Evidence map›Paper›PMID 34534188›Full record

ArticleMedical care2021

Cost-effectiveness of a Digital Health Intervention for Acute Myocardial Infarction Recovery.

Vinayak Bhardwaj, Erin M Spaulding, Francoise A Marvel, Sarah LaFave, Jeffrey Yu, Daniel Mota, Ting-Jia Lorigiano, Pauline P Huynh, Rongzi Shan, Pooja S Yesantharao and 21 more

Open access · hybridAbstract read
In one paragraph

Article in Medical care, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
3.1field-weighted citation impact, top 7% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 28 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

31 authors at 6 institutions in 1 country.

Vinayak BhardwajJohns Hopkins Bloomberg School of Public Health.
Erin M SpauldingJohns Hopkins Bloomberg School of Public Health.
Francoise A MarvelDigital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine.
Sarah LaFaveJohns Hopkins University School of Nursing.
Jeffrey YuJohns Hopkins Bloomberg School of Public Health.
Daniel MotaJohns Hopkins Bloomberg School of Public Health.
Ting-Jia LorigianoJohns Hopkins University School of Medicine, Baltimore, MD.
Pauline P HuynhDigital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine.
Rongzi ShanDigital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine.
Pooja S YesantharaoDigital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine.
Matthias A LeeJohns Hopkins University Whiting School of Engineering, Baltimore, MD.
William E YangDigital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine.
Ryan DemoJohns Hopkins University Whiting School of Engineering, Baltimore, MD.
Jie DingJohns Hopkins Bloomberg School of Public Health.
Jane WangDigital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine.
Helen XunDigital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine.
Lochan ShahDigital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine.
Daniel WengDigital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine.
Shannon WongvibulsinDigital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine.
Jocelyn CarterMassachusetts General Hospital, Boston, MA.
Julie SheidyReading Hospital, West Reading, PA.
Renee McLinReading Hospital, West Reading, PA.
Jennifer FlowersReading Hospital, West Reading, PA.
Maulik MajmudarMassachusetts General Hospital, Boston, MA.
Eric ElginReading Hospital, West Reading, PA.
Valerie VilarinoDigital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine.
David LumelskyDigital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine.
Curtis LeungJohns Hopkins Health System, Baltimore, MD.
Jerilyn K AllenJohns Hopkins Bloomberg School of Public Health.
Seth S MartinJohns Hopkins Bloomberg School of Public Health.
William V PadulaDepartment of Pharmaceutical & Health Economics, School of Pharmacy, University of Southern California.
Johns Hopkins University · USJohns Hopkins Medicine · USReading Hospital · USHarvard University · USMassachusetts General Hospital · USUniversity of Southern California · US

Funding

MEDICAL SCIENTIST TRAINING PROGRAMT32GM007309 · NIGMS · JOHNS HOPKINS UNIVERSITY · PI COX, ANDREA L · 1985 to 2019
$43.2M
Sex Hormones in Pulmonary Arterial HypertensionP01HL108800 · NHLBI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI HAMID, RIZWAN · 2012 to 2021
$22.0M
CARDIOVASCULAR EPIDEMIOLOGY INSTITUTIONAL TRAININGT32HL007024 · NHLBI · JOHNS HOPKINS UNIVERSITY · PI Elizabeth Selvin · 1985 to 2026
$17.5M
Medical Scientist Training ProgramT32GM136577 · NIGMS · JOHNS HOPKINS UNIVERSITY · PI ANDREA L COX · 2020 to 2026
$13.4M
Mentored Career DevelopmentKL2TR001854 · NCATS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Elizabeth Rhea Erwin Burner, Amy Elizabeth West · 2016 to 2026
$9.0M
Interdisciplinary Training in Cardiovascular Health ResearchT32NR012704 · NINR · JOHNS HOPKINS UNIVERSITY · PI ALLEN, JERILYN K · 2011 to 2020
$1.8M
Implementing a digitally-enabled community health worker intervention for patients with heart failureK23HL150287 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI CARTER, JOCELYN ALEXANDRIA · 2020 to 2024
$978k
Sudden Cardiac Arrest (SCA): Prediction and PreventionF30HL142131 · NHLBI · JOHNS HOPKINS UNIVERSITY · PI WONGVIBULSIN, SHANNON · 2018 to 2020
$135k
The Process of User Engagement with mHealthF31NR017328 · NINR · JOHNS HOPKINS UNIVERSITY · PI SPAULDING, ERIN MURDOCK · 2017 to 2019
$129k
NCATS NIH HHS KL2 TR001854NHLBI NIH HHS F30 HL142131NHLBI NIH HHS K23 HL150287NHLBI NIH HHS P01 HL108800NHLBI NIH HHS T32 HL007024NIGMS NIH HHS T32 GM007309NIGMS NIH HHS T32 GM136577NINR NIH HHS F31 NR017328NINR NIH HHS T32 NR012704
6 · The paper itself

Abstract

backgroundAcute myocardial infarction (AMI) is a common cause of hospital admissions, readmissions, and mortality worldwide. Digital health interventions (DHIs) that promote self-management, adherence to guideline-directed therapy, and cardiovascular risk reduction may improve health outcomes in this population. The "Corrie" DHI consists of a smartphone application, smartwatch, and wireless blood pressure monitor to support medication tracking, education, vital signs monitoring, and care coordination. We aimed to assess the cost-effectiveness of this DHI plus standard of care in reducing 30-day readmissions among AMI patients in comparison to standard of care alone.

methodsA Markov model was used to explore cost-effectiveness from the hospital perspective. The time horizon of the analysis was 1 year, with 30-day cycles, using inflation-adjusted cost data with no discount rate. Currencies were quantified in US dollars, and effectiveness was measured in quality-adjusted life-years (QALYs). The results were interpreted as an incremental cost-effectiveness ratio at a threshold of $100,000 per QALY. Univariate sensitivity and multivariate probabilistic sensitivity analyses tested model uncertainty.

resultsThe DHI reduced costs and increased QALYs on average, dominating standard of care in 99.7% of simulations in the probabilistic analysis. Based on the assumption that the DHI costs $2750 per patient, use of the DHI leads to a cost-savings of $7274 per patient compared with standard of care alone.

conclusionsOur results demonstrate that this DHI is cost-saving through the reduction of risk for all-cause readmission following AMI. DHIs that promote improved adherence with guideline-based health care can reduce hospital readmissions and associated costs.

Indexed as

Quality-Adjusted Life YearsAcute DiseaseCost-Benefit AnalysisHumansMarkov ChainsMyocardial InfarctionTelemedicine

Identifiers

PMID34534188
PMCPMC8516712
OpenAlexW3199159516

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.