title: Using principal component analysis to aid bayesian network development for prediction of critical care patient outcomes.
abstract: BACKGROUND
  Predicting an intensive care unit patient's outcome is highly desirable. An end goal is for computational techniques to provide updated, accurate predictions about changing patient condition using a manageable number of physiologic parameters.
  
  
  METHODS
  Principal component analysis was used to select input parameters for critical care patient outcome models. Vital signs and laboratory values from each patient's hospital stay along with outcomes ("Discharged" vs. "Deceased") were collected retrospectively at a Level I Trauma-Military Medical Center in the southwest; intensive care unit patients were included if they had been admitted for burn, infection, or hypovolemia during a 5-year period ending October 2007. Principal component analysis was used to determine which of the 24 parameters would serve as inputs in a bayesian network developed for outcome prediction.
  
  
  RESULTS
  Data for 581 patients were collected. Pulse pressure, heart rate, temperature, respiratory rate, sodium, and chloride were found to have statistically significant differences between Discharged and Deceased groups for "Hypovolemia" patients. For "Burn" patients, pulse pressure, hemoglobin, hematocrit, and potassium were found to have statistically significant differences. For a "Combined" group, heart rate, temperature, respiratory rate, sodium, and chloride had statistically significant differences. A bayesian network based on these results, developed for the Combined group, achieved an accuracy of 75% when predicting patient outcome.
  
  
  CONCLUSIONS
  Outcome prediction for critical care patients is possible. Future work should explore model development using additional temporal data and should include prospective validation. Such technology could serve as the basis of real-time intelligent monitoring systems for critical patients.
authors.affiliation: AFrame Digital, Inc., Reston, Virginia, USA. ccrump@aframedigital.com
authors.forename: Cindy
authors.initials: C
authors.lastname: Crump
authors.forename: Christine Tsien
authors.initials: CT
authors.lastname: Silvers
authors.forename: Bruce
authors.initials: B
authors.lastname: Wilson
authors.forename: Loretta
authors.initials: L
authors.lastname: Schlachta-Fairchild
authors.forename: Colleen A
authors.initials: CA
authors.lastname: Lingley-Papadopoulos
authors.forename: Jeffrey S
authors.initials: JS
authors.lastname: Ashley
mesh_terms: D000328:Adult; D001499:Bayes Theorem; D002423:Cause of Death; D003422:Critical Care / Q000379:methods*; D016638:Critical Illness / Q000401:mortality* / Q000628:therapy; D005260:Female; D017052:Hospital Mortality; D006772:Hospitals, Military; D006801:Humans; D007362:Intensive Care Units; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D010351:Patient Discharge / Q000706:statistics & numerical data; D011237:Predictive Value of Tests; D025341:Principal Component Analysis; D018570:Risk Assessment; D016019:Survival Analysis; D014193:Trauma Centers; D016896:Treatment Outcome; D014947:Wounds and Injuries / Q000175:diagnosis / Q000401:mortality* / Q000628:therapy; D055815:Young Adult