Evidence map›Paper›PMID 36202815›Full record

ArticleScientific reports2022

Intra-beat biomarker for accurate continuous non-invasive blood pressure monitoring.

Arash Abiri, En-Fan Chou, Chengyang Qian, Joseph Rinehart, Michelle Khine

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
0.5field-weighted citation impact, top 42% 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

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 7 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. 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

5 authors at 2 institutions in 1 country.

Arash AbiriDepartment of Biomedical Engineering, University of California Irvine, Irvine, CA, 92697, USA.
En-Fan ChouDepartment of Biomedical Engineering, University of California Irvine, Irvine, CA, 92697, USA.
Chengyang QianDepartment of Biomedical Engineering, University of California Irvine, Irvine, CA, 92697, USA.
Joseph RinehartDepartment of Anesthesiology & Perioperative Care, University of California, Irvine Medical Center, Orange, CA, USA.
Michelle KhineDepartment of Biomedical Engineering, University of California Irvine, Irvine, CA, 92697, USA. mkhine@uci.edu.
University of California, Irvine · USUniversity of California, Irvine Medical Center · US

Funding

MEDICAL SCIENTIST TRAINING PROGRAMT32GM008620 · NIGMS · UNIVERSITY OF CALIFORNIA-IRVINE · PI GOLDIN, ALAN L · 1999 to 2023
$8.1M
Alzheimer's Association 2019-AARGD-NTF-644466NIGMS NIH HHS T32 GM008620NIH HHS T32GM008620
6 · The paper itself

Abstract

Accurate continuous non-invasive blood pressure (CNIBP) monitoring is the holy grail of digital medicine but remains elusive largely due to significant drifts in signal and motion artifacts that necessitate frequent device recalibration. To address these challenges, we developed a unique approach by creating a novel intra-beat biomarker (Diastolic Transit Time, DTT) to achieve highly accurate blood pressure (BP) estimations. We demonstrated our approach's superior performance, compared to other common signal processing techniques, in eliminating stochastic baseline wander, while maintaining signal integrity and measurement accuracy, even during significant hemodynamic changes. We applied this new algorithm to BP data collected using non-invasive sensors from a diverse cohort of high acuity patients and demonstrated that we could achieve close agreement with the gold standard invasive arterial line BP measurements, for up to 20 min without recalibration. We established our approach's generalizability by successfully applying it to pulse waveforms obtained from various sensors, including photoplethysmography and capacitive-based pressure sensors. Our algorithm also maintained signal integrity, enabling reliable assessments of BP variability. Moreover, our algorithm demonstrated tolerance to both low- and high-frequency motion artifacts during abrupt hand movements and prolonged periods of walking. Thus, our approach shows promise in constituting a necessary advance and can be applied to a wide range of wearable sensors for CNIBP monitoring in the ambulatory and inpatient settings.

Indexed as

Blood Pressure DeterminationPhotoplethysmographyBiomarkersBlood PressureHeart RateHumansBiomarkers

Identifiers

PMID36202815
PMCPMC9537243
OpenAlexW4302424697

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.