneity and randomness introduced from patients transitioning to each state.
A second-order probabilistic analysis was also conducted to characterize the uncertainty in the deterministic results. Each parameter in the model was assigned a distribution based on the nature of the input parameter [
Currently, most patients enrolled in the
As mentioned, the
One-way deterministic analyses were also conducted to address the uncertainty associated with the estimates for
Over a 25-year time horizon, the average total costs were Can $97,497 (US $73,547.84) for the comparator group and Can $102,508 (US $77,327.93) for patients using
Deterministic results of the reference case analysis.
Reference | Costs, Can $ (US $) | MCSEa, Can $ (US $) | QALYsb | MCSE | Incremental cost, Can $ (US $) | MCSE, Can $ (US $) | Incremental QALYs | MCSE | ICERc, Can $ (US $; $/QALY) |
Comparator | 97,497 (73,831) | 3948 (2989) | 4.95 | 0.12 | N/Ad | N/A | N/A | N/A | N/A |
Medly | 102,508 (77,626) | 3592 (2720) | 5.51 | 0.13 | 5011 (3794) | 2014 (1525) | 0.57 | 0.05 | 8850 (6701) |
aMCSE: Monte Carlo standard error.
bQALY: quality-adjusted life year.
cICER: incremental cost-effectiveness ratio.
dN/A: not applicable.
On the basis of 1000 simulations of the reference case scenario in which each parameter was sampled from their respective distribution, 81.6% (816/1000) of the simulations showed that
Cost-utility plane of 1000 iterations from the reference case probabilistic analysis. QALY: quality-adjusted life years.
Cost-effectiveness acceptability curve of reference case probabilistic analysis.
Deterministic results for New York Health Association classes I, II, and III.
NYHAa classes | Costs, Can $ (US $) | MCSEb, Can $ (US $) | QALYsc | MCSE | Incremental cost, Can $ (US $) | MCSE, Can $ (US $) | Incremental QALYs | MCSE | ICERd, Can $ (US $; $/QALY) | |
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Comparator | 81,714 (61,641) | 3417 (2577) | 6.89 | 0.13 | N/Ae | N/A | N/A | N/A | N/A |
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Medly | 88,016 (66395) | 3215 (2425) | 7.48 | 0.14 | 6302 (4753) | 1806 (1362) | 0.60 | 0.05 | 10,567 (7971) |
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Comparator | 88,405 (66,689) | 3821 (2882) | 5.65 | 0.12 | N/A | N/A | N/A | N/A | N/A |
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Medly | 94,335 (71,162) | 3521 (2656) | 6.35 | 0.13 | 5930 (4473) | 2014 (1519) | 0.70 | 0.06 | 8510 (6419) |
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Comparator | 104,421 | 4356 | 4.12 | 0.11 | N/A | N/A | N/A | N/A | N/A |
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Medly | 107,803 | 3929 | 4.69 | 0.11 | 3382 | 2134 | 0.57 | 0.05 | 5931 |
aNYHA: New York Health Association.
bMCSE: Monte Carlo standard error.
cQALY: quality-adjusted life year.
dICER: incremental cost-effectiveness ratio.
eN/A: not applicable.
The CEAC curves for NYHA functional classes I, II, and III are shown in
Cost-effectiveness acceptability curve of the New York Health Association functional class scenario analyses. CEAC: cost-effectiveness acceptability curve; NYHA: New York Health Association.
The CEAC curves for each deployment model are shown in
Deterministic results for each deployment model of Medly.
Deployment models | Costs (Can $) | MCSEa (Can $) | QALYsb | MCSE | Incremental cost (Can $) | MCSE (Can $) | Incremental QALYs | MCSE | ICERc (Can $/QALY) | |
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Comparator | 97,497 | 3948 | 4.95 | 0.12 | N/Ad | N/A | N/A | N/A | N/A |
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Medly | 99,393 | 3542 | 5.51 | 0.13 | 1896 | 2006 | 0.57 | 0.05 | 3349 |
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Comparator | 97,497 | 3948 | 4.947 | 0.12 | N/A | N/A | N/A | N/A | N/A |
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Medly | 100,769 | 3567 | 5.51 | 0.13 | 3273 | 2007 | 0.57 | 0.05 | 5780 |
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Comparator | 97,497 | 3948 | 4.947 | 0.12 | N/A | N/A | N/A | N/A | N/A |
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Medly | 106,194 | 3646 | 5.51 | 0.13 | 8697 | 2015 | 0.57 | 0.05 | 15,362 |
aMCSE: Monte Carlo standard error.
bQALY: quality-adjusted life year.
cICER: incremental cost-effectiveness ratio.
dN/A: not applicable.
Cost-effectiveness acceptability curves for each deployment model in the scenario analyses. BYOE: Bring Your Own Everything; CEAC: cost-effectiveness acceptability curve; FK: Full Kit.
When the RR for mortality was set to its lower range, the ICER increased to Can $18,556 (US $13,997.90)/QALY (
Deterministic results for the upper and lower limits of effectiveness in reducing mortality and hospitalization rates.
Effectiveness | Costs (Can $) | MCSEa (Can $) | QALYsb | MCSE | Incremental cost (Can $) | MCSE (Can $) | Incremental QALYs | MCSE | ICERc (Can $/QALY) | ||
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Comparator | 97,497 | 3948 | 4.95 | 0.12 | —e | — | — | — | — |
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Medly | 114,682 | 3995 | 5.87 | 0.13 | 17,186 | 2660 | 0.93 | 0.07 | 18,556 |
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Comparator | 97,497 | 3948 | 4.95 | 0.12 | — | — | — | — | — |
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Medly | 91,542 | 3410 | 5.14 | 0.12 | −5955 | 1339 | 0.19 | 0.03 | −30,806 |
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Comparator | 97,497 | 3948 | 4.95 | 0.12 | — | — | — | — | — |
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Medly | 92,107 | 3176 | 5.55 | 0.13 | −5390 | 2126 | 0.61 | 0.06 | −8895 |
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Comparator | 97,497 | 3948 | 4.95 | 0.12 | — | — | — | — | — |
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Medly | 113,763 | 4052 | 5.50 | 0.13 | 16,267 | 2104 | 0.56 | 0.028 | 29,240 |
aMCSE: Monte Carlo standard error.
bQALY: quality-adjusted life year.
cICER: incremental cost-effectiveness ratio.
dRR: relative risk.
eN/A: not applicable.
The purpose of this study was to assess the cost utility of the Medly program for patients with HF compared with the standard of care from a public payer perspective. The analysis showed that Medly had a high probability (90.1%) of being cost-effective at a WTP threshold of Can $50,000 (US $37,718)/QALY. The results also showed that cost-effectiveness improved in cohorts with more advanced HF. This is attributable to the higher health care utilization rates experienced in higher NYHA functional classes. Deployment models with larger proportions of patients bringing their own equipment to the
The significance of the study findings are 3-fold: (1) providing evidence for health care decision makers on the use of TM for HF, (2) supporting the use of a nurse-led model of TM using clinically validated algorithms within HF clinics, and (3) informing the use of economic modeling for future evaluation of early-stage health informatics technology.
Our study provided the
A key factor contributing to the cost-effectiveness of Medly was the high number of patients (500) that a single nurse could manage, which is possible owing to the clinically validated algorithms that generate automatic clinical alerts and self-care messages. Other studies have reported a concern regarding increased clinical workload associated with incompatibility of the TM program with existing workflows, including management of and responding to alerts [
Our study provides a case study on the use of multiple data sources and methods to develop a decision model for an early-stage health informatics intervention where knowledge gaps existed. As the purpose of this study was to evaluate the potential long-term effects of the
Other studies have investigated the cost-effectiveness of other types of TM in HF, where data are transmitted to medical staff. The study by Thokala et al (2013) [
As with any modeling exercise, it is important to understand the limitations around data availability and assumptions. First, owing to the lack of long-term studies, the trajectory of the effectiveness of TM was unknown and was assumed constant over the patient’s lifetime. It is not known if effectiveness changes over time, which may affect the results of this study. Another limitation was the assumption that patients used
The
Additional methodology supplementary material.
Bring Your Own Everything
Bring Your Own Phone
cost-effectiveness acceptability curve
cost-utility analysis
emergency department
Full Kit
general practitioner
heart failure
incremental cost-effectiveness ratio
New York Health Association
quality-adjusted life year
quality of life
relative risk
Seattle Heart Failure Model
telemonitoring
University Health Network
willingness-to-pay
HR and ES are considered inventors of the Medly system under the intellectual property policies of the UHN and may benefit from future commercialization of the technology by UHN.