Literature DB >> 25524362

Reliability and accuracy of the thoracic impedance signal for measuring cardiopulmonary resuscitation quality metrics.

Erik Alonso1, Jesús Ruiz2, Elisabete Aramendi2, Digna González-Otero2, Sofía Ruiz de Gauna2, Unai Ayala2, James K Russell3, Mohamud Daya4.   

Abstract

AIM: To determine the accuracy and reliability of the thoracic impedance (TI) signal to assess cardiopulmonary resuscitation (CPR) quality metrics.
METHODS: A dataset of 63 out-of-hospital cardiac arrest episodes containing the compression depth (CD), capnography and TI signals was used. We developed a chest compression (CC) and ventilation detector based on the TI signal. TI shows fluctuations due to CCs and ventilations. A decision algorithm classified the local maxima as CCs or ventilations. Seven CPR quality metrics were computed: mean CC-rate, fraction of minutes with inadequate CC-rate, chest compression fraction, mean ventilation rate, fraction of minutes with hyperventilation, instantaneous CC-rate and instantaneous ventilation rate. The CD and capnography signals were accepted as the gold standard for CC and ventilation detection respectively. The accuracy of the detector was evaluated in terms of sensitivity and positive predictive value (PPV). Distributions for each metric computed from the TI and from the gold standard were calculated and tested for normality using one sample Kolmogorov-Smirnov test. For normal and not normal distributions, two sample t-test and Mann-Whitney U test respectively were applied to test for equal means and medians respectively. Bland-Altman plots were represented for each metric to analyze the level of agreement between values obtained from the TI and gold standard.
RESULTS: The CC/ventilation detector had a median sensitivity/PPV of 97.2%/97.7% for CCs and 92.2%/81.0% for ventilations respectively. Distributions for all the metrics showed equal means or medians, and agreements >95% between metrics and gold standard was achieved for most of the episodes in the test set, except for the instantaneous ventilation rate.
CONCLUSION: With our data, the TI can be reliably used to measure all the CPR quality metrics proposed in this study, except for the instantaneous ventilation rate.
Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Entities:  

Keywords:  Automated external defibrillator; Cardiopulmonary resuscitation quality; Chest compression; Thoracic impedance; Ventilations

Mesh:

Year:  2014        PMID: 25524362     DOI: 10.1016/j.resuscitation.2014.11.027

Source DB:  PubMed          Journal:  Resuscitation        ISSN: 0300-9572            Impact factor:   5.262


  9 in total

1.  Association of ventilation with outcomes from out-of-hospital cardiac arrest.

Authors:  Mary P Chang; Yuanzheng Lu; Brian Leroux; Elisabete Aramendi Ecenarro; Pamela Owens; Henry E Wang; Ahamed H Idris
Journal:  Resuscitation       Date:  2019-05-18       Impact factor: 5.262

2.  A Method to Detect Presence of Chest Compressions During Resuscitation Using Transthoracic Impedance.

Authors:  Jason Coult; Jennifer Blackwood; Thomas D Rea; Peter J Kudenchuk; Heemun Kwok
Journal:  IEEE J Biomed Health Inform       Date:  2019-05-24       Impact factor: 5.772

3.  Automatic identification of compressions and ventilations during CPR based on the fuzzy c-means clustering and deep belief network.

Authors:  He-Hua Zhang; Li Yang; An-Hai Wei; Ao-Wen Duan; Yong-Ming Li; Ping Zhao; Yong-Qin Li
Journal:  Ann Transl Med       Date:  2020-09

4.  Automatic Detection of Ventilations During Mechanical Cardiopulmonary Resuscitation.

Authors:  Xabier Jaureguibeitia; Unai Irusta; Elisabete Aramendi; Pamela C Owens; Henry E Wang; Ahamed H Idris
Journal:  IEEE J Biomed Health Inform       Date:  2020-01-17       Impact factor: 5.772

5.  Novel application of thoracic impedance to characterize ventilations during cardiopulmonary resuscitation in the pragmatic airway resuscitation trial.

Authors:  Michelle M J Nassal; Xabier Jaureguibeitia; Elisabete Aramendi; Unai Irusta; Ashish R Panchal; Henry E Wang; Ahamed Idris
Journal:  Resuscitation       Date:  2021-09-28       Impact factor: 5.262

6.  Deep Neural Networks for ECG-Based Pulse Detection during Out-of-Hospital Cardiac Arrest.

Authors:  Andoni Elola; Elisabete Aramendi; Unai Irusta; Artzai Picón; Erik Alonso; Pamela Owens; Ahamed Idris
Journal:  Entropy (Basel)       Date:  2019-03-21       Impact factor: 2.524

7.  Quality of Cardiopulmonary Resuscitation and 5-Year Survival Following in-Hospital Cardiac Arrest.

Authors:  Lone Due Vestergaard; Kasper Glerup Lauridsen; Niels Henrik Vinther Krarup; Jane Uhrenholt Kristensen; Lone Kaerslund Andersen; Bo Løfgren
Journal:  Open Access Emerg Med       Date:  2021-12-16

8.  Feedback on the Rate and Depth of Chest Compressions during Cardiopulmonary Resuscitation Using Only Accelerometers.

Authors:  Sofía Ruiz de Gauna; Digna M González-Otero; Jesus Ruiz; James K Russell
Journal:  PLoS One       Date:  2016-03-01       Impact factor: 3.240

9.  Monitoring chest compression rate in automated external defibrillators using the autocorrelation of the transthoracic impedance.

Authors:  Sofía Ruiz de Gauna; Jesus María Ruiz; Jose Julio Gutiérrez; Digna María González-Otero; Daniel Alonso; Carlos Corcuera; Juan Francisco Urtusagasti
Journal:  PLoS One       Date:  2020-09-30       Impact factor: 3.240

  9 in total

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