The Specialist Treatment of Inpatients: Caring for Diabetes in Surgery (STOIC-D Surgery) Trial: A Randomized Controlled Trial of Early Intervention With an Electronic Specialist-Led Model of Diabetes Care.

OBJECTIVE: To investigate the effect of early intervention with an electronic specialist-led "proactive" model of care on glycemic and clinical outcomes. RESEARCH DESIGN AND METHODS: The Specialist Treatment of Inpatients: Caring for Diabetes in Surgery (STOIC-D Surgery) randomized controlled trial was performed at the Royal Melbourne Hospital. Eligible participants were adults admitted to a surgical ward during the study with either known diabetes or newly detected hyperglycemia (at least one random blood glucose result ≥11.1 mmol/L). Participants were randomized 1:1 to standard diabetes care or the intervention consisting of an early consult by a specialist inpatient diabetes team using electronic tools for patient identification, communication of recommendations, and therapy intensification. The primary outcome was median patient-day mean glucose (PDMG). The key secondary outcome was incidence of health care-associated infection (HAI). RESULTS: Between 12 February 2021 and 17 December 2021, 1,371 admissions met inclusion criteria, with 680 assigned to early intervention and 691 to standard diabetes care. Baseline characteristics were similar between groups. The early intervention group achieved a lower median PDMG of 8.2 mmol/L (interquartile range [IQR] 6.9-10.0 mmol/L) compared with 8.6 mmol/L (IQR 7.2-10.3 mmol/L) in the control group for an estimated difference of -0.3 mmol/L (95% CI -0.4 to -0.2 mmol/L, P < 0.0001). The incidence of HAI was lower in the intervention group (77 [11%] vs. 110 [16%]), for an absolute risk difference of -4.6% (95% CI -8.2 to -1.0, P = 0.016). CONCLUSIONS: In surgical inpatients, early diabetes management intervention with an electronic specialist-led diabetes model of care reduces glucose and HAI.

Clinical Prediction Tool To Identify Adults With Type 2 Diabetes at Risk for Persistent Adverse Glycemia in Hospital.

OBJECTIVES: Given the high incidence of hyperglycemia and hypoglycemia in hospital and the lack of prediction tools for this problem, we developed a clinical tool to assist early identification of individuals at risk for persistent adverse glycemia (AG) in hospital. METHODS: We analyzed a cohort of 594 consecutive adult inpatients with type 2 diabetes. We identified clinical factors available early in the admission course that were associated with persistent AG (defined as ≥2 days with capillary glucose <4 or >15 mmol/L during admission). A prediction model for persistent AG was constructed using logistic regression and internal validation was performed using a split-sample approach. RESULTS: Persistent AG occurred in 153 (26%) of inpatients, and was associated with admission dysglycemia (odds ratio [OR], 3.65), glycated hemoglobin ≥8.1% (OR, 5.08), glucose-lowering treatment regimen containing sulfonylurea (OR, 3.50) or insulin (OR, 4.22), glucocorticoid medication treatment (OR, 2.27), Charlson Comorbidity Index score and the number of observed days. An early-identification prediction tool, based on clinical factors reliably available at admission (admission dysglycemia, glycated hemoglobin, glucose-lowering regimen and glucocorticoid treatment), could accurately predict persistent AG (receiver-operating characteristic area under curve = 0.806), and, at the optimal cutoff, the sensitivity, specificity and positive predictive value were 84%, 66% and 53%, respectively. CONCLUSIONS: A clinical prediction tool based on clinical risk factors available at admission to hospital identified patients at increased risk for persistent AG and could assist early targeted management by inpatient diabetes teams.

Glucometric benchmarking in an Australian hospital enabled by networked glucose meter technology.

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.

Early Intervention for Diabetes in Medical and Surgical Inpatients Decreases Hyperglycemia and Hospital-Acquired Infections: A Cluster Randomized Trial.

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.