title: Real-time detection of pneumothorax using electrical impedance tomography.
abstract: OBJECTIVE
  Pneumothorax is a frequent complication during mechanical ventilation. Electrical impedance tomography (EIT) is a noninvasive tool that allows real-time imaging of regional ventilation. The purpose of this study was to 1) identify characteristic changes in the EIT signals associated with pneumothoraces; 2) develop and fine-tune an algorithm for their automatic detection; and 3) prospectively evaluate this algorithm for its sensitivity and specificity in detecting pneumothoraces in real time.
  
  
  METHODS
  Prospective controlled laboratory animal investigation.
  
  
  METHODS
  Experimental Pulmonology Laboratory of the University of São Paulo.
  
  
  METHODS
  Thirty-nine anesthetized mechanically ventilated supine pigs (31.0 +/- 3.2 kg, mean +/- SD).
  
  
  METHODS
  In a first group of 18 animals monitored by EIT, we either injected progressive amounts of air (from 20 to 500 mL) through chest tubes or applied large positive end-expiratory pressure (PEEP) increments to simulate extreme lung overdistension. This first data set was used to calibrate an EIT-based pneumothorax detection algorithm. Subsequently, we evaluated the real-time performance of the detection algorithm in 21 additional animals (with normal or preinjured lungs), submitted to multiple ventilatory interventions or traumatic punctures of the lung.
  
  
  RESULTS
  Primary EIT relative images were acquired online (50 images/sec) and processed according to a few imaging-analysis routines running automatically and in parallel. Pneumothoraces as small as 20 mL could be detected with a sensitivity of 100% and specificity 95% and could be easily distinguished from parenchymal overdistension induced by PEEP or recruiting maneuvers. Their location was correctly identified in all cases, with a total delay of only three respiratory cycles.
  
  
  CONCLUSIONS
  We created an EIT-based algorithm capable of detecting early signs of pneumothoraces in high-risk situations, which also identifies its location. It requires that the pneumothorax occurs or enlarges at least minimally during the monitoring period. Such detection was operator-free and in quasi real-time, opening opportunities for improving patient safety during mechanical ventilation.
authors: Respiratory Intensive Care Unit, University of São Paulo School of Medicine, Brazil.
authors: Eduardo L V
authors: EL
authors: Costa
authors: Caroline N
authors: CN
authors: Chaves
authors: Susimeire
authors: S
authors: Gomes
authors: Marcelo A
authors: MA
authors: Beraldo
authors: Márcia S
authors: MS
authors: Volpe
authors: Mauro R
authors: MR
authors: Tucci
authors: Ivany A L
authors: IA
authors: Schettino
authors: Stephan H
authors: SH
authors: Bohm
authors: Carlos R R
authors: CR
authors: Carvalho
authors: Harki
authors: H
authors: Tanaka
authors: Raul G
authors: RG
authors: Lima
authors: Marcelo B P
authors: MB
authors: Amato
mesh_terms: D000465:Algorithms; D000818:Animals; D001784:Blood Gas Analysis; D017097:Electric Impedance; D006439:Hemodynamics; D011030:Pneumothorax / Q000175:diagnosis*; D012680:Sensitivity and Specificity; D013552:Swine; D014054:Tomography / Q000379:methods*