Literature DB >> 16484913

Power of breathing determined noninvasively with use of an artificial neural network in patients with respiratory failure.

Michael J Banner1, Neil R Euliano, Vic Brennan, Carl Peters, A Joseph Layon, Andrea Gabrielli.   

Abstract

OBJECTIVE: To determine work of breathing per minute or power of breathing noninvasively (POB(N)) by using an artificial neural network (ANN) without the need for an esophageal catheter in patients with respiratory failure.
DESIGN: Prospective study comparing the relationship between POB(N) and invasively measured power of breathing (POB(I)).
SETTING: Intensive care unit of a university hospital. PATIENTS: Forty-five intubated adults (age, 51 +/- 11 yrs; weight, 71 +/- 18 kg; 28 males and 17 females) receiving pressure support ventilation (PSV).
INTERVENTIONS: Data from an esophageal catheter and airway pressure/flow sensor were used to measure POB(I). A pretrained ANN provided real time calculation of POB(N). POB(I) and POB(N) were measured at various levels of PSV, ranging from 5 to 25 cm H(2)O.
MEASUREMENTS AND MAIN RESULTS: POB(N) was highly correlated with POB(I) (r = 0.91; p < .002), and because POB(N) explained or predicted 83% of the variance in POB(I), it was considered a very good predictor (r(2) = 0.83; p < .002). Bias was negligible (0.00) and precision was clinically acceptable (2.2 J/min).
CONCLUSIONS: POB can be calculated noninvasively with reasonable clinical accuracy for patients receiving ventilatory support by using an ANN. This method obviates the need for inserting an esophageal catheter and thus greatly simplifies measurement of POB. POB(N) may be a clinically useful tool for consideration when setting PSV to unload the respiratory muscles. Before considering its use in clinical practice, POB(N) would need to be incorporated within the context of load tolerance and shown to improve outcomes.

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Year:  2006        PMID: 16484913     DOI: 10.1097/01.CCM.0000206288.90613.1C

Source DB:  PubMed          Journal:  Crit Care Med        ISSN: 0090-3493            Impact factor:   7.598


  5 in total

1.  Noninvasive work of breathing improves prediction of post-extubation outcome.

Authors:  Michael J Banner; Neil R Euliano; A Daniel Martin; Nawar Al-Rawas; A Joseph Layon; Andrea Gabrielli
Journal:  Intensive Care Med       Date:  2011-11-24       Impact factor: 17.440

2.  Abnormally increased power of breathing as a complication of closed endotracheal suction catheter systems.

Authors:  Mehmet S Ozcan; Steven W Bonett; A Daniel Martin; Andrea Gabrielli; A Joseph Layon; Michael J Banner
Journal:  Respir Care       Date:  2006-04       Impact factor: 2.258

3.  Real time noninvasive estimation of work of breathing using facemask leak-corrected tidal volume during noninvasive pressure support: validation study.

Authors:  Michael J Banner; Carl G Tams; Neil R Euliano; Paul J Stephan; Trevor J Leavitt; A Daniel Martin; Nawar Al-Rawas; Andrea Gabrielli
Journal:  J Clin Monit Comput       Date:  2015-06-13       Impact factor: 2.502

Review 4.  Journal of Clinical Monitoring and Computing 2016 end of year summary: respiration.

Authors:  D S Karbing; S E Rees; M B Jaffe
Journal:  J Clin Monit Comput       Date:  2017-03-02       Impact factor: 2.502

5.  Artificial neural network approach to predict surgical site infection after free-flap reconstruction in patients receiving surgery for head and neck cancer.

Authors:  Pao-Jen Kuo; Shao-Chun Wu; Peng-Chen Chien; Shu-Shya Chang; Cheng-Shyuan Rau; Hsueh-Ling Tai; Shu-Hui Peng; Yi-Chun Lin; Yi-Chun Chen; Hsiao-Yun Hsieh; Ching-Hua Hsieh
Journal:  Oncotarget       Date:  2018-02-09
  5 in total

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