title: Derivation and validation of a search algorithm to retrospectively identify mechanical ventilation initiation in the intensive care unit.
abstract: BACKGROUND
  The development and validation of automated electronic medical record (EMR) search strategies are important for establishing the timing of mechanical ventilation initiation in the intensive care unit (ICU).Thus, we sought to develop and validate an automated EMR search algorithm (strategy) for time zero, the moment of mechanical ventilation initiation in the critically ill patient.
  
  
  METHODS
  The EMR search algorithm was developed on the basis of several mechanical ventilation parameters, with the final parameter being positive end-expiratory pressure (PEEP), and was applied to a comprehensive institutional EMR database. The search algorithm was derived from a secondary retrospective analysis of a subset of 450 patients from a cohort of 2,684 patients admitted to a medical ICU and a surgical ICU from January 1, 2010, through December 31, 2011. It was then validated in an independent subset of 450 patients from the same period. The overall percent of agreement between our search algorithm and a comprehensive manual medical record review in the derivation and validation subsets, using peak inspiratory pressure (PIP) as the reference standard, was compared to assess timing of mechanical ventilation initiation.
  
  
  RESULTS
  In the derivation subset, the automated electronic search strategy achieved an 87% (κ = 0.87) perfect agreement, with 94% agreement to within one minute. In validating this search algorithm, perfect agreement was found in 92% (κ = 0.92) of patients, with 99% agreement occurring within one minute.
  
  
  CONCLUSIONS
  The use of an electronic search strategy resulted in highly accurate extraction of mechanical ventilation initiation in the ICU. The search algorithm of mechanical ventilation initiation is highly efficient and reliable and can facilitate both clinical research and patient care management in a timely manner.
authors: Department of Anesthesiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA. smischney.nathan@mayo.edu.
authors: Nathan J
authors: NJ
authors: Smischney
authors: Venu M
authors: VM
authors: Velagapudi
authors: James A
authors: JA
authors: Onigkeit
authors: Brian W
authors: BW
authors: Pickering
authors: Vitaly
authors: V
authors: Herasevich
authors: Rahul
authors: R
authors: Kashyap
mesh_terms: D000328:Adult; D000465:Algorithms; D057286:Electronic Health Records / Q000592:standards*; D005240:Feasibility Studies; D006801:Humans; D007362:Intensive Care Units / Q000592:standards*; D016032:Randomized Controlled Trials as Topic / Q000592:standards*; D012121:Respiration, Artificial / Q000379:methods / Q000592:standards*; D012189:Retrospective Studies; D013997:Time Factors