Differences in regional pulmonary pressure-impedance curves before and after lung injury assessed with a novel algorithm

Bartłomiej Grychtol, Gerhard K. Wolf, John H. Arnold

    Research output: Contribution to journalArticlepeer-review

    22 Citations (Scopus)


    Global pressure-volume (PV) curves are an adjunct measure to describe lung characteristics in patients with acute respiratory distress syndrome (ARDS). There is convincing evidence that high peak inspiratory pressures (PIP) cause barotrauma, while optimized positive end-expiratory pressure (PEEP) helps avoid mechanical injury to the lungs by preventing repeated alveolar opening and closing. The optimal values of PIP and PEEP are deduced from the shape of the PV curve by the identification of so-called lower and upper inflection points. However, it has been demonstrated using electrical impedance tomography (EIT) that the inflection points vary across the lung. This study employs a simple curve-fitting technique to automatically define inflection points on both pressure-volume (PV) and pressure-impedance (PI) curves to asses the differences between global PV and regional PI estimates in animals before and after induced lung injury. The results demonstrate a clear increase in lower inflection point (LIP) along the gravitational axis both before and after lung injury. Moreover, it is clear from comparison of the local EIT-derived LIPs with those derived from global PV curves that a ventilation strategy based on the PV curve alone may leave dependent areas of the lung collapsed. EIT-based PI curve analysis may help choosing an optimal ventilation strategy.
    Original languageEnglish
    Pages (from-to)S137-S148
    JournalPhysiological Measurement
    Issue number6
    Publication statusPublished - Jun 2009


    • EIT
    • pressure-volume curve
    • inflection points
    • optimal ventilation strategy


    Dive into the research topics of 'Differences in regional pulmonary pressure-impedance curves before and after lung injury assessed with a novel algorithm'. Together they form a unique fingerprint.

    Cite this