Evidence map›Paper›PMID 34362950›Full record

ArticleScientific reports2021

Cardiopulmonary coupling indices to assess weaning readiness from mechanical ventilation.

Pablo Armañac-Julián, David Hernando, Jesús Lázaro, Candelaria de Haro, Rudys Magrans, John Morales, Jonathan Moeyersons, Leonardo Sarlabous, Josefina López-Aguilar, Carles Subirà and 7 more

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
2.4field-weighted citation impact, top 10% 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

7 citing papers in PubMed, 21 citations in OpenAlex.

  1. Observational
  2. Article
  3. 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

17 authors at 10 institutions in 4 countries.

Pablo Armañac-JuliánBiomedical Signal Interpretation and Computational Simulation (BSICoS) group at the Aragón Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, Zaragoza, Spain. parmanac@unizar.es.
David HernandoBiomedical Signal Interpretation and Computational Simulation (BSICoS) group at the Aragón Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, Zaragoza, Spain.
Jesús LázaroBiomedical Signal Interpretation and Computational Simulation (BSICoS) group at the Aragón Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, Zaragoza, Spain.
Candelaria de HaroCritical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació Parc Taulí I3PT, Universitat Autónoma de Barcelona, Sabadell, Spain.
Rudys MagransBetter Care, Barcelona, Spain.
John MoralesDepartment of Electrical Engineering-ESAT, STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium.
Jonathan MoeyersonsDepartment of Electrical Engineering-ESAT, STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium.
Leonardo SarlabousCritical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació Parc Taulí I3PT, Universitat Autónoma de Barcelona, Sabadell, Spain.
Josefina López-AguilarCritical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació Parc Taulí I3PT, Universitat Autónoma de Barcelona, Sabadell, Spain.
Carles SubiràDepartment of Intensive Care, Fundació Althaia, Universitat Internacional de Catalunya, Manresa, Spain.
Rafael FernándezCIBER de Enfermedades Respiratorias (CIBER-ES), Instituto de Salud Carlos III, Madrid, Spain.
Michele OriniInstitute of Cardiovascular Science, University College London, London, UK.
Pablo LagunaBiomedical Signal Interpretation and Computational Simulation (BSICoS) group at the Aragón Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, Zaragoza, Spain.
Carolina VaronDepartment of Electrical Engineering-ESAT, STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium.
Eduardo GilBiomedical Signal Interpretation and Computational Simulation (BSICoS) group at the Aragón Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, Zaragoza, Spain.
Raquel BailónBiomedical Signal Interpretation and Computational Simulation (BSICoS) group at the Aragón Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, Zaragoza, Spain.
Lluís BlanchCritical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació Parc Taulí I3PT, Universitat Autónoma de Barcelona, Sabadell, Spain.
Instituto de Salud Carlos III · ESKU Leuven · BEBiomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine · ESInstitute of Research and Innovation Parc Tauli · ESAlthaia · ESCentro de Investigación Biomédica en Red de Enfermedades Respiratorias · ESInstituto de Investigación Sanitaria Aragón · ESUniversidad de Zaragoza · ESUniversitat Autònoma de Barcelona · ESUniversity College London · GB

Funding

Low Temperature AFM/Freeze Fracture &EtchR01EB002017 · NIBIB · UNIVERSITY OF VIRGINIA CHARLOTTESVILLE · PI SHAO, ZHIFENG · 2003 to 2003
$259k
6 · The paper itself

Abstract

The ideal moment to withdraw respiratory supply of patients under Mechanical Ventilation at Intensive Care Units (ICU), is not easy to be determined for clinicians. Although the Spontaneous Breathing Trial (SBT) provides a measure of the patients' readiness, there is still around 15-20% of predictive failure rate. This work is a proof of concept focused on adding new value to the prediction of the weaning outcome. Heart Rate Variability (HRV) and Cardiopulmonary Coupling (CPC) methods are evaluated as new complementary estimates to assess weaning readiness. The CPC is related to how the mechanisms regulating respiration and cardiac pumping are working simultaneously, and it is defined from HRV in combination with respiratory information. Three different techniques are used to estimate the CPC, including Time-Frequency Coherence, Dynamic Mutual Information and Orthogonal Subspace Projections. The cohort study includes 22 patients in pressure support ventilation, ready to undergo the SBT, analysed in the 24 h previous to the SBT. Of these, 13 had a successful weaning and 9 failed the SBT or needed reintubation -being both considered as failed weaning. Results illustrate that traditional variables such as heart rate, respiratory frequency, and the parameters derived from HRV do not differ in patients with successful or failed weaning. Results revealed that HRV parameters can vary considerably depending on the time at which they are measured. This fact could be attributed to circadian rhythms, having a strong influence on HRV values. On the contrary, significant statistical differences are found in the proposed CPC parameters when comparing the values of the two groups, and throughout the whole recordings. In addition, differences are greater at night, probably because patients with failed weaning might be experiencing more respiratory episodes, e.g. apneas during the night, which is directly related to a reduced respiratory sinus arrhythmia. Therefore, results suggest that the traditional measures could be used in combination with the proposed CPC biomarkers to improve weaning readiness.

Indexed as

Heart RateRespirationAgedFemaleHumansIntensive Care UnitsMaleMiddle AgedProspective StudiesRespiration, ArtificialVentilator Weaning

Identifiers

PMID34362950
PMCPMC8346488
OpenAlexW3192134680

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Registered trials

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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.