Evidence map›Paper›PMID 31683938›Full record

ReviewJournal of clinical medicine2019

Multi-Site Photoplethysmography Technology for Blood Pressure Assessment: Challenges and Recommendations.

Gabriel Chan, Rachel Cooper, Manish Hosanee, Kaylie Welykholowa, Panayiotis A Kyriacou, Dingchang Zheng, John Allen, Derek Abbott, Nigel H Lovell, Richard Fletcher and 1 more

Open access · goldAbstract readReview
In one paragraph

Review in Journal of clinical medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed, 1 pooled it
4.7field-weighted citation impact, top 4% 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

33 citing papers in PubMed, 1 synthesis or guideline pooled it, 85 citations in OpenAlex.

  1. Pooled it
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  18. PPGFeat: a novel MATLAB toolbox for extracting PPG fiducial points.Frontiers in bioengineering and biotechnology · 2023
    Article
  19. Review
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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

11 authors at 7 institutions in 4 countries.

Gabriel ChanFaculty of Medicine, University of British Columbia, Vancouver, BC V6T 1Z3, Canada. gabriel.chan@alumni.ubc.ca.
Rachel CooperFaculty of Medicine, University of British Columbia, Vancouver, BC V6T 1Z3, Canada. rachelcooper@alumni.ubc.ca.
Manish HosaneeFaculty of Medicine, University of British Columbia, Vancouver, BC V6T 1Z3, Canada. manish.hosanee@alumni.ubc.ca.
Kaylie WelykholowaFaculty of Medicine, University of British Columbia, Vancouver, BC V6T 1Z3, Canada. kayliewelykholowa@gmail.com.
Panayiotis A KyriacouSchool of Mathematics, Computer Science and Engineering, University of London, London, EC1V 0HB, UK. p.kyriacou@city.ac.uk.
Dingchang ZhengResearch Center of Intelligent Healthcare, Faculty of Health and Life Science, Coventry University, Coventry CV1 5FB, UK. dingchang.zheng@coventry.ac.uk.
John AllenMicrovascular Diagnostics, Northern Medical Physics and Clinical Engineering, Freeman Hospital, Newcastle Upon Tyne NE7 7DN, UK. john.allen@ncl.ac.uk.
Derek AbbottSchool of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, SA 5005, Australia. derek.abbott@adelaide.edu.au.
Nigel H LovellGraduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW 2052, Australia. n.lovell@unsw.edu.au.
Richard FletcherD-Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. fletcher@media.mit.edu.
Mohamed ElgendiFaculty of Medicine, University of British Columbia, Vancouver, BC V6T 1Z3, Canada. melgendi@bcchr.ubc.ca.
University of British Columbia · CAFreeman Hospital · GBIntelligent Health (United Kingdom) · GBThe University of Adelaide · AUUniversity of London · GBUniversity of Massachusetts Chan Medical School · USUNSW Sydney · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hypertension is one of the most prevalent diseases and is often called the "silent killer" because there are usually no early symptoms. Hypertension is also associated with multiple morbidities, including chronic kidney disease and cardiovascular disease. Early detection and intervention are therefore important. The current routine method for diagnosing hypertension is done using a sphygmomanometer, which can only provide intermittent blood pressure readings and can be confounded by various factors, such as white coat hypertension, time of day, exercise, or stress. Consequently, there is an increasing need for a non-invasive, cuff-less, and continuous blood pressure monitoring device. Multi-site photoplethysmography (PPG) is a promising new technology that can measure a range of features of the pulse, including the pulse transit time of the arterial pulse wave, which can be used to continuously estimate arterial blood pressure. This is achieved by detecting the pulse wave at one body site location and measuring the time it takes for it to reach a second, distal location. The purpose of this review is to analyze the current research in multi-site PPG for blood pressure assessment and provide recommendations to guide future research. In a systematic search of the literature from January 2010 to January 2019, we found 13 papers that proposed novel methods using various two-channel PPG systems and signal processing techniques to acquire blood pressure using multi-site PPG that offered promising results. However, we also found a general lack of validation in terms of sample size and diversity of populations.

Indexed as

anesthesiadigital healthglobal healthhypertension assessmentintensive care unitphotoplethysmographypulse arrival timepulse oximeterpulse transit timepulse wavewearable devices

Identifiers

PMID31683938
PMCPMC6912608
OpenAlexW2983580362

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.