An algorithm to describe the oxygen equilibrium curve for the thoroughbred racehorse.

The research develops an algorithm to determine the oxygen equilibrium curve for Thoroughbred racehorses, providing a more precise understanding of their oxygen saturation levels under different conditions.

Understanding the Oxygen Equilibrium Curve

• The oxygen equilibrium curve (OEC) is a graphical representation that shows the relationship between the oxygen saturation level in the blood and the partial pressure of oxygen. It helps in understanding how oxygen binds to the haemoglobin in the blood and the factors that influence this binding, such as temperature, pH and partial pressure of carbon dioxide (PCO2).

Creation of the Algorithm

• The researchers derived the algorithm from raw oxygen equilibrium curve data that was obtained under standard conditions. The data used was specifically for Thoroughbred horses and collected in a uniform manner to ensure accuracy.
• The creation of the algorithm involved a curve-fitting procedure that was based on the human blood algorithm developed by Kelman in 1966. However, it was adapted to match the specifics of a horse’s physiological conditions.
• The algorithm also incorporated various factors such as temperature, fixed acid and net CO2 coefficients. This was done to enable accurate calculation of oxygen saturation from any combination of partial pressure of oxygen (PO2), temperature, pH and PCO2.

Validation of the Algorithm

• The newly created algorithm was tested using actual blood gas data from Thoroughbred horses. The data was gathered from treadmill exercise tests that simulate similar conditions to those in a race.
• A standardized breathing test was also used, where horses inhaled different gas mixtures. This helped in understanding how various factors affect oxygen saturation under different inhaled gas compositions.
• The algorithm created for the Thoroughbred racehorse proved to be more accurate than the original one that was created for humans. This suggests that while the human algorithm by Kelman was a great starting point, a more specialized algorithm leads to better results when dealing with different species.