| Summary: | Clinicians use several oxygen-based indices in intensive care units as surrogates to determine the condition of the patient’s lung and verify monitoring progress. Examples of these oxygen indices include the ratio of arterial oxygen tension to inspired oxygen fraction (PaO2/FiO2 ratio); arterial/alveolar oxygen tension ratio (PaO2/PAO2); alveolar–arterial oxygen tension difference (PA-aO2); respiratory index (RI= (PA-aO2)/PaO2), and content-based venous admixture (Qs/Qt). One of the issues with this approach is that these indices fail to take into consideration several additional external pulmonary physiological factors and, as such, these indices could potentially mislead clinicians. This thesis explores the nature of the oxygen-tension-based indices response and examined the effect that varying certain external pulmonary factors, such as FiO2, PaCO2, Hb, respiratory rate, oxygen consumption, cardiac output, and respiratory quotient, had on PaO2 using virtual subjects and patients’ data to quantify oxygenation defect through a combination of mathematics, different diseases, and pathophysiology. There were one or two approaches that could lead us to the answer, and many dead end routes. Eventually, the research produced a new index that was compared and validated using two approaches. First, on virtual subjects with lung pathologies that were commonly seen in the intensive care unit and then on real clinical data that was obtained from the intensive care unit. The results of these validation investigations indicated that the proposed index is more robust and resistant to variations in certain external pulmonary factors than the PaO2/FiO2 ratio. As such, there is a strong indication that it may help to improve the quality of patient care provided. The feasibility of manually calculating and applying this newly proposed index in the ICU is an issue that merits further exploration. Theoretically, if the newly proposed index was found to be practicable, it could improve the healthcare provided; reduce the cost of unnecessary blood work, and save time and effort. However, due to the time it takes to calculate crPaO2 manually, the use of medical technology and computer applications is desirable.
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