Computationally Optimized Ventilation in the Prehospital Hospital Setting
Abstract
Severe trauma, sepsis, hemorrhage, or burns, which often occur to combat trauma patients, can cause an excessive systemic inflammation that over-stimulates the body’s immune system. This systemic inflammatory response syndrome (SIRS) can cause acute respiratory distress syndrome (ARDS). ARDS occurred so often to soldiers in Vietnam that it was called “Da Nang Lung” and remains a serious military and civilian problem. Indeed, in 2017 the U.S Breast Cancer Statistics organization predicted death from breast cancer at 40,610, whereas deaths from ARDS were projected to be around 75,000; thus, in 2017 there was ~34,000 more deaths from ARDS than from breast cancer. The hallmark of ARDS pathology is lung collapse and instability (i.e., lung tissue collapses and reopens with each breath, causing severe mechanical damage), which necessitates mechanical ventilation. Improper settings on the mechanical ventilator can deliver the incorrect airway pressure wave, causing a secondary ventilator induced lung injury (VILI), which can significantly increase ARDS mortality. If the ventilation strategy can open and stabilize the lung as soon as mechanical ventilation is applied, VILI can be prevented and ARDS mortality reduced. Our novel concept is a computational modeling approach that will adjust the mechanical ventilator settings appropriately to keep the lung open and stable. The goal of our study is to design a ventilation strategy that will protect the lungs of wounded warfighters as soon as they are placed on mechanical ventilation in the combat theater and during transport from the field of combat. Our protective mechanical breath will be designed to open and stabilize the lung. We have previously shown that our basic Time-Controlled Adaptive Ventilation (TCAV) protocol with an extended time at inspiration and a very short time at expiration is effective at opening the lung and keeping it open. In the proposed study, we will modify and improve our TCAV protocol by computationally directing (CD) the settings based on changes in lung pathology (CD-TCAV). This will personalize the protective mechanical breath to each patient. Our CD-TCAV strategy is based on the understanding that dynamic lung volume change is viscoelastic in nature. Why is viscoelastic lung volume change important? Because there is a time lag from when the mechanical breath is pushed into the lung and lung tissue opens, and when the breath is exhaled and lung tissue collapses. Thus, we will use time to open and stabilize the lung by the following mechanisms: (1) an extended time at inspiration will gradually “nudge” open the lung and (2) minimal expiratory duration will not give the lung sufficient time to collapse. Our CD-TCAV protocol will be built into a military-grade transport ventilator (ZOLL 731 EMV+) already in military service. Our central hypothesis is that a computationally directed, time-dependent, and adaptive mechanical breath (CD-TCAV protocol) will optimally open and stabilize the lung, minimize VILI, and reduce the incidence and mortality of ARDS. Our objective here is to: (1) design candidate ventilator protocols for the injured lung, using structurally explicit computational models of the porcine respiratory system, (2) build the ability to deliver the CD-TCAV protocol into the ZOLL 731 EMV+, and (3) demonstrate that the CD-TCAV protocol will minimize VILI more effectively than either the current standard of care protocol for transport ventilators or the current lung protective ARDS Network low tidal volume protocol; both of these ventilation strategies use a short inspiratory and long expiratory time. Our hypothesis will be tested in a clinically applicable porcine ARDS model. The rationale is that our computationally optimized, time-controlled breath will result in a paradigm shift in how patients at high risk of developing ARDS are ventilated, significantly reducing VILI-induced morbidity and ARDS mortality.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Mar 10, 2021
- Source ID
- W81XWH2010696
Entities
People
- Gary F Nieman
Organizations
- State University of New York Upstate Medical University
- United States Army