Evidence map›Paper›PMID 31935231›Full record

ArticlePloS one2020

A smartphone-enabled wireless and batteryless implantable blood flow sensor for remote monitoring of prosthetic heart valve function.

Bernhard Vennemann, Dominik Obrist, Thomas Rösgen

Abstract read
In one paragraph

Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. NFC-Enabled Dual-Channel Flexible Printed Sensor Tag.Sensors (Basel, Switzerland) · 2023
    Article
  5. Article
  6. Article
  7. Review
  8. Near-Field Communication in Biomedical Applications.Sensors (Basel, Switzerland) · 2021
    Review
  9. 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

3 authors.

Bernhard VennemannInstitute of Fluid Dynamics, ETH Zürich, Zürich, Switzerland.ORCID 0000-0003-4696-7666
Dominik ObristARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.ORCID 0000-0002-6062-9076
Thomas RösgenInstitute of Fluid Dynamics, ETH Zürich, Zürich, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aortic valve disease is one of the leading forms of complications in the cardiovascular system. The failing native aortic valve is routinely surgically replaced with a bioprosthesis. However, insufficient durability of bioprosthetic heart valves often requires reintervention. Valve degradation can be assessed by an analysis of the blood flow characteristics downstream of the valve. This is cost and labor intensive using clinical methodologies and is performed infrequently. The integration of consumer smartphones and implantable blood flow sensors into the data acquisition chain facilitates remote management of patients that is not limited by access to clinical facilities. This article describes the characteristics of an implantable magnetic blood flow sensor which was optimized for small size and low power consumption to allow for batteryless operation. The data is wirelessly transmitted to the patient's smartphone for in-depth processing. Tests using three different experimental setups confirmed that wireless and batteryless blood flow recording using a magnetic flow meter technique is feasible and that the sensor system is capable of monitoring the characteristic flow downstream of the valve.

Indexed as

Heart Valve ProsthesisHemodynamicsAortic ValveAortic Valve StenosisBicuspid Aortic Valve DiseaseBioprosthesisHeart Defects, CongenitalHeart Valve DiseasesHumansProsthesis DesignProsthesis FailureSmartphone

Identifiers

PMID31935231
PMCPMC6959614

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.