Evidence map›Paper›PMID 38733504›Full record

ArticleJournal of clinical monitoring and computing2024

A non-invasive method to monitor respiratory muscle effort during mechanical ventilation.

Guillermo Gutierrez

Registry-linked trialAbstract read
PubMed Publisher
In one paragraph

Article in Journal of clinical monitoring and computing, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07205952 (Automatic Detection of Dyspnea in Mechanically Ventilated, Critically Ill Patients), which is not on this 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.

NCT07205952 recruitingnot on this map

Automatic Detection of Dyspnea in Mechanically Ventilated, Critically Ill Patients

TypeobservationalSponsorHospital de Clinicas José de San MartínRan2024 to 2025Enrolled60ConditionsDyspnea During Mechanical Ventilation
3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Observational
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

1 author.

Guillermo GutierrezProfessor Emeritus Medicine, Anesthesiology and Engineering, The George Washington University, 700 New Hampshire Ave, NW Suite 510, Washington, DC, 20037, USA. gutier@gwu.edu.ORCID 0000-0001-7754-5816

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThis study introduces a method to non-invasively and automatically quantify respiratory muscle effort (P

methodsThe study utilized data from 250 patients on invasive MV. The data included detailed recordings of P

resultsThere was a strong correlation of P

conclusionThe study supports the feasibility of assessing respiratory effort during MV non-invasively through airway signal analysis. Further research is warranted to validate this method and investigate its clinical applications.

Indexed as

AlgorithmsRespiration, ArtificialRespiratory MusclesTidal VolumeAdultAgedFemaleHumansMaleMiddle AgedMonitoring, PhysiologicPressureRespirationRespiratory MechanicsSignal Processing, Computer-AssistedAcute respiratory failureAirway resistanceDyspneaMechanical ventilationNumerical analysisRespiratory effortsStatic compliance

Identifiers

PMID38733504

What OpenQuestion holds

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Registered trials

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.