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. 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

Using a Clinical Surveillance System to Detect Drug‐Associated Hypoglycemia in Nursing Home Residents

“…Prior studies have found detection of hypoglycemia using glycemic control monitoring with computerized systems in hospital settings to be an efficient method of surveillance . To the best of the knowledge of the authors of the current study, this is the first study to prospectively use a computerized clinical surveillance system to detect and determine the incidence of drug‐associated hypoglycemia in NHs.…”

“…Prior studies have found detection of hypoglycemia using glycemic control monitoring with computerized systems in hospital settings to be an efficient method of surveillance. [9][10][11][12] To the best of the knowledge of the authors of the current study, this is the first study to prospectively use a computerized clinical surveillance system to detect and determine the incidence of drug-associated hypoglycemia in NHs. This study is responsive to the OIG's recommendations to raise awareness of ADEs in NH residents receiving postacute care by mirroring methods used to promote medication safety and adverse event detection in the hospitals.…”

Temporal and external validation of a prediction model for adverse outcomes among inpatients with diabetes

“…Only two other algorithms that aim to identify patients at risk of hypoglycaemia exist for inpatients with diabetes, but these were not externally validated [27,28]. Only two other algorithms that aim to identify patients at risk of hypoglycaemia exist for inpatients with diabetes, but these were not externally validated [27,28].…”

“…Overall, the results of the present analysis show that the model is both temporally and externally valid: it performed well in both more recent data from the original hospital (UHB) and in other hospitals with sociodemographically The strengths of the present study include the external validation across two distinctly different hospital settings, datasets with large numbers of patients and events, robust methodology including a number of sensitivity analyses, and being the first study to derive and validate a prediction model to identify adverse events in hospitalized patients with diabetes based on routinely collected data close to the time of admission. Only two other algorithms that aim to identify patients at risk of hypoglycaemia exist for inpatients with diabetes, but these were not externally validated [27,28]. Validation of the model is, however, limited to hospitals in the UK, and data from Ipswich Hospital had limitations that prevented us from optimally demonstrating the performance in a district general hospital setting.…”

Multifaceted approach to reducing occurrence of severe hypoglycemia in a large healthcare system

“…The alert was pilot tested at select nursing stations at BJC's adult academic hospital in 2011, with a subsequent 68% reduction of severe hypoglycemia in high-risk patients that year. 13 A further reduction of 19% was seen in 2012, when the alert system was implemented on all acute and progressive-care nursing units. 14 With the approval of medical and administrative leaders at each BJC hospital, diabetes educators, nurses, and pharmacists were trained to respond to this alert at all remaining BJC hospitals.…”