Towards goal-directed perfusion - Part II: Temperature dependence of Q ₁₀ , oxygen-demand dependence on age and weight, further clinical insights

BackgroundGoal-directed perfusion (GDP) during cardiopulmonary bypass (CPB) seeks to match oxygen delivery with patient-specific metabolic demand, yet determinants of oxygen demand and dynamics of oxygen extraction ratio (OER) are not well characterised in paediatric populations. Building on our GARIX (Global AutoRegressive Integrated model with eXogenous variables and an equilibrium force) model from Part 1, we analysed minute-level intraoperative data to derive clinical insights.MethodsGARIX integrates autoregressive memory, exogenous clinical variables (cardiac index, haemoglobin, arterial oxygen saturation, temperature), and an equilibrium component defining indexed oxygen demand (tVO₂i) as a function of age, weight, and temperature. We applied GARIX to 20,443 minutes of data from 293 paediatric CPB procedures to study: (1) age- and weight-related variation in oxygen demand; (2) temperature dependence of demand, estimating local and interval Q₁₀; (3) age-stratified OER responses to haemoglobin and other variables; and (4) time evolution of the mismatch between oxygen consumption and demand after step changes in oxygen delivery.ResultstVO₂i followed a non-monotonic pattern, rising from birth to ∼3 years and declining thereafter. Metabolic demand decreased nonlinearly with temperature, with a 17% reduction from 37°C to 32°C and 39% further reduction to 27°C. Local Q₁₀ ranged from ∼1.3 near normothermia to ∼3.8 in deep hypothermia. OER responsiveness to haemoglobin was blunted in neonates and infants compared with older children. Simulations revealed transient over- and under-oxygenation following step changes in oxygen delivery due to lags in OER adaptation.ConclusionsWe identify risks of over- and under-oxygenation during CPB despite normal OER readings, challenge the use of fixed targets and constant Q₁₀, and highlight age- and weight-dependent dynamics. These findings support individualised, time-aware perfusion strategies and lay the groundwork for model-guided decision support and control.