Evidence map›Paper›PMID 34206457›Full record

ReviewSensors (Basel, Switzerland)2021

Advances in Non-Invasive Blood Pressure Monitoring.

Xina Quan, Junjun Liu, Thomas Roxlo, Siddharth Siddharth, Weyland Leong, Arthur Muir, So-Min Cheong, Anoop Rao

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed, 1 pooled it
–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

29 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Guideline
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  10. Article
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  15. Article
  16. Evolving Management Practices for Early Sepsis-induced Hypoperfusion: A Narrative Review.American journal of respiratory and critical care medicine · 2023
    Review
  17. Article
  18. Review
  19. Artificial Intelligence in Hypertension Management: An Ace up Your Sleeve.Journal of cardiovascular development and disease · 2023
    Review
  20. 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

8 authors.

Xina QuanPyrAmes Inc., Cupertino, CA 95014, USA.
Junjun LiuPyrAmes Inc., Cupertino, CA 95014, USA.
Thomas RoxloPyrAmes Inc., Cupertino, CA 95014, USA.
Siddharth SiddharthPyrAmes Inc., Cupertino, CA 95014, USA.
Weyland LeongPyrAmes Inc., Cupertino, CA 95014, USA.
Arthur MuirPyrAmes Inc., Cupertino, CA 95014, USA.
So-Min CheongDepartment of Geography & Atmospheric Science, University of Kansas, Lawrence, KS 66045, USA.
Anoop RaoDepartment of Pediatrics, Neonatology, Stanford University, Palo Alto, CA 94304, USA.

Funding

Continuous Non-Invasive Blood Pressure Monitor for NeonatesR43HD101175 · NICHD · PYRAMES, INC. · PI QUAN, XINA · 2020 to 2020
$225k
Eunice Kennedy Shriver National Institute of Child Health and Human Development 1R43HD101175-01NICHD NIH HHS R43 HD101175
6 · The paper itself

Abstract

This paper reviews recent advances in non-invasive blood pressure monitoring and highlights the added value of a novel algorithm-based blood pressure sensor which uses machine-learning techniques to extract blood pressure values from the shape of the pulse waveform. We report results from preliminary studies on a range of patient populations and discuss the accuracy and limitations of this capacitive-based technology and its potential application in hospitals and communities.

Indexed as

AlgorithmsBlood Pressure DeterminationBlood PressureHeart RateHumansMachine LearningcNIBPhypertensionhypotensionneonateNICUnon-invasive blood pressure monitoring

Identifiers

PMID34206457
PMCPMC8271585

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.