Evidence map›Paper›PMID 29270437›Full record

ArticleBioMed research international2017

Description and Validation of TAVIApp: A Novel Mobile Application for Support of Physicians in the Management of Aortic Stenosis-Management of Aortic Stenosis with TAVIApp.

Ciro Indolfi, Jolanda Sabatino, Salvatore De Rosa, Annalisa Mongiardo, Pietrantonio Ricci, Carmen Spaccarotella

Abstract read
In one paragraph

Article in BioMed research international, 2017. 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. [eHealth-smart devices revolutionizing cardiology].Herzschrittmachertherapie & Elektrophysiologie · 2020
    Review
  3. Article
  4. Article
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

6 authors.

Ciro IndolfiDivision of Cardiology, Department of Medical and Surgical Sciences, Magna Græcia University, Catanzaro, Italy.ORCID 0000-0003-1241-6487
Jolanda SabatinoDivision of Cardiology, Department of Medical and Surgical Sciences, Magna Græcia University, Catanzaro, Italy.
Salvatore De RosaDivision of Cardiology, Department of Medical and Surgical Sciences, Magna Græcia University, Catanzaro, Italy.ORCID 0000-0001-5388-942X
Annalisa MongiardoDivision of Cardiology, Department of Medical and Surgical Sciences, Magna Græcia University, Catanzaro, Italy.
Pietrantonio RicciInstitute of Forensic Medicine, Department of Medical and Surgical Sciences, Magna Græcia University, Catanzaro, Italy.
Carmen SpaccarotellaDivision of Cardiology, Department of Medical and Surgical Sciences, Magna Græcia University, Catanzaro, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAortic stenosis (AS) is the most common heart valve disease in developed countries. The advent of transcatheter aortic valve implantation (TAVI) significantly improved patients' outcome but made clinical management more complex. The aim of the present study was to describe TAVIApp, a mobile app we developed to guide the management of AS, and test its efficacy. METHODS AND

resultsClinical cases comprising 42 patients with AS were blindly evaluated by (A) an interventional cardiologist, assisted by the Heart Team (EXPERT), (B) young residents in cardiology, and (C) a young resident supported by TAVIApp. There was poor concordance between Group A and Group B with low performance by young residents (

conclusionsThis study is the first describing and validating a new mobile application to support the management of AS. TAVIApp supports cardiologists in the evaluation of stenosis severity, eligibility for TAVI or AVR, and selection of the most appropriate prosthesis size in individual patients.

Indexed as

Disease ManagementMobile ApplicationsAgedAortic ValveAortic Valve StenosisCardiologistsFemaleHumansInternship and ResidencyMaleRisk AssessmentRisk FactorsSeverity of Illness IndexTreatment OutcomeWorkforce

Identifiers

PMID29270437
PMCPMC5705902

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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.