Is the asynchronous phase of thoracoabdominal movement a novel feature of successful extubation? A preliminary result

Mechanical ventilation is necessary to maintain patients’ life in intensive care units. However, too early or too late extubation may injure the muscles or lead to respiratory failure. Therefore, the spontaneous breathing trial (SBT) is applied for testing whether the patients can spontaneously breathe or not. However, previous evidence still reported 15%~20% of the rate of extubation fail. The monitor only considers the ventilation variables during SBT. Therefore, this study measures the asynchronization between thoracic and abdomen wall movement (TWM and AWM) by using instantaneous phase difference method (IPD) during SBT for 120 minutes. The respiratory inductive plethysmography were used for TWM and AWM measurement. The preliminary result recruited 31 signals for further analysis. The result showed that in successful extubation group can be classified into two groups, IPD increase group, and IPD decrease group; but in extubation fail group, the IPD value only increase. Therefore, the IPD decrease group can almost perfectly be discriminated with extubation fail group, especially after 70 minutes (Area under curve of operating characteristic curve was 1). These results showed IPD is an important key factor to find whether the patient is suitable for extubation or not. These finding suggest that the asynchronization between TWM and AWM should be considered as a predictor of extubation outcome. In future work, we plan to recruit 150 subjects to validate the result of this preliminary result. In addition, advanced machine learning method is considered to apply for building effective models to discriminate the IPD increase group and extubation fail group.Clinical Relevance— The finding of this study is that the patients whose average IPD of 95 to 100 minutes was smaller than average IPD of first 5 minutes of SBT could be 100% successful extubation. In addition, ability of discrimination of average IPD after 70 minutes presents AUC 1.

[1]  M. Tobin.  Respiratory monitoring during mechanical ventilation. , 1990, Critical care clinics.

[2]  Arthur S Slutsky,et al.  Mechanical Ventilation: State of the Art. , 2017, Mayo Clinic proceedings.

[3]  M J Tobin,et al.  Advances in mechanical ventilation. , 2001, The New England journal of medicine.

[4]  R. Andriolo,et al.  Pressure support versus T-tube for weaning from mechanical ventilation in adults. , 2014, The Cochrane database of systematic reviews.

[5]  C. Chao,et al.  An Artificial Neural Network Model for Predicting Successful Extubation in Intensive Care Units , 2018, Journal of clinical medicine.

[6]  Norden E. Huang,et al.  Complementary Ensemble Empirical Mode Decomposition: a Novel Noise Enhanced Data Analysis Method , 2010, Adv. Data Sci. Adapt. Anal..

[7]  Giorgio Conti,et al.  A prospective, blinded evaluation of indexes proposed to predict weaning from mechanical ventilation , 2004, Intensive Care Medicine.

[8]  M A Sackner,et al.  Accuracy of respiratory inductive plethysmograph over wide range of rib cage and abdominal compartmental contributions to tidal volume in normal subjects and in patients with chronic obstructive pulmonary disease. , 1984, The American review of respiratory disease.

[9]  Norden E. Huang,et al.  On Instantaneous Frequency , 2009, Adv. Data Sci. Adapt. Anal..

[10]  Shahriar Gharibzadeh,et al.  Nonlinear model for estimating respiratory volume based on thoracoabdominal breathing movements , 2013, Respirology.

[11]  S. N. Nemer,et al.  Evaluation of maximal inspiratory pressure, tracheal airway occlusion pressure, and its ratio in the weaning outcome. , 2009, Journal of critical care.

[12]  T. Hsiao,et al.  Instantaneous phase difference analysis between thoracic and abdominal movement signals based on complementary ensemble empirical mode decomposition , 2016, BioMedical Engineering OnLine.

[13]  B. Krieger,et al.  Variability of the breathing pattern before and after extubation. , 1988, Chest.

[14]  L. Blanch,et al.  Effect of spontaneous breathing trial duration on outcome of attempts to discontinue mechanical ventilation. Spanish Lung Failure Collaborative Group. , 1999, American journal of respiratory and critical care medicine.

[15]  Martina Mueller,et al.  Predicting Extubation Outcome in Preterm Newborns: A Comparison of Neural Networks with Clinical Expertise and Statistical Modeling , 2004, Pediatric Research.

[16]  Romesh Stanislaus,et al.  Can Machine Learning Methods Predict Extubation Outcome in Premature Infants as well as Clinicians? , 2013, Journal of neonatal biology.

[17]  R. Arena,et al.  Maximum inspiratory pressure and rapid shallow breathing index as predictors of successful ventilator weaning , 2015, Journal of physical therapy science.

[18]  L. Segal,et al.  Evolution of pattern of breathing during a spontaneous breathing trial predicts successful extubation , 2010, Intensive Care Medicine.

[19]  K. Burns,et al.  Spontaneous Breathing Trials With T-Piece or Pressure Support Ventilation , 2016, Respiratory Care.

[20]  Tzu-Chien Hsiao,et al.  Towards instantaneous phase difference on the COPD pre-discrimination , 2018, 2018 IEEE 23rd International Conference on Digital Signal Processing (DSP).

[21]  M. Tobin,et al.  A prospective study of indexes predicting the outcome of trials of weaning from mechanical ventilation. , 1992, The New England journal of medicine.