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Crit Care Med. 2008 Apr;36(4):1230-8. doi: 10.1097/CCM.0b013e31816a0380.

Real-time detection of pneumothorax using electrical impedance tomography.

Author information

1
Respiratory Intensive Care Unit, University of São Paulo School of Medicine, Brazil.

Abstract

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.

PMID:
18379250
DOI:
10.1097/CCM.0b013e31816a0380
[Indexed for MEDLINE]

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