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Farhad Nezami



Job Title

Lead Investigator

Academic Rank




Emma Roussel, Mohammadmostafa Asheghan, Bradford Smith, Farhad R. Nezami

Principal Investigator

Dr. Farhad R. Nezami

Research Category: Lung Research


Leveraging Ventilators as a Diagnostic Tool for Lung Injury in ARDS Patients Using a Lumped Parameter Model

Scientific Abstract

Acute Respiratory Distress Syndrome (ARDS) is a life-threatening lung injury where fluid accumulates in alveoli, causing shortness of breath and restricted blood oxygenation. Mechanical ventilation is the main treatment; however, settings vary with ARDS severity and there is currently no optimal method to determine them. Our aim is to develop a novel lumped parameter model (LPM) for lung to ultimately leverage the ventilator as a diagnostic real-time tool to determine lung injury’s severity at the bedside. LPM relied on distinction between three alveoli regions (healthy/unstable but recruitable/damaged) wherein relative proportions can be used to measure ARDS severity. LPM was implemented in MATLAB & Simulink. Alveoli regions were modeled in distinct RC compartments, and proportions linearly influenced RC components. Proportions estimation framework, using two sets of pressure-flow ventilator measurements, was tested on Control (n=6) and ARDS-model mice (n=6). Promising results were obtained and later verified as healthy alveoli’s proportion was much higher in Control compared to ARDS mice, and ARDS mice had a higher proportion of recruitable alveoli, reflecting lung injury. This early-stage, yet promising study, suggests that our novel LPM can successfully discriminate healthy from ARDS patients based on simple ventilator recordings, highlighting the diagnostic utility of ventilators as hypothesized.

Lay Abstract

Acute Respiratory Distress Syndrome (ARDS) is a life-threatening disease where fluid accumulation in the lungs makes breathing difficult for patients. A mechanical device, called ventilator, helps patients breathe pushing air into and out of the lungs. Machine settings should be changed according to how much lung is injured and unfortunately there exists no straightforward method to do so. We herein intend to develop a new computational procedure to estimate lung injury severity and determine appropriate ventilator settings. We simulated an electrical circuit mimicking lungs properties which modeled three possible lung regions: healthy, unstable (prone to collapse) and damaged (collapsed) zones. The relative size of each region is directly related to ARDS severity. We developed a procedure to deduce proportions of each zone based on patient’s ventilator measurements, easily acquired at the bedside. Our method was tested on ventilator data from healthy and ARDS mice. Promising results were obtained wherein ARDS mice demonstrated smaller healthy zone and larger unstable and damaged zones than healthy mice, reflecting the experimentally-induced lung injury. Therefore, our new model successfully discriminates healthy from ARDS subjects. Future steps will include testing whether the model can estimate the amount of injury, and then use the tool in clinics.

Clinical Implications

Translating this novel tool to the clinical setting will tremendously help physicians to leverage ventilators as diagnostic tools for ARDS severity and continuously optimize ventilator settings in a patient-specific manner, thereby reducing ventilator-induced injury and leading to faster patient recovery.