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Real-time detection of pneumothorax using electrical impedance tomography

Costa, Eduardo L. V ; Chaves, Caroline N ; Gomes, Susimeire ; Beraldo, Marcelo A ; Volpe, Márcia S ; Tucci, Mauro R ; Schettino, Ivany A. L ; Bohm, Stephan H ; Carvalho, Carlos R. R ; Tanaka, Harki ; Lima, Raul G ; Amato, Marcelo B. P

Critical care medicine, 2008-04, Vol.36 (4), p.1230-1238 [Periódico revisado por pares]

United States: by the Society of Critical Care Medicine and Lippincott Williams & Wilkins

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  • Título:
    Real-time detection of pneumothorax using electrical impedance tomography
  • Autor: Costa, Eduardo L. V ; Chaves, Caroline N ; Gomes, Susimeire ; Beraldo, Marcelo A ; Volpe, Márcia S ; Tucci, Mauro R ; Schettino, Ivany A. L ; Bohm, Stephan H ; Carvalho, Carlos R. R ; Tanaka, Harki ; Lima, Raul G ; Amato, Marcelo B. P
  • Assuntos: Algorithms ; Animals ; Blood Gas Analysis ; Electric Impedance ; Hemodynamics ; Pneumothorax - diagnosis ; Sensitivity and Specificity ; Swine ; Tomography - methods
  • É parte de: Critical care medicine, 2008-04, Vol.36 (4), p.1230-1238
  • Notas: ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-2
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  • Descrição: OBJECTIVES: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. DESIGN:Prospective controlled laboratory animal investigation. SETTING:Experimental Pulmonology Laboratory of the University of São Paulo. SUBJECTS:Thirty-nine anesthetized mechanically ventilated supine pigs (31.0 ± 3.2 kg, mean ± sd). INTERVENTIONS: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. MEASUREMENTS AND MAIN 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.
  • Editor: United States: by the Society of Critical Care Medicine and Lippincott Williams & Wilkins
  • Idioma: Inglês
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