title: Automated closed-loop resuscitation of multiple hemorrhages: a comparison between fuzzy logic and decision table controllers in a sheep model.
abstract: BACKGROUND
  Hemorrhagic shock is the leading cause of trauma-related death in the military setting. Definitive surgical treatment of a combat casualty can be delayed and life-saving fluid resuscitation might be necessary in the field. Therefore, improved resuscitation strategies are critically needed for prolonged field and en route care. We developed an automated closed-loop control system capable of titrating fluid infusion to a target endpoint. We used the system to compare the performance of a decision table algorithm (DT) and a fuzzy logic controller (FL) to rescue and maintain the mean arterial pressure (MAP) at a target level during hemorrhages. Fuzzy logic empowered the control algorithm to emulate human expertise. We hypothesized that the FL controller would be more effective and more efficient than the DT algorithm by responding in a more rigid, structured way.
  
  
  METHODS
  Ten conscious sheep were submitted to a hemorrhagic protocol of 25 ml/kg over three separate bleeds. Automated resuscitation with lactated Ringer's was initiated 30 min after the first hemorrhage started. The endpoint target was MAP. Group differences were assessed by two-tailedttest and alpha of 0.05.
  
  
  RESULTS
  Both groups maintained MAP at similar levels throughout the study. However, the DT group required significantly more fluid than the FL group, 1745 ± 552 ml (42 ± 11 ml/kg) versus 978 ± 397 ml (26 ± 11 ml/kg), respectively (p= 0.03).
  
  
  CONCLUSIONS
  The FL controller was more efficient than the DT algorithm and may provide a means to reduce fluid loading. Effectiveness was not different between the two strategies. Automated closed-loop resuscitation can restore and maintain blood pressure in a multi-hemorrhage model of shock.
authors.affiliation: University of Texas Medical Branch, 301 University Boulevard, Galveston, TX 775555 USA.
authors.forename: Nicole Ribeiro
authors.identifier: 0000-0001-8619-2354
authors.initials: NR
authors.lastname: Marques
authors.affiliation: University of Texas Medical Branch, 301 University Boulevard, Galveston, TX 775555 USA.
authors.forename: Brent J
authors.initials: BJ
authors.lastname: Ford
authors.affiliation: University of Texas Medical Branch, 301 University Boulevard, Galveston, TX 775555 USA.
authors.forename: Muzna N
authors.initials: MN
authors.lastname: Khan
authors.affiliation: University of Texas Medical Branch, 301 University Boulevard, Galveston, TX 775555 USA.
authors.forename: Michael
authors.initials: M
authors.lastname: Kinsky
authors.affiliation: University of Texas Medical Branch, 301 University Boulevard, Galveston, TX 775555 USA.
authors.forename: Donald J
authors.initials: DJ
authors.lastname: Deyo
authors.affiliation: University of Texas Medical Branch, 301 University Boulevard, Galveston, TX 775555 USA.
authors.forename: William J
authors.initials: WJ
authors.lastname: Mileski
authors.affiliation: Wayne State University, 45050 Anthony Wayne Drive, Detroit, MI 48202 USA.
authors.forename: Hao
authors.initials: H
authors.lastname: Ying
authors.affiliation: University of Texas Medical Branch, 301 University Boulevard, Galveston, TX 775555 USA.
authors.forename: George C
authors.initials: GC
authors.lastname: Kramer
keywords: Animal model; Decision table control; Fluid resuscitation; Fuzzy logic control; Hypotension; Traumatic brain injury