OBJECTIVE To investigate if early electronic identification and bedside management of inpatients with diabetes improves glycemic control in noncritical care. RESEARCH DESIGN AND METHODS We investigated a proactive or early intervention model of care (whereby an inpatient diabetes team electronically identified individuals with diabetes and aimed to provide bedside management within 24 h of admission) compared with usual care (a referral-based consultation service). We conducted a cluster randomized trial on eight wards, consisting of a 10-week baseline period (all clusters received usual care) followed by a 12-week active period (clusters randomized to early intervention or usual care). Outcomes were adverse glycemic days (AGDs) (patient-days with glucose <4 or >15 mmol/L [<72 or >270 mg/dL]) and adverse patient outcomes. RESULTS We included 1,002 consecutive adult inpatients with diabetes or new hyperglycemia. More patients received specialist diabetes management (92% vs. 15%, P < 0.001) and new insulin treatment (57% vs. 34%, P = 0.001) with early intervention. At the cluster level, incidence of AGDs decreased by 24% from 243 to 186 per 1,000 patient-days in the intervention arm (P < 0.001), with no change in the control arm. At the individual level, adjusted number of AGDs per person decreased from a mean 1.4 (SD 1.6) to 1.0 (0.9) days (−28% change [95% CI −45 to −11], P = 0.001) in the intervention arm but did not change in the control arm (1.8 [2.0] to 1.5 [1.8], −9% change [−25 to 6], P = 0.23). Early intervention reduced overt hyperglycemia (55% decrease in patient-days with mean glucose >15 mmol/L, P < 0.001) and hospital-acquired infections (odds ratio 0.20 [95% CI 0.07–0.58], P = 0.003). CONCLUSIONS Early identification and management of inpatients with diabetes decreased hyperglycemia and hospital-acquired infections.

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 patientday); 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 patientdays. 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,] 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.The known: Despite the importance of glucose control for people admitted to hospital, inpatient glucose levels have not been systematically audited or benchmarked in Australia. The new:We report the first detailed glucometric analysis for inpatients in a major Australian hospital, an analysis facilitated by networked glucose meter technology. For 260 of every 1000 patient-days, blood glucose levels were outside the safe range for hospital patients. The incidence of hyperglycaemia was higher and that of hypoglycaemia lower than in an American hospital benchmark.

Use of a novel glucose alert system improved health professional responses to adverse glycaemia and decreased hyperglycaemia in the hospital setting.

Despite the high incidence of hyperglycemia in hospitalized individuals with diabetes, clinical tools to predict those at risk for hyperglycemia are lacking. We developed a clinical prediction model that identifies individuals who developed persistent hyperglycemia and/or hypoglycemia during hospitalization. This clinical prediction tool allows early identification of high-risk patients with diabetes, and can assist targeted management by inpatient diabetes teams.

We previously showed that a proactive inpatient diabetes service decreased adverse glycemia (AG) and hospital acquired infections (RAPIDS: ADA2017, 231-OR). To further focus our proactive care on high-risk inpatients, we investigated clinical risk factors associated with AG. We analyzed multiple clinical variables in 643 consecutive inpatients with diabetes or new hyperglycemia (random BG ≥11.1mmol/L). Capillary BG from day 2 of admission until discharge (censored at day 14) were analyzed. AG was defined as BG <4 or >15mmol/L on any day and recurrent AG (RAG) was defined as AG on ≥2 days. A split-sample multivariable logistical regression was performed with internal validation. The patient characteristics included 87% type 2 diabetes, 33% insulin-treated and mean HbA1c 7.6%. AG and RAG occurred in 278 (43%), and 176 (27%) patients respectively. Pre-hospital factors (sulphonylurea or insulin treatment, HbA1c, Charlson index) and in-hospital factors (dysglycemia on day 1, length of stay) were independently associated with both AG and RAG (Table). Glucocorticoid treatment was associated with RAG but not AG. A model using these multiple variables accurately identified AG (ROC-AUC 0.88). Age, diabetes type, creatinine and admission unit were not associated with either AG or RAG. This study identified multiple key clinical risk factors associated with adverse glycemia, and may be used to better concentrate efforts for inpatient diabetes care. Disclosure M. Kyi: None. J.E. Reid: None. A. Gorelik: None. S.S. Kumar: None. A. Galligan: None. L.M. Rowan: Speaker's Bureau; Self; AstraZeneca, Sanofi, Novo Nordisk Inc.. A.J. Nankervis: None. K.A. Marley: None. D.M. Russell: None. P.R. Wraight: None. P.G. Colman: None. S. Fourlanos: None.

Background/Aims Diabetes mellitus is increasingly prevalent among hospital inpatients. Management requires regular blood glucose monitoring by nurses, yet research into nurse perceptions of glucose management importance is lacking. Methods A 5-point Likert-scale survey was administered to 718 nurses at an Australian tertiary centre. Nurses were predominantly from acute medical wards (57%) and in the first decade of their career (66%). Results The six tested aspects of glucose monitoring were perceived as important by the majority, but the importance of timely management of abnormal glucose was rated lower by clinical nurse educators (4.33 vs 4.70, P=0.019) and by nurses with 5 or more years of experience compared with first-year nurses. Both predictors remained significant following multivariable adjustment (educator status odds ratio 0.51, P=0.043, years of nursing experience odds ratio 0.84, P=0.018). Conclusions These findings imply that concurrent nurse (re-)education in glucose management should be considered in the design and implementation of future glucose management programmes.

Adverse glycaemia in hospital is associated with worse clinical outcomes. Given the high prevalence of diabetes in the cardiology wards, we introduced an early intervention model of diabetes care in high-risk inpatients aiming to decrease adverse glycaemia. A prospective pre- and post-intervention study was conducted in a tertiary referral hospital cardiology ward. It consisted of an initial 6-month baseline phase of standard care (a ‘reactive’ referral-based diabetes consultation service), followed by an 9-month intervention phase, comprising a ‘proactive’ multidisciplinary inpatient diabetes team (IDT) assisted by networked blood glucose (BG) meter technology. The proactive IDT performed electronic surveillance of capillary BG measurements and provided an early consultation service (within 24 hours of admission), without waiting for referral from the parent team. Consecutive inpatients admitted to the cardiology ward (length of stay >48 hours) at high risk of adverse glycaemia (insulin treatment prior to hospital; or BG <4, >15 or two BG >10 mmol/L in the first 48 hours) were recruited. BG data from day 2 until discharge, along with demographic and clinical features, was collected. The primary outcome was the incidence of adverse glycaemic days (AGD) (rate per 1000 patient-days with any capillary BG measure <4 or >15 mmol/L). Overall, we observed 332 patients (1320patient-days and 4887 BG measurements). Compared to usual care, the incidence of AGD decreased from 405 to 320 per 1000 patient-days (21% decrease, p=0.002) which remained significant after MVA adjustment. During the intervention phase, there was decreased mean (±SD) patient-day BG (10.8 ±3.3 vs. 10.1 ±2.8, p<0.001), decreased patient-days with hyperglycaemia (BG >15 mmol/L) (10.4% vs. 6.1%, p=0.044) with no significant difference in patient-days with hypoglycaemia. Analysis of clinical outcomes is pending. An early intervention model of diabetes care decreased adverse glycaemia in cardiology in patients with diabetes. Disclosure S. Fourlanos: None. N.D. Mingos: None. L.M. Rowan: None. R. Barmanray: None. M. Kyi: None.

Aims: To assess the effect of early intervention with electronic-based proactive specialist diabetes care in surgical inpatients on glycaemia and clinical outcomes. Methods: The Specialist Treatment of Inpatients: Caring for Diabetes (STOIC-D) Surgery randomised controlled trial (RCT) recruited consecutive adults admitted to surgical units of the Royal Melbourne Hospital (Australia) in 2021 with diabetes or blood glucose ≥200 mg/dL and length of stay (LOS) ≥24 hours. Intervention arm patients received remote proactive consultation by the inpatient diabetes service (IDS) in the electronic medical record (Epic®) within 24 hours of admission and, if escalation criteria were met, received a bedside consultation. Patients receiving standard care were reviewed by the IDS at the bedside only following referral. Insulin and non-insulin agents were used to target glucose 90-180 mg/dL. Outcomes included glucometrics, healthcare-associated infection (HAI), and mortality. Registration: ACTRN12620001303932. Results: 1,383 admissions met inclusion criteria; 689 received the intervention. The primary outcome of mean patient-day mean glucose was lower in the active (158.4 mg/dL, standard deviation [SD] 48.6) vs. control arm (162.0 mg/dL, SD 46.8, p<0.001). HAI (most commonly pneumonia) was lower in the active vs. control arm (11% vs. 16%, p=0.02). Mortality (2.4% vs. 4.2%, p=0.08) and LOS (10.7 vs. 10.0 days, p=0.26) were no different. The number needed to treat for HAI prevention was 22. Hypoglycaemia <72 mg/dL was not increased (1.0% active vs. 0.9% control, p=0.23). The IDS performed a bedside consultation in 333 (49%) of the active vs. 93 (14%) of the control arm. Conclusion: The STOIC-D Surgery trial is the largest RCT of a diabetes model-of-care intervention in non-critical care. Early, electronic-based specialist diabetes intervention significantly reduced patient-day mean glucose and HAIs in a surgical population. Disclosure R.Barmanray: None. L.J.Worth: None. S.Fourlanos: Advisory Panel; Viatris Inc., Pfizer Inc., Speaker's Bureau; Novo Nordisk, AstraZeneca, Boehringer Ingelheim and Eli Lilly Alliance. M.Kyi: None. P.G.Colman: None. L.M.Rowan: None. L.Collins: None. L.E.Donaldson: Stock/Shareholder; Medtronic, Novo Nordisk. S.Montalto: None. E.Sun: None. M.V.H.Le: None.