title: Applying Computer Models to Realize Closed-Loop Neonatal Oxygen Therapy.
abstract: Within the context of automating neonatal oxygen therapy, this article describes the transformation of an idea verified by a computer model into a device actuated by a computer model. Computer modeling of an entire neonatal oxygen therapy system can facilitate the development of closed-loop control algorithms by providing a verification platform and speeding up algorithm development.
  
  
  
  In this article, we present a method of mathematically modeling the system's components: the oxygen transport within the patient, the oxygen blender, the controller, and the pulse oximeter. Furthermore, within the constraints of engineering a product, an idealized model of the neonatal oxygen transport component may be integrated effectively into the control algorithm of a device, referred to as the adaptive model. Manual and closed-loop oxygen therapy performance were defined in this article by 3 criteria in the following order of importance: percent duration of SpO2 spent in normoxemia (target SpO2 ± 2.5%), hypoxemia (less than normoxemia), and hyperoxemia (more than normoxemia); number of 60-second periods <85% SpO2 and >95% SpO2; and number of manual adjustments.
  
  
  
  Results from a clinical evaluation that compared the performance of 3 closed-loop control algorithms (state machine, proportional-integral-differential, and adaptive model) with manual oxygen therapy on 7 low-birth-weight ventilated preterm babies, are presented. Compared with manual therapy, all closed-loop control algorithms significantly increased the patients' duration in normoxemia and reduced hyperoxemia (P < 0.05). The number of manual adjustments was also significantly reduced by all of the closed-loop control algorithms (P < 0.05).
  
  
  
  Although the performance of the 3 control algorithms was equivalent, it is suggested that the adaptive model, with its ease of use, may have the best utility.
authors.affiliation: From the *British Columbia's Women's Hospital and Health Center, Vancouver, British Columbia, Canada; †School of Engineering, Simon Fraser University, Burnaby, British Columbia, Canada; and ‡Faculty of Engineering, University of Windsor, Windsor, Ontario, Canada.
authors.forename: Edmund
authors.initials: E
authors.lastname: Morozoff
authors.forename: John A
authors.initials: JA
authors.lastname: Smyth
authors.forename: Mehrdad
authors.initials: M
authors.lastname: Saif
mesh_terms: D000465:Algorithms; D015415:Biomarkers / Q000097:blood; D003198:Computer Simulation; D005260:Female; D006801:Humans; D018496:Hyperoxia / Q000097:blood / Q000175:diagnosis / Q000209:etiology; D000860:Hypoxia / Q000097:blood / Q000175:diagnosis / Q000209:etiology / Q000628:therapy*; D007230:Infant, Low Birth Weight; D007231:Infant, Newborn; D007234:Infant, Premature; D008297:Male; D008954:Models, Biological; D010092:Oximetry; D010100:Oxygen / Q000097:blood; D010102:Oxygen Inhalation Therapy / Q000009:adverse effects / Q000379:methods*; D012121:Respiration, Artificial / Q000009:adverse effects / Q000379:methods*; D013813:Therapy, Computer-Assisted / Q000379:methods*; D013997:Time Factors
chemical_list: D015415:Biomarkers; D010100:Oxygen