title: Disease-based modeling to predict fluid response in intensive care units.
abstract: OBJECTIVE
  To compare general and disease-based modeling for fluid resuscitation and vasopressor use in intensive care units.
  
  
  METHODS
  Retrospective cohort study involving 2944 adult medical and surgical intensive care unit (ICU) patients receiving fluid resuscitation. Within this cohort there were two disease-based groups, 802 patients with a diagnosis of pneumonia, and 143 patients with a diagnosis of pancreatitis. Fluid resuscitation either progressing to subsequent vasopressor administration or not was used as the primary outcome variable to compare general and disease-based modeling.
  
  
  RESULTS
  Patients with pancreatitis, pneumonia and the general group all shared three common predictive features as core variables, arterial base excess, lactic acid and platelets. Patients with pneumonia also had non-invasive systolic blood pressure and white blood cells added to the core model, and pancreatitis patients additionally had temperature. Disease-based models had significantly higher values of AUC (p < 0.05) than the general group (0.82 ± 0.02 for pneumonia and 0.83 ± 0.03 for pancreatitis vs. 0.79 ± 0.02 for general patients).
  
  
  CONCLUSIONS
  Disease-based predictive modeling reveals a different set of predictive variables compared to general modeling and improved performance. Our findings add support to the growing body of evidence advantaging disease specific predictive modeling.
authors.affiliation: André S. Fialho, PhD, Massachusetts Institute of Technology, Engineering Systems Division, 77 Massachusetts Avenue, 02139 Cambridge, MA, USA, E-mail: afialho@mit.edu.
authors.forename: A S
authors.initials: AS
authors.lastname: Fialho
authors.forename: L A
authors.initials: LA
authors.lastname: Celi
authors.forename: F
authors.initials: F
authors.lastname: Cismondi
authors.forename: S M
authors.initials: SM
authors.lastname: Vieira
authors.forename: S R
authors.initials: SR
authors.lastname: Reti
authors.forename: J M C
authors.initials: JM
authors.lastname: Sousa
authors.forename: S N
authors.initials: SN
authors.lastname: Finkelstein
mesh_terms: D000137:Acid-Base Imbalance / Q000503:physiopathology / Q000628:therapy; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001794:Blood Pressure / Q000502:physiology; D015331:Cohort Studies; D003198:Computer Simulation; D020000:Decision Support Systems, Clinical; D003661:Decision Support Techniques; D005260:Female; D005440:Fluid Therapy / Q000379:methods*; D017052:Hospital Mortality; D006801:Humans; D007362:Intensive Care Units; D019344:Lactic Acid / Q000097:blood; D007958:Leukocyte Count; D008297:Male; D008875:Middle Aged; D008991:Monitoring, Physiologic; D010195:Pancreatitis / Q000401:mortality / Q000503:physiopathology / Q000628:therapy*; D010976:Platelet Count; D011014:Pneumonia / Q000401:mortality / Q000503:physiopathology / Q000628:therapy*; D012189:Retrospective Studies; D014662:Vasoconstrictor Agents / Q000627:therapeutic use
chemical_list: D014662:Vasoconstrictor Agents; D019344:Lactic Acid
keywords: Disease-based modeling; decision modeling; fluid resuscitation; intensive care units