Algorithms,modelling and $$ dot{V}{text{O}}_{ 2} $$ kinetics

This article summarises the pros and cons of different algorithms developed for estimating breath-by-breath (B-by-B) alveolar O₂ transfer (( dot{V}{text{O}}_{{ 2 {text{A}}}} )) in humans. ( dot{V}{text{O}}_{{ 2 {text{A}}}} ) is the difference between O₂ uptake at the mouth and changes in alveolar O₂ stores (?VO_2s), which for any given breath, are equal to the alveolar volume change at constant ( F_{{{text{AO}}_{ 2} }} [ (F_{{{text{A}}i{text{O}}_{ 2} }} Updelta {text{V}}_{{{text{A}}i}} ) ] ) plus the O₂ alveolar fraction change at constant volume ( [V_{{{text{A}}i - 1}} (F_{{{text{A}}i}} -F_{{{text{A}}i - 1}} )_{{{text{O}}_{ 2} }} ] ), where V _Ai?1 is the alveolar volume at the beginning of a breath. Therefore, ( dot{V}{text{O}}_{{ 2 {text{A}}}} ) can be determined B-by-B provided that V _Ai?1 is: (a) set equal to the subject’s functional residual capacity (algorithm of Auchincloss, A) or to zero; (b) measured (optoelectronic plethysmography, OEP); (c) selected according to a procedure that minimises B-by-B variability (algorithm of Busso and Robbins, BR). Alternatively, the respiratory cycle can be redefined as the time between equal FO₂ in two subsequent breaths (algorithm of Grønlund, G), making any assumption of V _Ai?1 unnecessary. All the above methods allow an unbiased estimate of ( dot{V}{text{O}}_{ 2} ) at steady state, albeit with different precision. Yet the algorithms “per se” affect the parameters describing the B-by-B kinetics during exercise transitions. Among these approaches, BR and G, by increasing the signal-to-noise ratio of the measurements, reduce the number of exercise repetitions necessary to study ( dot{V}{text{O}}_{ 2} ) kinetics, compared to A approach. OEP and G (though technically challenging and conceptually still debated), thanks to their ability to track ?VO_2s changes during the early phase of exercise transitions, appear rather promising for investigating B-by-B gas exchange.