Prevention of inpatient hypoglycemia with a real-time informatics alert

• Published in: Journal of hospital medicine Vol. 9; no. 10; pp. 621 - 626
• Main Authors: Kilpatrick, C. Rachel, Elliott, Michael B., Pratt, Elizabeth, Schafers, Stephen J., Blackburn, Mary Clare, Heard, Kevin, McGill, Janet B., Thoelke, Mark, Tobin, Garry S.
• Format: Journal Article
• Language: English
• Published: United States Blackwell Publishing Ltd 01-10-2014; Frontline Medical Communications
• Subjects: Aged; Algorithms; Blood Glucose - analysis; Body Weight; Creatinine - blood; Female; Humans; Hypoglycemia - prevention & control; Incidence; Inservice Training - organization & administration; Insulin - metabolism; Male; Middle Aged; Missouri; Nursing Staff, Hospital - organization & administration; Personnel, Hospital; Prospective Studies; Risk Assessment; Sensitivity and Specificity; Missouri
• ISSN: 1553-5592; 1553-5606
• DOI: 10.1002/jhm.2221

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.
DESIGN
A 5‐month prospective cohort intervention study.
SETTING
Acute care medical floors in a tertiary care academic hospital in St. Louis, Missouri.
PATIENTS
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.
MEASUREMENTS
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.
CONCLUSION
A real‐time predictive informatics‐generated alert, when supported by trained nurse responders, significantly reduced inpatient SH. Journal of Hospital Medicine 2014;9:621–626. © 2014 Society of Hospital Medicine