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  • Title: Glucometric benchmarking in an Australian hospital enabled by networked glucose meter technology.
    Author: Kyi M, Colman PG, Rowan LM, Marley KA, Wraight PR, Fourlanos S.
    Journal: Med J Aust; 2019 Aug; 211(4):175-180. PubMed ID: 31231826.
    Abstract:
    OBJECTIVE: To assess glucometric outcomes and to estimate the incidence of hypo- and hyperglycaemia among non-critical care inpatients in a major Australian hospital. DESIGN, SETTING AND PARTICIPANTS: A prospective 10-week observational study (7 March - 22 May 2016) of consecutive inpatients with diabetes or newly detected hyperglycaemia admitted to eight medical and surgical wards at the Royal Melbourne Hospital. Point-of-care blood glucose (BG) data were collected with networked glucose meters. MAIN OUTCOME MEASURES: Glycaemic control, as assessed with three glucometric models (by population, by patient, by patient-day); incidence of adverse glycaemic days (AGDs; patient-days with BG levels below 4 mmol/L or above 15 mmol/L). RESULTS: During the study period, there were 465 consecutive admissions of 441 patients with diabetes or newly detected hyperglycaemia, and 9817 BG measurements over 2953 patient-days. The mean patient-day BG level was 9.5 mmol/L (SD, 3.3 mmol/L). The incidence of hyperglycaemia was higher than for a United States hospital benchmark (patient-days with mean BG level above 10 mmol/L, 37% v 32), and that of hypoglycaemia lower (proportion of patient-days with mean BG level below 3.9 mmol/L, 4.1% v 6.1%). There were 260 (95% CI, 245-277) AGDs per 1000 patient-days; the incidence was higher in medical than surgical ward patients (290 [CI, 270-310] v 206 [CI, 181-230] per 1000 patient-days). 604 AGDs (79%) were linked with 116 patients (25%). Episodes of hyperglycaemia (BG above 15 mmol/L) were more frequent before lunch, dinner, and bedtime; 94 of 187 episodes of hypoglycaemia (BG below 4 mmol/L) occurred between 11 pm and 8 am. DISCUSSION: Glucometric analysis supported by networked glucose meter technology provides detailed inpatient data that could enable local benchmarking for promoting safe diabetes care in Australian hospitals.
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