Glucose alert system improves health professional responses to adverse glycaemia and reduces the number of hyperglycaemic episodes in non-critical care inpatients

Longitudinal Digital Glucometric Benchmarking to Evaluate the Impact of Institutional Diabetes Care Initiatives in Adults With Diabetes Mellitus Over the 2016-2020 Period

BACKGROUND

While glucometric benchmarking has been used to compare glucose management between institutions, the value of longitudinal intra-institution benchmarking to assess quality improvement changes is not established.

METHODS

A prospective six-month observational study (October 2019-March 2020 inclusive) of inpatients with diabetes or newly detected hyperglycemia admitted to eight medical and surgical wards at the Royal Melbourne Hospital. Networked blood glucose (BG) meters were used to collect capillary BG levels. Outcomes were measures of glycemic control assessed by mean and threshold glucometric measures and comparison with published glucometric benchmarks. Intra-institution comparison was over the 2016-2020 period.

RESULTS

In all, 620 admissions (588 unique individuals) met the inclusion criteria, contributing 15 164 BG results over 4023 admission-days. Compared with the 2016 cohort from the same institution, there was increased BG testing (3.8 [SD = 2.2) vs 3.3 [SD = 1.7] BG measurements per patient-day, P < .001), lower mean patient-day mean glucose (PDMG; 8.9 mmol/L [SD = 3.2] vs 9.5 mmol/L [SD = 3.3], P < .001), and reduced mean and threshold measures of hyperglycemia (P < .001 for all). Comparison with institutions across the United States revealed lower incidence of mean PDMG >13.9 or >16.7 mmol/L, and reduced hypoglycemia (<3.9, <2.8, and <2.2 mmol/L), when compared with published benchmarks from an earlier period (2009-2014).

CONCLUSIONS

Comprehensive digital-based glucometric benchmarking confirmed institutional quality improvement changes were followed by reduced hyperglycemia and hypoglycemia in a five-year comparison. Longitudinal glucometric benchmarking enables evaluation and validation of changes to institutional diabetes care management practices.

Clinical Decision Support for Glycemic Management Reduces Hospital Length of Stay

OBJECTIVE

Dysglycemia influences hospital outcomes and resource utilization. Clinical decision support (CDS) holds promise for optimizing care by overcoming management barriers. This study assessed the impact on hospital length of stay (LOS) of an alert-based CDS tool in the electronic medical record that detected dysglycemia or inappropriate insulin use, coined as gaps in care (GIC).

RESEARCH DESIGN AND METHODS

Using a 12-month interrupted time series among hospitalized persons aged ≥18 years, our CDS tool identified GIC and, when active, provided recommendations. We compared LOS during 6-month-long active and inactive periods using linear models for repeated measures, multiple comparison adjustment, and mediation analysis.

RESULTS

Among 4,788 admissions with GIC, average LOS was shorter during the tool's active periods. LOS reductions occurred for all admissions with GIC (-5.7 h, P = 0.057), diabetes and hyperglycemia (-6.4 h, P = 0.054), stress hyperglycemia (-31.0 h, P = 0.054), patients admitted to medical services (-8.4 h, P = 0.039), and recurrent hypoglycemia (-29.1 h, P = 0.074). Subgroup analysis showed significantly shorter LOS in recurrent hypoglycemia with three events (-82.3 h, P = 0.006) and nonsignificant in two (-5.2 h, P = 0.655) and four or more (-14.8 h, P = 0.746). Among 22,395 admissions with GIC (4,788, 21%) and without GIC (17,607, 79%), LOS reduction during the active period was 1.8 h (P = 0.053). When recommendations were provided, the active tool indirectly and significantly contributed to shortening LOS through its influence on GIC events during admissions with at least one GIC (P = 0.027), diabetes and hyperglycemia (P = 0.028), and medical services (P = 0.019).

CONCLUSIONS

Use of the alert-based CDS tool to address inpatient management of dysglycemia contributed to reducing LOS, which may reduce costs and improve patient well-being.

Investigating the effect of an education program on diabetes and lipid lowering medication usage following coronary artery bypass graft surgery

BACKGROUND

Guidelines advocate multifactorial cardiovascular risk management in patients with diabetes and atherosclerotic cardiovascular disease. In hospitalised patients with diabetes following coronary artery bypass graft (CABG) we evaluated the impacts of decision-support algorithms for optimising glycaemia and lipid-lowering. We also assessed the safety of initiating sodium-glucose cotransporter 2 (SGLT2) inhibitors near time of hospital discharge.

METHODS

This was a single-site, pre- and post-intervention analysis of glucose and lipid management in consecutive hospitalised patients with diabetes undergoing CABG surgery. The intervention involved education and decision-support algorithms designed by a multidisciplinary committee to guide cardiac surgery unit clinicians.

RESULTS

A total of 200 patients were included in the study. The pre- and post-intervention groups had similar baseline characteristics (HbA1c 7.9 ± 1.9% versus 8.1 ± 1.8%). Of 4092 blood glucose measurements the incidence of levels between 5 to 10 mmol/L was not different post-intervention (55.5% versus 57.0%, p = 0.441). Fewer endocrinology consultations occurred (59.0% versus 45.0%, p = 0.048) and rates of hypoglycaemia remained low. High-intensity statin was prescribed in >90% pre- and post-intervention although non-statin lipid-lowering agents remained <10% despite patients not achieving LDL-C targets. No 30-day readmissions for diabetic ketoacidosis occurred in patients prescribed SGLT2 inhibitors.

CONCLUSION

The intervention did not improve inpatient glycaemia or increase non-statin lipid-lowering prescriptions in patients with diabetes following CABG surgery but did reduce reliance on specialty input. Initiation of SGLT2 inhibitor therapy near time of hospital discharge was not associated with safety concerns. Alternative interventions or strategies are required to optimise glycaemia and non-statin lipid-lowering therapy prescribing in this setting. This article is protected by copyright. All rights reserved.

Managing inpatient hyperglycaemia and initiating sodium-glucose cotransporter 2 inhibitor therapy in the setting of diabetes and acute coronary syndrome

We previously showed that implementing algorithms for managing diabetes in acute coronary syndrome was associated with improved inpatient glycaemic control and increased sodium-glucose cotransporter 2 (SGLT2) inhibitor prescriptions. The present study performed 1 year later found that inpatient hyperglycaemia had relapsed to pre-intervention rates, although SGLT2 inhibitor prescriptions remained increased. We discuss the challenges of improving inpatient glycaemic control.

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.

Implementing simple algorithms to improve glucose and lipid management in people with diabetes and acute coronary syndrome

AIM

Diabetes mellitus is associated with increased risk of adverse outcomes following acute coronary syndrome. Translating evidence-based recommendations into practice is necessary to improve outcomes. We evaluated whether implementing algorithms to guide inpatient care improved glycaemic control, and increased use of sodium-glucose co-transporter 2 (SGLT2) inhibitors and lipid-lowering medication in a tertiary cardiac unit.

METHOD

A 3-month audit (phase 1) was conducted to evaluate hyperglycaemia and dyslipidaemia management, and medication prescriptions. Consecutive people with diabetes admitted for acute coronary syndrome were prospectively identified. Target blood glucose level was defined as 5-10 mmol/l. A multidisciplinary committee designed and implemented decision-support algorithms plus education. A 3-month post-implementation audit (phase 2) was conducted.

RESULTS

There were 104 people in phase 1 and 101 in phase 2, with similar characteristics [HbA_1c 64 ± 20 mmol/mol vs. 61 ± 21 mmol/mol (8.0 ± 1.8% vs. 7.8 ± 1.9%]. Post implementation, the incidence of blood glucose levels > 10 mmol/l was lower [phase 1: 46.4% vs. phase 2: 31.8%, rate ratio (RR) = 0.77, 95% confidence intervals (CI) 0.60-0.98; P = 0.031], without a difference in blood glucose levels < 5mmol/l (phase 1: 4.9% vs. phase 2: 4.5%, RR = 1.20, 95% CI 0.70-2.08; P = 0.506). SGLT2 inhibitor prescriptions increased significantly (baseline to discharge: 12.5% to 15.4% vs. 7.9% to 24.8%; P = 0.007) but high-intensity statin prescriptions did not (baseline to discharge: 35.6% to 72.1% vs. 40.6% to 85.1%; P = 0.074). Prescription rates of non-statin lipid-lowering medications were not significantly increased.

CONCLUSIONS

Implementing decision-support algorithms was associated with improved inpatient glycaemic control and increased use of cardioprotective therapies at discharge in people with diabetes and acute coronary syndrome.

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.