Evidence map›Paper›PMID 31788552›Full record

Article... IEEE Signal Processing in Medicine and Biology Symposium (SPMB). IEEE Signal Processing in Medicine and Biology Symposium2018

Stenosis Characterization and Identification for Dialysis Vascular Access.

S Chin, B Panda, M S Damaser, S J A Majerus

Abstract read
In one paragraph

Article in ... IEEE Signal Processing in Medicine and Biology Symposium (SPMB). IEEE Signal Processing in Medicine and Biology Symposium, 2018. 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
1.4field-weighted citation impact, top 17% 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

4 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. Article
  3. Flexible, Skin Coupled Microphone Array for Point of Care Vascular Access Monitoring.IEEE transactions on biomedical circuits and systems · 2019
    Article
  4. VASCULAR STENOSIS DETECTION USING TEMPORAL-SPECTRAL DIFFERENCES IN CORRELATED ACOUSTIC MEASUREMENTS.... IEEE Signal Processing in Medicine and Biology Symposium (SPMB). IEEE Signal Processing in Medicine and Biology Symposium · 2019
    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

4 authors at 3 institutions in 1 country.

S ChinDept. of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA.
B PandaDept. of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA.
M S DamaserAdvanced Platform Technology Center, Louis Stokes Cleveland VA Medical Center, Cleveland, Ohio, USA.
S J A MajerusAdvanced Platform Technology Center, Louis Stokes Cleveland VA Medical Center, Cleveland, Ohio, USA.
Case Western Reserve University · USCleveland Clinic Lerner College of Medicine · USLouis Stokes Cleveland VA Medical Center · US

Funding

Real-Time Monitoring Device for Vascular SignalsIK1RX001968 · VA · LOUIS STOKES CLEVELAND VA MEDICAL CENTER · PI MAJERUS, STEVE · 2016 to 2017
–
RRD VA IK1 RX001968
6 · The paper itself

Abstract

Vascular access dysfunction is the leading cause of hospitalization for hemodialysis patients and accounts for the most medical costs in this patient population. Vascular access flow is commonly hindered by blood vessel narrowing (stenosis). Current screening methods involving imaging to detect stenosis are too costly for routine use at the point of care. Noninvasive, real-time screening of patients at risk of vascular access dysfunction could potentially identify high-risk patients and reduce the likelihood of emergency surgical interventions. Bruits (sounds produced by turbulent blood flow near stenoses) can be interpreted by skilled clinical staff using conventional stethoscopes. To improve the sensitivity of detection, digital analysis of blood flow sounds (phonoangiograms or PAGs) is a promising approach for classifying vascular access stenosis using non-invasive auditory recordings. Here, we demonstrate auditory and spectral features of PAGs which estimate both the location and degree of stenosis (DOS). Auditory recordings from nine stenosis phantoms with variable DOS and hemodynamic flow rate were obtained using a digital recording stethoscope and analyzed to extract classification features. Autoregressive modeling and discrete wavelet transforms were used for multiresolution signal decomposition to produce 14 distinct features, most of which were linearly correlated with DOS. Our initial results suggest that the widely-used auditory spectral centroid is a simple way to calculate features which can estimate both the location and severity of vascular access stenosis.

Identifiers

PMID31788552
PMCPMC6885304
OpenAlexW2911133460

What OpenQuestion holds

Textmetadata
LicenceTDM
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.