title: The use of an electronic medical record based automatic calculation tool to quantify risk of unplanned readmission to the intensive care unit: a validation study.
abstract: OBJECTIVE
  The aim of this study was to refine and validate an automatic risk of unplanned readmission (Stability and Workload Index for Transfer, or SWIFT) calculator in a prospective cohort of consecutive medical intensive care unit (ICU) patients in a teaching hospital with comprehensive electronic medical records (EMRs).
  
  
  METHODS
  A 2-phase (derivation and validation) prospective cohort study was conducted.
  
  
  METHODS
  The study was conducted in an academic medical ICU.
  
  
  METHODS
  A consecutive cohort of adult (age >18 years) patients with research authorization were analyzed.
  
  
  METHODS
  The EMR-based automatic SWIFT calculator was used for this study.
  
  
  METHODS
  Agreement between the manual ("gold standard") and automatic SWIFT calculation tool was obtained.
  
  
  RESULTS
  During the derivation phase, we enrolled 191 consecutive medical ICU patients. Scores of SWIFT for these patients calculated manually by the 2 reviewers had strong positive correlation (r = 0.97), and the mean (SD) difference was 0.43 (3.5). The first iteration of the automatic SWIFT calculator in the derivation cohort demonstrated excellent agreement with manual calculation, partial pressure of carbon dioxide in arterial blood (κ = 0.95), partial pressure of oxygen in arterial blood/fraction of inspired oxygen ratio (κ = 0.69), length of ICU stay (κ = 0.91), and Glasgow comma scale (κ = 0.90) and no agreement for source of ICU admission (κ = -0.15). After adjustment in rules, the κ value for hospital admission source improved to 1.0. Automatic calculation demonstrated strong correlation with manual (r = 0.92), and mean (SD) difference was -2.3 (5.9). During validation phase, 100 subjects were enrolled at 5 days. The automatic tool retained excellent correlation with gold-standard calculation for SWIFT (r = 0.92), and the mean (SD) difference was -2.2 (5.5).
  
  
  CONCLUSIONS
  The EMR-based automatic tool accurately calculates SWIFT score and can facilitate ICU discharge decisions without the need for manual data collection.
authors.affiliation: Department of Emergency Medicine, Mayo Clinic, Rochester, MN 55905, USA.
authors.forename: Subhash
authors.initials: S
authors.lastname: Chandra
authors.forename: Dipti
authors.initials: D
authors.lastname: Agarwal
authors.forename: Andrew
authors.initials: A
authors.lastname: Hanson
authors.forename: Joseph C
authors.initials: JC
authors.lastname: Farmer
authors.forename: Brian W
authors.initials: BW
authors.lastname: Pickering
authors.forename: Ognjen
authors.initials: O
authors.lastname: Gajic
authors.forename: Vitaly
authors.initials: V
authors.lastname: Herasevich
mesh_terms: D000368:Aged; D015331:Cohort Studies; D003422:Critical Care; D016638:Critical Illness / Q000453:epidemiology*; D057286:Electronic Health Records / Q000592:standards*; D005260:Female; D006784:Hospitals, Teaching; D006801:Humans; D007362:Intensive Care Units; D008297:Male; D008875:Middle Aged; D008910:Minnesota; D010359:Patient Readmission / Q000706:statistics & numerical data*; D011446:Prospective Studies; D015203:Reproducibility of Results; D012306:Risk; D012720:Severity of Illness Index