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Reliability of Transpulmonary Pressure-time Curve Profile to Identify Tidal Recruitment/hyperinflation in Experimental Unilateral Pleural Effusion

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Publisher Springer
Date 2016 Jul 21
PMID 27438965
Citations 2
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Abstract

The stress index (SI) is a parameter that characterizes the shape of the airway pressure-time profile (P/t). It indicates the slope progression of the curve, reflecting both lung and chest wall properties. The presence of pleural effusion alters the mechanical properties of the respiratory system decreasing transpulmonary pressure (Ptp). We investigated whether the SI computed using Ptp tracing would provide reliable insight into tidal recruitment/overdistention during the tidal cycle in the presence of unilateral effusion. Unilateral pleural effusion was simulated in anesthetized, mechanically ventilated pigs. Respiratory system mechanics and thoracic computed tomography (CT) were studied to assess P/t curve shape and changes in global lung aeration. SI derived from airway pressure (Paw) was compared with that calculated by Ptp under the same conditions. These results were themselves compared with quantitative CT analysis as a gold standard for tidal recruitment/hyperinflation. Despite marked changes in tidal recruitment, mean values of SI computed either from Paw or Ptp were remarkably insensitive to variations of PEEP or condition. After the instillation of effusion, SI indicates a preponderant over-distension effect, not detected by CT. After the increment in PEEP level, the extent of CT-determined tidal recruitment suggest a huge recruitment effect of PEEP as reflected by lung compliance. Both SI in this case were unaffected. We showed that the ability of SI to predict tidal recruitment and overdistension was significantly reduced in a model of altered chest wall-lung relationship, even if the parameter was computed from the Ptp curve profile.

Citing Articles

Performance and applications of bedside visual inspection of airway pressure-time curve profiles for estimating stress index in patients with acute respiratory distress syndrome.

Wongsurakiat P, Yuangtrakul N J Clin Monit Comput. 2018; 33(2):281-290.

PMID: 29744774 DOI: 10.1007/s10877-018-0153-0.


Journal of Clinical Monitoring and Computing 2017 end of year summary: respiration.

Karbing D, Perchiazzi G, Rees S, Jaffe M J Clin Monit Comput. 2018; 32(2):197-205.

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