It appears you don't have support to open PDFs in this web browser. To view this file, Open with your PDF reader
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.
You have requested "on-the-fly" machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Show full disclaimer
Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer
Details
1 University of Zaragoza, Biomedical Signal Interpretation and Computational Simulation (BSICoS) group at the Aragón Institute of Engineering Research (I3A), IIS Aragón, Zaragoza, Spain (GRID:grid.11205.37) (ISNI:0000 0001 2152 8769); Instituto de Salud Carlos III, CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain (GRID:grid.413448.e) (ISNI:0000 0000 9314 1427)
2 Universitat Autónoma de Barcelona, Critical Care Center, Hospital Universitari Parc Taulí, Institut d’Investigació Parc Taulí I3PT, Sabadell, Spain (GRID:grid.7080.f); Instituto de Salud Carlos III, CIBER de Enfermedades Respiratorias (CIBER-ES), Madrid, Spain (GRID:grid.413448.e) (ISNI:0000 0000 9314 1427)
3 Better Care, Barcelona, Spain (GRID:grid.413448.e)
4 KU Leuven, Department of Electrical Engineering-ESAT, STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Leuven, Belgium (GRID:grid.5596.f) (ISNI:0000 0001 0668 7884)
5 Universitat Autónoma de Barcelona, Critical Care Center, Hospital Universitari Parc Taulí, Institut d’Investigació Parc Taulí I3PT, Sabadell, Spain (GRID:grid.7080.f)
6 Universitat Internacional de Catalunya, Department of Intensive Care, Fundació Althaia, Manresa, Spain (GRID:grid.410675.1) (ISNI:0000 0001 2325 3084)
7 Instituto de Salud Carlos III, CIBER de Enfermedades Respiratorias (CIBER-ES), Madrid, Spain (GRID:grid.413448.e) (ISNI:0000 0000 9314 1427); Universitat Internacional de Catalunya, Department of Intensive Care, Fundació Althaia, Manresa, Spain (GRID:grid.410675.1) (ISNI:0000 0001 2325 3084)
8 University College London, Institute of Cardiovascular Science, London, UK (GRID:grid.83440.3b) (ISNI:0000000121901201); University College London, Barts Heart Centre, St Bartholomews Hospital, London, UK (GRID:grid.83440.3b) (ISNI:0000000121901201)
9 KU Leuven, Department of Electrical Engineering-ESAT, STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Leuven, Belgium (GRID:grid.5596.f) (ISNI:0000 0001 0668 7884); Delft University of Technology, Circuits and Systems (CAS) group, Delft, The Netherlands (GRID:grid.5292.c) (ISNI:0000 0001 2097 4740)




