Primary blast lung injury (PBLI) caused by exposure to high-intensity pressure waves is associated with parenchymal tissue injury and severe ventilation-perfusion mismatch. Although supportive ventilation is often required in patients with PBLI, maldistribution of gas flow in mechanically heterogeneous lungs may lead to further injury due to increased parenchymal strain and strain rate, which are difficult to predict in vivo. In this study, we developed a computational lung model with mechanical properties consistent with healthy and PBLI conditions. PBLI conditions were simulated with bilateral derecruitment and increased perihilar tissue stiffness. As a result of these tissue abnormalities, airway flow was heterogeneously distributed in the model under PBLI conditions, during both conventional mechanical ventilation (CMV) and high-frequency oscillatory ventilation. PBLI conditions resulted in over three-fold higher parenchymal strains compared to the healthy condition during CMV, with flow distributed according to regional tissue stiffness. During high-frequency oscillatory ventilation, flow distribution became increasingly heterogeneous and frequency-dependent. We conclude that the distribution and rate of parenchymal distension during mechanical ventilation depend on PBLI severity as well as ventilatory modality. These simulations may allow realistic assessment of the risks associated with ventilator-induced lung injury following PBLI, and facilitate the development of alternative lung-protective ventilation modalities.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6515895 | PMC |
http://dx.doi.org/10.1093/milmed/usy305 | DOI Listing |
Mil Med
March 2019
Department of Anesthesia, University of Iowa Hospitals and Clinics, 200 Hawkins Drive, Iowa City, IA.
Primary blast lung injury (PBLI) caused by exposure to high-intensity pressure waves is associated with parenchymal tissue injury and severe ventilation-perfusion mismatch. Although supportive ventilation is often required in patients with PBLI, maldistribution of gas flow in mechanically heterogeneous lungs may lead to further injury due to increased parenchymal strain and strain rate, which are difficult to predict in vivo. In this study, we developed a computational lung model with mechanical properties consistent with healthy and PBLI conditions.
View Article and Find Full Text PDFAnnu Int Conf IEEE Eng Med Biol Soc
October 2016
To supply proper treatments to the primary blast lung injury (PBLI) patients, it is important to estimate the severity of the primary blast lung injury in accordance with the blast conditions. In this study, a blast-induced mechanical parameter (first principal stress) of lung was calculated using a finite element thorax model and the correlation between the survival rate of the subjects with blast-induced lung damage and an objective index that was related to the first principal stress of the lung model. This study propose the objective index for the estimation of the degree of PBLI.
View Article and Find Full Text PDFWhile protective measures have been taken to mitigate injury to the thorax during a blast exposure, primary blast lung injury (PBLI) is still evident in mounted/in vehicle cases during military conflicts. Moreover, civilians, who are unprotected from blast exposure, can be severely harmed by terrorist attacks that use improvised explosive devices (IEDs). Since the lungs are the most susceptible organ due to their air-filled nature, PBLI is one of the most serious injuries seen in civilian blast cases.
View Article and Find Full Text PDFPurpose: The complex competency labeled practice-based learning and improvement (PBLI) by the Accreditation Council for Graduate Medical Education (ACGME) incorporates core knowledge in evidence-based medicine (EBM). The purpose of this study was to operationally define a "PBLI-EBM" domain for assessing resident physician competence.
Method: The authors used an iterative design process to first content analyze and map correspondences between ACGME and EBM literature sources.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!