title: Prevention of inpatient hypoglycemia with a real-time informatics alert.
abstract: BACKGROUND
  Severe hypoglycemia (SH), defined as a blood glucose (BG) <40 mg/dL, is associated with an increased risk of adverse clinical outcomes in inpatients.
  
  
  OBJECTIVE
  To determine whether a predictive informatics hypoglycemia risk-alert supported by trained nurse responders would reduce the incidence of SH in our hospital.
  
  
  METHODS
  A 5-month prospective cohort intervention study.
  
  
  METHODS
  Acute care medical floors in a tertiary care academic hospital in St. Louis, Missouri.
  
  
  METHODS
  From 655 inpatients on designated medical floors with a BG of <90 mg/dL, 390 were identified as high risk for hypoglycemia by the alert system.
  
  
  METHODS
  The primary outcome was the incidence of SH occurring in high-risk intervention versus high-risk control patients. Secondary outcomes included: number of episodes of SH in all study patients, incidence of BG < 60 mg/dL and severe hyperglycemia with a BG >299 mg/dL, length of stay, transfer to a higher level of care, the frequency that high-risk patient's orders were changed in response to the alert-intervention process, and mortality.
  
  
  RESULTS
  The alert process, when augmented by nurse-physician collaboration, resulted in a significant decrease by 68% in the rate of SH in alerted high-risk patients versus nonalerted high-risk patients (3.1% vs 9.7%, P = 0.012). Rates of hyperglycemia were similar on intervention and control floors at 28% each. There was no difference in mortality, length of stay, or patients requiring transfer to a higher level of care.
  
  
  CONCLUSIONS
  A real-time predictive informatics-generated alert, when supported by trained nurse responders, significantly reduced inpatient SH.
authors.affiliation: Department of Endocrinology, Metabolism, and Lipid Research, Washington University School of Medicine, St. Louis, Missouri.
authors.forename: C Rachel
authors.initials: CR
authors.lastname: Kilpatrick
authors.forename: Michael B
authors.initials: MB
authors.lastname: Elliott
authors.forename: Elizabeth
authors.initials: E
authors.lastname: Pratt
authors.forename: Stephen J
authors.initials: SJ
authors.lastname: Schafers
authors.forename: Mary Clare
authors.initials: MC
authors.lastname: Blackburn
authors.forename: Kevin
authors.initials: K
authors.lastname: Heard
authors.forename: Janet B
authors.initials: JB
authors.lastname: McGill
authors.forename: Mark
authors.initials: M
authors.lastname: Thoelke
authors.forename: Garry S
authors.initials: GS
authors.lastname: Tobin
mesh_terms: D000368:Aged; D000465:Algorithms; D001786:Blood Glucose / Q000032:analysis; D001835:Body Weight; D003404:Creatinine / Q000097:blood; D005260:Female; D006801:Humans; D007003:Hypoglycemia / Q000517:prevention & control*; D015994:Incidence; D007318:Inservice Training / Q000458:organization & administration; D007328:Insulin / Q000378:metabolism; D008297:Male; D008875:Middle Aged; D008923:Missouri; D009741:Nursing Staff, Hospital / Q000458:organization & administration*; D010564:Personnel, Hospital; D011446:Prospective Studies; D018570:Risk Assessment; D012680:Sensitivity and Specificity
chemical_list: D001786:Blood Glucose; D007328:Insulin; D003404:Creatinine