Development and preclinical testing of an adaptive algorithm for automated control of inspired oxygen in the preterm infant

Objective: To assess the performance of a novel algorithm for automated oxygen control using a simulation of oxygenation founded on in vivo data from preterm infants.

Methods: A proportional-integral-derivative (PID) control algorithm was enhanced by (i) compensation for the non-linear SpO₂-PaO₂ relationship, (ii) adaptation to the severity of lung dysfunction and (iii) error attenuation within the target range. Algorithm function with and without enhancements was evaluated by iterative linking with a computerised simulation of oxygenation. Data for this simulation (FiO₂ and SpO₂ at 1 Hz) were sourced from extant recordings from preterm infants (n=16), and converted to a datastream of values for ventilation:perfusion ratio and shunt. Combination of this datastream second by second with the FiO₂ values from the algorithm under test produced a sequence of novel SpO₂ values, allowing time in the SpO₂ target range (91%-95%) and in various degrees of hypoxaemia and hyperoxaemia to be determined. A PID algorithm with 30 s lockout after each FiO₂ adjustment, and a proportional-derivative (PD) algorithm were also evaluated.

Results: Separate addition of each enhancing feature to the PID algorithm showed a benefit, but not with uniformly positive effects. The fully enhanced algorithm was optimal for the combination of targeting the desired SpO₂ range and avoiding time in, and episodes of, hypoxaemia and hyperoxaemia. This algorithm performed better than one with a 30 s lockout, and considerably better than PD control.

Conclusions: An enhanced PID algorithm was very effective for automated oxygen control in a simulation of oxygenation, and deserves clinical evaluation.