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Resumo:
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 Sao 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.
Resumo:
BACKGROUND: The arterial pulse pressure variation induced by mechanical ventilation (Delta PP) has been shown to be a predictor of fluid responsiveness. Until now, Delta PP has had to be calculated offline (from a computer recording or a paper printing of the arterial pressure curve), or to be derived from specific cardiac output monitors, limiting the widespread use of this parameter. Recently, a method has been developed for the automatic calculation and real-time monitoring of Delta PP using standard bedside monitors. Whether this method is to predict reliable predictor of fluid responsiveness remains to be determined. METHODS: We conducted a prospective clinical study in 59 mechanically ventilated patients in the postoperative period of cardiac surgery. Patients studied were considered at low risk for complications related to fluid administration (pulmonary artery occlusion pressure <20 mm Hg, left ventricular ejection fraction >= 40%). All patients were instrumented with an arterial line and a pulmonary artery catheter. Cardiac filling pressures and cardiac output were measured before and after intravascular fluid administration (20 mL/kg of lactated Ringer`s solution over 20 min), whereas Delta PP was automatically calculated and continuously monitored. RESULTS: Fluid administration increased cardiac output by at least 15% in 39 patients (66% = responders). Before fluid administration, responders and nonresponders were comparable with regard to right atrial and pulmonary artery occlusion pressures. In contrast, Delta PP was significantly greater in responders than in nonresponders, (17% +/- 3% vs 9% +/- 2%, P < 0.001). The Delta PP cut-off value of 12% allowed identification of responders with a sensitivity of 97% and a specificity of 95%. CONCLUSION: Automatic real-time monitoring of Delta PP is possible using a standard bedside rnonitor and was found to be a reliable method to predict fluid responsiveness after cardiac surgery. Additional studies are needed to determine if this technique can be used to avoid the complications of fluid administration in high-risk patients.