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

ABSTRACT

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 ventilationperfusion 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.