Diabetes patient at risk score - a novel system for triaging appropriate referrals of inpatients with diabetes to the diabetes team

Examining the impact of Diabetes Inpatient Specialist Nursing in acute trusts in London

AIMS

To examine if the introduction of Diabetes Inpatient Specialist Nurses impacted on length of stay and rates of readmission.

DESIGN

Knowledge discovery through data mining as part of a larger realist evaluation of the role.

METHODS

Data from January 2017 to January 2019 was extracted and examined. A subset of performance data from July 2017-November 2018 was analysed. This consisted of 7320 records for Hospital Episode Statistics and 272 incident reports (Datix). The data were analysed via Generalised Linear Model regression routines in R. Analysis of readmission rates utilized binary logistic regression, while for the Length of Stay a count regression method was employed.

RESULTS

Four trusts were found to have complete and rich data sets. All Trusts that returned complete data were found to have varying decreased length of stay and reduced readmission rates. In two trusts there were significant decreases in patient readmissions and length of stay after the introduction of the Diabetes Inpatient Specialist Nurses. A marked decrease (approximately half) in patient length of stay was found in one London trust after the introduction of the post. Issues with data quality were noted.

CONCLUSION

Reduced patient length of stay and rate of readmission were found since introduction of Diabetes Specialist Nurses. Patient safety data was incomplete and varied significantly between trusts.

IMPACT

The project sought to understand the impact of employing Diabetes Inpatient Specialist Nurses in hospitals in London. Overall, the specialist nurses helped reduce length of stay and the rate of readmissions. The research will have an impact on the workforce in diabetes and also people with diabetes who need hospital care.

Suggested Canadian Standards for Perioperative/Periprocedure Glycemic Management in Patients With Type 1 and Type 2 Diabetes

OBJECTIVES

The goal of this quality initiative was to develop consensus standards for glycemic management of patients with diabetes who undergo surgical procedures in Canada.

METHODS

A modified Delphi method was used to gather broad stakeholder input and arrive at a consensus for perioperative/periprocedure diabetes management.

RESULTS

Glycemic management standards were developed for the following categories: Organization of Care; Preoperative Assessment; Immediate Preoperative and Intraoperative; Postanesthesia Care Unit or Recovery Room; Postoperative Period; and Transition to Outpatient Care.

CONCLUSIONS

It is anticipated these standards will serve as a basis to develop clinical tools to support the recommendations.

Improving Referrals to Diabetes Self-Management Education in Medically Underserved Adults

OBJECTIVE

Electronic health records (EHRs) and clinical decision-support algorithms improve diabetes care. This quality improvement (QI) project aimed to determine whether an electronic diabetes education referral protocol using the Diabetes Self-Management Education and Support for Adults With Type 2 Diabetes: Algorithm of Care (DSMES Algorithm) and protocol training would increase the proportion of adult patients with type 2 diabetes at a federally qualified health center electronically referred for diabetes self-management education and support (DSMES).

DESIGN AND METHODS

The EHR was modified to include the DSMES Algorithm and questions regarding prior participation in diabetes education. Protocol trainings were conducted. Data were obtained via retrospective chart review. A one-sample t test was used to evaluate the statistical difference between the electronic referral (e-referral) rates of the pre-intervention and intervention groups.

RESULTS

Completion of the DSMES Algorithm was positively associated with e-referrals to diabetes education (P <0.001). The intervention group had a higher rate of e-referral for DSMES than the pre-intervention group (31 vs. 0%, P <0.001).

CONCLUSION

E-referral protocols using the DSMES Algorithm and protocol training may aid in the identification and documentation of self-care needs of medically underserved patients with type 2 diabetes and improve e-referrals to DSMES. Of clinical importance, these findings translate into active patient engagement, team-based care, and information-sharing. Additional work is needed to determine whether the e-referral rate is sustained or increases over time. Further investigations should also be explored to evaluate the impact of e-referral protocols and algorithms on participation in DSMES.

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.

Impact of the Diabetes Inpatient Care and Education (DICE) project on length of stay and mortality

AIM

To determine whether the Diabetes Inpatient Care and Education (DICE) programme, a whole-systems approach to managing inpatient diabetes, reduces length of stay, in-hospital mortality and readmissions.

RESEARCH DESIGN AND METHODS

Diabetes Inpatient Care and Education initiatives included identification of all diabetes admissions, a novel DICE care-pathway, an online system for prioritizing referrals, use of web-linked glucose meters, an enhanced diabetes team, and novel diabetes training for doctors. Patient administration system data were extracted for people admitted to Ipswich Hospital from January 2008 to June 2016. Logistic regression was used to compare binary outcomes (mortality, 30-day readmissions) 6 months before and after the intervention; generalized estimating equations were used to compare lengths of stay. Interrupted time series analysis was performed over the full 7.5-year period to account for secular trends.

RESULTS

Before-and-after analysis revealed a significant reduction in lengths of stay for people with and without diabetes: relative ratios 0.89 (95% CI 0.83, 0.97) and 0.93 (95% CI 0.90, 0.96), respectively; however, in interrupted time series analysis the change in long-term trend for length of stay following the intervention was significant only for people with diabetes (P=0.017 vs P=0.48). Odds ratios for mortality were 0.63 (0.48, 0.82) and 0.81 (0.70, 0.93) in people with and without diabetes, respectively; however, the change in trend was not significant in people with diabetes, while there was an apparent increase in those without diabetes. There was no significant change in 30-day readmissions, but interrupted time series analysis showed a rising trend in both groups.

CONCLUSION

The DICE programme was associated with a shorter length of stay in inpatients with diabetes beyond that observed in people without diabetes.

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.

Common Models Used for Inpatient Diabetes Management

PURPOSE OF REVIEW

Diabetes affects about a third of all hospitalized patients and up to 50% of inpatients go on to experience hyperglycemia. Despite strong evidence supporting the importance of adequate glycemic control, as well detailed guidelines from major national organizations, many patients continue to have hypo- and hyperglycemia during their hospital stay. While this may be partially related to provider and patient-specific factors, system-based barriers continue to pose a major obstacle. Therefore, there is a need to go beyond merely discussing specific insulin protocols and provide guidance for effective models of care in the acute glycemic management of hospitalized patients.

RECENT FINDINGS

To date, there is limited data evaluating the various models of care for inpatient diabetes management in terms of efficacy or cost, and there is no summary on this topic guiding physicians and hospital administrators. In this paper, four common models of inpatient diabetes care will be presented including those models led by the following: an endocrinologist(s), mid-level provider(s), pharmacist(s), and a virtual glucose management team. The authors will outline the intrinsic benefits as well as limitations of each model of care as well as cite supporting evidence, when available. Discussion pertaining to how a given model of care shapes and formulates a particular organization's structured glucose management program (GMP) will be examined. Furthermore, the authors describe how the model of care chosen by an institution serves as the foundation for the creation of a GMP. Finally, the authors examine the critical factors needed for GMP success within an institution and outline the nature of hospital administrative support and accompanying reporting structure, the function of a multidisciplinary diabetes steering committee, and the role of the medical director.

Remote Monitoring and Consultation of Inpatient Populations with Diabetes

PURPOSE OF REVIEW

Inpatient hyperglycemia is common and is linked to increased morbidity and mortality. We review current and innovative ways diabetes specialists consult in the management of inpatient diabetes.

RECENT FINDINGS

With electronic medical records (EMRs), remote monitoring and intervention may improve the management of inpatient hyperglycemia. Automated reports allow monitoring of glucose levels and allow diabetes teams to intervene through formal or remote consultation. Following a 2-year transition of our complex paper-based insulin order sets to be EMR based, we leveraged this change by developing new daily glycemic reports and a virtual glucose management service (vGMS). Based on a daily report identifying patients with two or more glucoses over 225 mg/dl and/or a glucose <70 mg/dl in the past 24 h, a vGMS note with management recommendations was placed in the chart. Following the introduction of the vGMS, the proportion of hyperglycemic patients decreased 39% from a baseline of 6.5 per 100 patient-days to 4.0 per 100 patient-days The hypoglycemia proportion decreased by 36%. Ninety-nine percent of surveyed medical and surgical residents said the vGMS was both important and helpful.