Evidence map›Paper›PMID 34278383›Full record

ArticleArtificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- )2021

Detection of Junctional Ectopic Tachycardia by Central Venous Pressure.

Xin Tan, Yanwan Dai, Ahmed Imtiaz Humayun, Haoze Chen, Genevera I Allen, Parag N Jain

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- ), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

6 authors.

Xin TanDepartment of Statistics, Rice University, Houston, TX, USA.
Yanwan DaiDepartment of Statistics, Rice University, Houston, TX, USA.
Ahmed Imtiaz HumayunDepartment of Electrical and Computer Engineering, Rice University, Houston, TX, USA.
Haoze ChenDepartment of Electrical and Computer Engineering, Rice University, Houston, TX, USA.
Genevera I AllenDepartments of ECE, Statistics, and Computer Science, Rice University, TX, USA.
Parag N JainTexas Children's Hospital, Houston, TX, USA.

Funding

Graphical Models from Partially Observed Interactions with Biomedical ApplicationsR01GM140468 · NIGMS · RICE UNIVERSITY · PI ALLEN, GENEVERA IRENE · 2020 to 2022
$600k
NIGMS NIH HHS R01 GM140468
6 · The paper itself

Abstract

Central venous pressure (CVP) is the blood pressure in the venae cavae, near the right atrium of the heart. This signal waveform is commonly collected in clinical settings, and yet there has been limited discussion of using this data for detecting arrhythmia and other cardiac events. In this paper, we develop a signal processing and feature engineering pipeline for CVP waveform analysis. Through a case study on pediatric junctional ectopic tachycardia (JET), we show that our extracted CVP features reliably detect JET with comparable results to the more commonly used electrocardiogram (ECG) features. This machine learning pipeline can thus improve the clinical diagnosis and ICU monitoring of arrhythmia. It also corroborates and complements the ECG-based diagnosis, especially when the ECG measurements are unavailable or corrupted.

Indexed as

Automatic Arrythmia DetectionCentral Venous PressureJunctional Ectopic TachycardiaPhysiological Signal Feature Extraction

Identifiers

PMID34278383
PMCPMC8281976

What OpenQuestion holds

Textmetadata
LicenceCC BY-ND
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.