title: A Data Mining Approach to Determine Sepsis Guideline Impact on Inpatient Mortality and Complications.
abstract: Sepsis incidents have doubled from 2000 through 2008, and hospitalizations for these diagnoses have increased by 70%. The use of the Surviving Sepsis Campaign (SSC) guidelines can lead to earlier diagnosis and treatment; however, the effectiveness of the SSC guidelines in preventing complications for this population is unclear. The overall purpose of this study was to apply SSC guideline recommendations to EHR data for patients with severe sepsis or septic shock and determine guideline compliance as well as its impact on inpatient mortality and sepsis complications. Propensity Score Matching in conjuction with Bootstrap Simulation were used to match patients with and without exposure to the SSC recommendations. Findings showed that EHR data could be used to estimate compliance with SSC recommendations as well as the effect of compliance on outcomes. Compliance with guideline recommendations ranged from 9% to 100%. For individual recommendations with sufficient data, association with outcomes varied. Checking lactate influenced four outcomes; however, two were negative and two positive. Use of a ventilator for patients with respiratory distress had a positive association with three outcomes.
authors: School of Nursing, University of Minnesota, Minneapolis, MN;
authors: Lisiane
authors: L
authors: Pruinelli
authors: Department of Computer Science & Engineering, University of Minnesota, Minneapolis, MN;
authors: Pranjul
authors: P
authors: Yadav
authors: Department of Computer Science & Engineering, University of Minnesota, Minneapolis, MN;
authors: Andrew
authors: A
authors: Hangsleben
authors: Department of Computer Science & Engineering, University of Minnesota, Minneapolis, MN;
authors: Jakob
authors: J
authors: Johnson
authors: IBM T. J. Watson Research Center, White Plains, NY;
authors: Sanjoy
authors: S
authors: Dey
authors: Hennepin County Medical Center, Minneapolis, MN;
authors: Maribet
authors: M
authors: McCarty
authors: Department of Computer Science & Engineering, University of Minnesota, Minneapolis, MN;
authors: Vipin
authors: V
authors: Kumar
authors: School of Nursing, University of Minnesota, Minneapolis, MN;
authors: Connie W
authors: CW
authors: Delaney
authors: Department of Computer Science & Engineering, University of Minnesota, Minneapolis, MN;
authors: Michael
authors: M
authors: Steinbach
authors: School of Nursing, University of Minnesota, Minneapolis, MN;; Institute for Health Informatics, University of Minnesota, Minneapolis, MN;
authors: Bonnie L
authors: BL
authors: Westra
authors: Mayo Clinic, Rochester, MN.
authors: György J
authors: GJ
authors: Simon