Dargaville Peter A, Sadeghi Fathabadi Omid, Plottier Gemma K, Lim Kathleen, Wheeler Kevin I, Jayakar Rohan, Gale Timothy J
Neonatal Respiratory Group, Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia.
Department of Paediatrics, Royal Hobart Hospital, Hobart, Tasmania, Australia.
Arch Dis Child Fetal Neonatal Ed. 2017 Jan;102(1):F31-F36. doi: 10.1136/archdischild-2016-310650. Epub 2016 Sep 15.
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.
A proportional-integral-derivative (PID) control algorithm was enhanced by (i) compensation for the non-linear SpO-PaO 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 (FiO and SpO 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 FiO values from the algorithm under test produced a sequence of novel SpO values, allowing time in the SpO target range (91%-95%) and in various degrees of hypoxaemia and hyperoxaemia to be determined. A PID algorithm with 30 s lockout after each FiO adjustment, and a proportional-derivative (PD) algorithm were also evaluated.
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 SpO 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.
An enhanced PID algorithm was very effective for automated oxygen control in a simulation of oxygenation, and deserves clinical evaluation.
基于早产儿的体内数据模拟氧合，评估一种新型自动氧控算法的性能。
通过以下方式增强比例积分微分（PID）控制算法：（i）补偿非线性SpO-PaO关系；（ii）适应肺功能障碍的严重程度；（iii）在目标范围内衰减误差。通过与计算机化氧合模拟进行迭代链接，评估有无增强功能时的算法功能。该模拟的数据（1 Hz时的FiO和SpO）来自早产儿的现有记录（n = 16），并转换为通气与灌注比和分流值的数据流。将该数据流逐秒与受试算法的FiO值相结合，产生一系列新的SpO值，从而确定SpO目标范围（91%-95%）以及不同程度低氧血症和高氧血症的持续时间。还评估了每次FiO调整后有30秒锁定时间的PID算法和比例微分（PD）算法。
将每个增强功能单独添加到PID算法中均显示出益处，但并非都具有一致的积极效果。完全增强的算法在靶向所需SpO范围以及避免低氧血症和高氧血症的持续时间和发作方面最为理想。该算法的性能优于有30秒锁定时间的算法，且远优于PD控制算法。
在氧合模拟中，增强的PID算法对自动氧控非常有效，值得进行临床评估。
