Mechanical Ventilation (MV) therapy is one of the most common treatments offered to
patients with respiratory failure in ICU. MV assists patient recovery by completely or
partially taking over the breathing process and helping with oxygen delivery and removal
of carbon dioxide. However, inappropriate MV settings mismatched to a given patient’s
condition can cause further damage. On the other hand, suboptimal MV settings can
increase the length of stay of the patient in ICU and increase the cost of treatment.
Acute Respiratory Distress Syndrome (ARDS) is a major form of Acute Lung Injury (ALI)
where clinicians offer a supportive environment for patient recovery by application of MV.
ARDS is characterised by inflamed and fluid filled lungs that result in alveolar collapse
and thus severe hypoxemia. Application of positive end expiratory pressure (PEEP) is
employed to recruit and retain lung units to maximise gas exchange. However, a delicate
trade-off is required between maximising gas exchange and preventing further unintended
damage to the lungs, when determining optimum PEEP level.
Currently, no specific protocols to determine optimum PEEP level exist and selection of
PEEP is dependent on medical intuition and experience, primarily due to lack of easy
methods to determine patient – specific condition at the patient’s bedside. A mathematical
recruitment model is developed in Labview to help determine patient – specific condition
based on fundamental lung physiology and engineering principals in this thesis. The model
utilises readily available clinical data to determine parameters that identify underlying
patient – specific lung characteristics and conditions. Changes in these parameters can be
monitored over time and compared between patients to determine the severity of the
disease and evolution of disease with time.
A second model is developed to determine dynamic functional residual capacity (dFRC),
that represents the extra volume retained in a lung through application of PEEP. The model
extends previous efforts in the field that applied the stress – strain theory to lung
mechanics to estimate dFRC. This model estimates the patient’s dFRC using readily
available clinical data (PV data) and can be monitored over time to determine changes in a
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given patient’s condition. The dFRC model introduces a new parameter, , which is
considered a population constant for the particular PEEP. The model offers an easy and
reliable method to determine dFRC since other methods are normally invasive or require
interruption of MV.
The models developed were validated against real – time clinical data obtained through
clinical trials. The recruitment model was found to fit the clinical data well with error
values within acceptable limits. It also enabled identification of parameters that reflect the
underlying patient – specific lung condition. The dFRC model was able to estimate the
dFRC for a patient with high level of accuracy for clinically applicable PEEP levels. The
two models work well in conjunction with each other and provide a novel and easy method
to clinicians to determine patient – specific lung characteristics and ultimately determine
optimal MV treatment parameters, especially PEEP.
| Identifer | oai:union.ndltd.org:canterbury.ac.nz/oai:ir.canterbury.ac.nz:10092/7005 |
| Date | January 2012 |
| Creators | Mishra, Ankit Nidhishchandra |
| Publisher | University of Canterbury. Mechanical Engineering |
| Source Sets | University of Canterbury |
| Language | English |
| Detected Language | English |
| Type | Electronic thesis or dissertation, Text |
| Rights | Copyright Ankit Nidhishchandra Mishra, http://library.canterbury.ac.nz/thesis/etheses_copyright.shtml |
| Relation | NZCU |
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