Evidence map›Paper›PMID 37983113›Full record

ArticleThe Journal of invasive cardiology2023

Improving patient outcomes with a mobile digital health platform for patients undergoing transcatheter aortic valve replacement.

Vishal Venkatraman, Shivanand P Lad, Ziad F Gellad, Helen Heo, Kevin A Wu, Rajeev Dharmapurikar, Beiyu Liu, Sin-Ho Jung, Ryan Plichta, J Kevin Harrison and 2 more

Abstract read
In one paragraph

Article in The Journal of invasive cardiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
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

12 authors.

Vishal VenkatramanDepartment of Neurosurgery, Duke University Medical Center, Durham, NC, USA.
Shivanand P LadDepartment of Neurosurgery, Duke University Medical Center, Durham, NC, USA.
Ziad F GelladHiggs Boson Health, Durham, NC, USA.
Helen HeoDepartment of Neurosurgery, Duke University Medical Center, Durham, NC, USA.
Kevin A WuDepartment of Neurosurgery, Duke University Medical Center, Durham, NC, USA.
Rajeev DharmapurikarHiggs Boson Health, Durham, NC, USA.
Beiyu LiuDepartment of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA.
Sin-Ho JungDepartment of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA.
Ryan PlichtaDivision of Cardiovascular and Thoracic Surgery, Department of Surgery, Duke University Medical Center, Durham, NC, USA.
J Kevin HarrisonDepartment of Medicine, Duke University Medical Center, Durham, NC, USA.
Tom C NguyenDivision of Adult Cardiothoracic Surgery, Department of Surgery, UC San Francisco, San Francisco, CA, USA.
Julie DoberneDivision of Cardiovascular and Thoracic Surgery, Department of Surgery, Duke University Medical Center, Durham, NC, USA. Email: julie.doberne@duke.edu.

Funding

MEDICAL SCIENTIST TRAINING PROGRAMT32GM007171 · NIGMS · DUKE UNIVERSITY · PI KONTOS, CHRISTOPHER D · 1985 to 2021
$31.2M
NRSA Training CoreTL1TR002555 · NCATS · DUKE UNIVERSITY · PI EDELMAN, DAVID · 2018 to 2022
$3.7M
NCATS NIH HHS TL1 TR002555NIGMS NIH HHS T32 GM007171
6 · The paper itself

Abstract

purposeManageMySurgery (MMS) is a digital health application (app) for patients undergoing surgery, including Transcatheter Aortic Valve Replacement (TAVR). Patients using MMS review procedure-specific education, view FAQs, and report patient-reported outcomes. This study assessed the impact of app use on postoperative outcomes.

methodsPatients who underwent TAVR and invited to use MMS between March 2019 and November 2021 were identified. Patients received standard perioperative care and were defined as App users if they signed into the app at least once and engaged with at least one task or FAQ. Demographics and postoperative outcomes were collected via medical record review. Multivariable logistic regression models were used to determine odds of 90-day readmission, Emergency Room (ER) visits, and complications.

results388 patients met inclusion criteria, of which 238 used the app. The average age at surgery was 76.4±7.7 years for users and 78.1±7.6 for non-users. 63.0% of users and 59.3% of non-users were male. App users had significantly lower 90-day readmission rates, (8.8% vs 16.0%, OR=0.51, p=0.0373), ER visit rates (12.6% vs 27.3%, OR=0.36, p=0.0003), and complication rates (Minor: 12.2% vs 20.7%, OR=0.48, P=0.0126; Major: 8.8% vs. 16%, OR=0.47, P=0.0235).

conclusionsIn this non-randomized, retrospective study, we found significant decreases in 90-day readmissions, ER visits, and complications in TAVR patients using an app compared to traditional care. By engaging patients throughout their interventional journey with structured education and tasks, mobile health platforms may mitigate unnecessary use of emergency and inpatient care, thereby improving patient well-being and lowering the burden on healthcare resources.

Indexed as

TelemedicineTranscatheter Aortic Valve ReplacementFemaleHospitalizationHumansLogistic ModelsMaleRetrospective Studiesdigital heathmobile applicationpatient outcomesTAVR

Identifiers

PMID37983113
PMCPMC11225031

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Registered trials

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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.